{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T17:40:47Z","timestamp":1776102047174,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":174,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,30]],"date-time":"2023-10-30T00:00:00Z","timestamp":1698624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["NIBIB R01 EB017205"],"award-info":[{"award-number":["NIBIB R01 EB017205"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,30]]},"DOI":"10.1145\/3617694.3623224","type":"proceedings-article","created":{"date-parts":[[2023,10,29]],"date-time":"2023-10-29T15:52:11Z","timestamp":1698594731000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Taking Off with AI: Lessons from Aviation for Healthcare"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8459-8403","authenticated-orcid":false,"given":"Elizabeth","family":"Bondi-Kelly","sequence":"first","affiliation":[{"name":"MIT, USA and University of Michigan, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5288-2792","authenticated-orcid":false,"given":"Tom","family":"Hartvigsen","sequence":"additional","affiliation":[{"name":"MIT, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4247-3004","authenticated-orcid":false,"given":"Lindsay M","family":"Sanneman","sequence":"additional","affiliation":[{"name":"MIT, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9239-3221","authenticated-orcid":false,"given":"Swami","family":"Sankaranarayanan","sequence":"additional","affiliation":[{"name":"MIT, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2335-2792","authenticated-orcid":false,"given":"Zach","family":"Harned","sequence":"additional","affiliation":[{"name":"Stanford University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1561-8009","authenticated-orcid":false,"given":"Grace","family":"Wickerson","sequence":"additional","affiliation":[{"name":"Federation of American Scientists, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1097-316X","authenticated-orcid":false,"given":"Judy Wawira","family":"Gichoya","sequence":"additional","affiliation":[{"name":"Emory University, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5471-5202","authenticated-orcid":false,"given":"Lauren","family":"Oakden-Rayner","sequence":"additional","affiliation":[{"name":"University of Adelaide, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6712-6626","authenticated-orcid":false,"given":"Leo Anthony","family":"Celi","sequence":"additional","affiliation":[{"name":"Laboratory for Computational Physiology, MIT, USA and Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8591-5861","authenticated-orcid":false,"given":"Matthew P","family":"Lungren","sequence":"additional","affiliation":[{"name":"Stanford University, USA and Microsoft, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1338-8107","authenticated-orcid":false,"given":"Julie A","family":"Shah","sequence":"additional","affiliation":[{"name":"MIT, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6349-7251","authenticated-orcid":false,"given":"Marzyeh","family":"Ghassemi","sequence":"additional","affiliation":[{"name":"MIT, United States"}]}],"member":"320","published-online":{"date-parts":[[2023,10,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Silva\u00a0III.","author":"Abr\u00e0moff D","year":"2022","unstructured":"Michael\u00a0 D Abr\u00e0moff , Cybil Roehrenbeck , Sylvia Trujillo , Juli Goldstein , Anitra\u00a0 S Graves , Michael\u00a0 X Repka , and Ezequiel\u00a0 \u201c Zeke \u201d Silva\u00a0III. 2022 . A reimbursement framework for artificial intelligence in healthcare. NPJ digital medicine 5, 1 (2022), 72. Michael\u00a0D Abr\u00e0moff, Cybil Roehrenbeck, Sylvia Trujillo, Juli Goldstein, Anitra\u00a0S Graves, Michael\u00a0X Repka, and Ezequiel\u00a0\u201cZeke\u201d Silva\u00a0III. 2022. A reimbursement framework for artificial intelligence in healthcare. NPJ digital medicine 5, 1 (2022), 72."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s43856-022-00214-4"},{"key":"e_1_3_2_1_3_1","unstructured":"Katie Adams. 2023. Epic to Integrate GPT-4 into Its EHR Through Expanded Microsoft Partnership. https:\/\/medcitynews.com\/2023\/04\/epic-to-integrate-gpt-4-into-its-ehr-through-expanded-microsoft-partnership\/. Katie Adams. 2023. Epic to Integrate GPT-4 into Its EHR Through Expanded Microsoft Partnership. https:\/\/medcitynews.com\/2023\/04\/epic-to-integrate-gpt-4-into-its-ehr-through-expanded-microsoft-partnership\/."},{"key":"e_1_3_2_1_4_1","volume-title":"Machine learning and health care disparities in dermatology. JAMA dermatology 154, 11","author":"Adamson S","year":"2018","unstructured":"Adewole\u00a0 S Adamson and Avery Smith . 2018. Machine learning and health care disparities in dermatology. JAMA dermatology 154, 11 ( 2018 ), 1247\u20131248. Adewole\u00a0S Adamson and Avery Smith. 2018. Machine learning and health care disparities in dermatology. JAMA dermatology 154, 11 (2018), 1247\u20131248."},{"key":"e_1_3_2_1_5_1","unstructured":"Federal\u00a0Aviation Administration. 2000. Advisory Circular 23-18: Installation of Terrain Awareness and Warning System (TAWS) Approved for Part 23 Airplanes. Federal\u00a0Aviation Administration. 2000. Advisory Circular 23-18: Installation of Terrain Awareness and Warning System (TAWS) Approved for Part 23 Airplanes."},{"key":"e_1_3_2_1_6_1","unstructured":"Federal\u00a0Aviation Administration. 2011. Introduction to TCAS II version 7.1. Federal\u00a0Aviation Administration. 2011. Introduction to TCAS II version 7.1."},{"key":"e_1_3_2_1_7_1","unstructured":"Federal\u00a0Aviation Administration. 2018. Instrument Rating \u2012 Airplane Airman Certification Standards. https:\/\/www.faa.gov\/training_testing\/testing\/acs\/media\/instrument_rating_acs_change_1.pdf. Federal\u00a0Aviation Administration. 2018. Instrument Rating \u2012 Airplane Airman Certification Standards. https:\/\/www.faa.gov\/training_testing\/testing\/acs\/media\/instrument_rating_acs_change_1.pdf."},{"key":"e_1_3_2_1_8_1","unstructured":"Federal\u00a0Aviation Administration. 2020. Summary of the FAA\u2019s Review of the Boeing 737 MAX: Return to Service of the Boeing 737 MAX Aircraft. Federal\u00a0Aviation Administration. 2020. Summary of the FAA\u2019s Review of the Boeing 737 MAX: Return to Service of the Boeing 737 MAX Aircraft."},{"key":"e_1_3_2_1_9_1","unstructured":"Federal\u00a0Aviation Administration. 2022. Airworthiness Certification Overview. https:\/\/www.faa.gov\/aircraft\/air_cert\/airworthiness_certification\/aw_overview. Federal\u00a0Aviation Administration. 2022. Airworthiness Certification Overview. https:\/\/www.faa.gov\/aircraft\/air_cert\/airworthiness_certification\/aw_overview."},{"key":"e_1_3_2_1_10_1","unstructured":"Federal\u00a0Aviation Administration. n.d.. A Brief History of the FAA. https:\/\/www.faa.gov\/about\/history\/brief_history. Federal\u00a0Aviation Administration. n.d.. A Brief History of the FAA. https:\/\/www.faa.gov\/about\/history\/brief_history."},{"key":"e_1_3_2_1_11_1","unstructured":"AI Incident Database. 2023. Welcome to the AI Incident Database. https:\/\/incidentdatabase.ai\/. AI Incident Database. 2023. Welcome to the AI Incident Database. https:\/\/incidentdatabase.ai\/."},{"key":"e_1_3_2_1_12_1","unstructured":"AI Vulnerability Database. 2023. AI Vulnerability Database. https:\/\/avidml.org\/. AI Vulnerability Database. 2023. AI Vulnerability Database. https:\/\/avidml.org\/."},{"key":"e_1_3_2_1_13_1","unstructured":"AIAAIC. 2023. Understanding the risks harms and impacts of AI algorithms and automation. https:\/\/www.aiaaic.org\/. AIAAIC. 2023. Understanding the risks harms and impacts of AI algorithms and automation. https:\/\/www.aiaaic.org\/."},{"key":"e_1_3_2_1_14_1","volume-title":"Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323","author":"Alsentzer Emily","year":"2019","unstructured":"Emily Alsentzer , John\u00a0 R Murphy , Willie Boag , Wei-Hung Weng , Di Jin , Tristan Naumann , and Matthew McDermott . 2019. Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323 ( 2019 ). Emily Alsentzer, John\u00a0R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019. Publicly available clinical BERT embeddings. arXiv preprint arXiv:1904.03323 (2019)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1258\/jhsrp.2010.009113"},{"key":"e_1_3_2_1_16_1","volume-title":"Polling Spotlight: Understanding the Experiences of LGBTQ+ Birthing People. https:\/\/www.aamchealthjustice.org\/news\/polling\/lgbtq-birth.","author":"Alvarado S.","year":"2022","unstructured":"Carla\u00a0 S. Alvarado , Diane\u00a0 M. Cassidy , Kendal Orgera , and Sarah Piepenbrink . 2022 . Polling Spotlight: Understanding the Experiences of LGBTQ+ Birthing People. https:\/\/www.aamchealthjustice.org\/news\/polling\/lgbtq-birth. Carla\u00a0S. Alvarado, Diane\u00a0M. Cassidy, Kendal Orgera, and Sarah Piepenbrink. 2022. Polling Spotlight: Understanding the Experiences of LGBTQ+ Birthing People. https:\/\/www.aamchealthjustice.org\/news\/polling\/lgbtq-birth."},{"key":"e_1_3_2_1_17_1","unstructured":"Michael Atleson. 2023. Chatbots deepfakes and voice clones: AI deception for sale. https:\/\/www.ftc.gov\/business-guidance\/blog\/2023\/03\/chatbots-deepfakes-voice-clones-ai-deception-sale. Michael Atleson. 2023. Chatbots deepfakes and voice clones: AI deception for sale. https:\/\/www.ftc.gov\/business-guidance\/blog\/2023\/03\/chatbots-deepfakes-voice-clones-ai-deception-sale."},{"key":"e_1_3_2_1_18_1","unstructured":"Michael Atleson. 2023. Keep your AI claims in check. https:\/\/www.ftc.gov\/business-guidance\/blog\/2023\/02\/keep-your-ai-claims-check. Michael Atleson. 2023. Keep your AI claims in check. https:\/\/www.ftc.gov\/business-guidance\/blog\/2023\/02\/keep-your-ai-claims-check."},{"key":"e_1_3_2_1_19_1","unstructured":"Michael Atleson. 2023. The Luring Test: AI and the engineering of consumer trust. https:\/\/www.ftc.gov\/business-guidance\/blog\/2023\/05\/luring-test-ai-engineering-consumer-trust. Michael Atleson. 2023. The Luring Test: AI and the engineering of consumer trust. https:\/\/www.ftc.gov\/business-guidance\/blog\/2023\/05\/luring-test-ai-engineering-consumer-trust."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3531146.3533179"},{"key":"e_1_3_2_1_21_1","volume-title":"Big data\u2019s disparate impact. California law review 104, 3","author":"Barocas Solon","year":"2016","unstructured":"Solon Barocas and Andrew\u00a0 D Selbst . 2016. Big data\u2019s disparate impact. California law review 104, 3 ( 2016 ), 671\u2013732. Solon Barocas and Andrew\u00a0D Selbst. 2016. Big data\u2019s disparate impact. California law review 104, 3 (2016), 671\u2013732."},{"key":"e_1_3_2_1_22_1","volume-title":"Digital Avionics Handbook","author":"Bartley F","unstructured":"Gregg\u00a0 F Bartley . 2017. Boeing B-777: Fly-by-wire flight controls . In Digital Avionics Handbook . CRC Press , 482\u2013495. Gregg\u00a0F Bartley. 2017. Boeing B-777: Fly-by-wire flight controls. In Digital Avionics Handbook. CRC Press, 482\u2013495."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3313831.3376718"},{"key":"e_1_3_2_1_24_1","volume-title":"The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ digital medicine 3, 1","author":"Benjamens Stan","year":"2020","unstructured":"Stan Benjamens , Pranavsingh Dhunnoo , and Bertalan Mesk\u00f3 . 2020. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ digital medicine 3, 1 ( 2020 ), 118. Stan Benjamens, Pranavsingh Dhunnoo, and Bertalan Mesk\u00f3. 2020. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ digital medicine 3, 1 (2020), 118."},{"key":"e_1_3_2_1_25_1","unstructured":"National Transportation\u00a0Safety Board. n.d.. About the NTSB. https:\/\/www.ntsb.gov\/about\/Pages\/default.aspx. National Transportation\u00a0Safety Board. n.d.. About the NTSB. https:\/\/www.ntsb.gov\/about\/Pages\/default.aspx."},{"key":"e_1_3_2_1_26_1","unstructured":"National Transportation\u00a0Safety Board. n.d.. History of The National Transportation Safety Board. https:\/\/www.ntsb.gov\/about\/history. National Transportation\u00a0Safety Board. n.d.. History of The National Transportation Safety Board. https:\/\/www.ntsb.gov\/about\/history."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2017.2778743"},{"key":"e_1_3_2_1_28_1","volume-title":"HHAI2022: Augmenting Human Intellect","author":"Browne T","unstructured":"Jacob\u00a0 T Browne , Saskia Bakker , Bin Yu , Peter Lloyd , and Somaya Ben\u00a0Allouch . 2022. Trust in Clinical AI: Expanding the Unit of Analysis . In HHAI2022: Augmenting Human Intellect . IOS Press , 96\u2013113. Jacob\u00a0T Browne, Saskia Bakker, Bin Yu, Peter Lloyd, and Somaya Ben\u00a0Allouch. 2022. Trust in Clinical AI: Expanding the Unit of Analysis. In HHAI2022: Augmenting Human Intellect. IOS Press, 96\u2013113."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3449287"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290605.3300234"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359206"},{"key":"e_1_3_2_1_32_1","volume-title":"AB-35 Civil damages: medical malpractice.https:\/\/leginfo.legislature.ca.gov\/faces\/billTextClient.xhtml?bill_id=202120220AB35","author":"Information Bill Text California Legislative","unstructured":"California Legislative Information Bill Text . 2022. AB-35 Civil damages: medical malpractice.https:\/\/leginfo.legislature.ca.gov\/faces\/billTextClient.xhtml?bill_id=202120220AB35 . California Legislative Information Bill Text. 2022. AB-35 Civil damages: medical malpractice.https:\/\/leginfo.legislature.ca.gov\/faces\/billTextClient.xhtml?bill_id=202120220AB35."},{"key":"e_1_3_2_1_33_1","unstructured":"Susan Carey. 2015. FAA Hits Southwest Airlines With Proposed $325 000 Fine. https:\/\/www.wsj.com\/articles\/faa-hits-southwest-airlines-with-proposed-325-000-fine-1439844153. Date Accessed: 2023-03-13. Susan Carey. 2015. FAA Hits Southwest Airlines With Proposed $325 000 Fine. https:\/\/www.wsj.com\/articles\/faa-hits-southwest-airlines-with-proposed-325-000-fine-1439844153. Date Accessed: 2023-03-13."},{"key":"e_1_3_2_1_34_1","unstructured":"Centers for Medicare & Medicaid Services. 2022. Nondiscrimination in Health Programs and Activities. https:\/\/www.federalregister.gov\/documents\/2022\/08\/04\/2022-16217\/nondiscrimination-in-health-programs-and-activities. Centers for Medicare & Medicaid Services. 2022. Nondiscrimination in Health Programs and Activities. https:\/\/www.federalregister.gov\/documents\/2022\/08\/04\/2022-16217\/nondiscrimination-in-health-programs-and-activities."},{"key":"e_1_3_2_1_35_1","unstructured":"Centers for Medicare and Medicaid. 2021. Medicare Coverage of Innovative Technologies (MCIT). https:\/\/www.cms.gov\/blog\/medicare-coverage-innovative-technologies-mcit. Centers for Medicare and Medicaid. 2021. Medicare Coverage of Innovative Technologies (MCIT). https:\/\/www.cms.gov\/blog\/medicare-coverage-innovative-technologies-mcit."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1142\/9789811232701_0006"},{"key":"e_1_3_2_1_37_1","volume-title":"Ethical machine learning in healthcare. Annual review of biomedical data science 4","author":"Chen Y","year":"2021","unstructured":"Irene\u00a0 Y Chen , Emma Pierson , Sherri Rose , Shalmali Joshi , Kadija Ferryman , and Marzyeh Ghassemi . 2021. Ethical machine learning in healthcare. Annual review of biomedical data science 4 ( 2021 ), 123\u2013144. Irene\u00a0Y Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi. 2021. Ethical machine learning in healthcare. Annual review of biomedical data science 4 (2021), 123\u2013144."},{"key":"e_1_3_2_1_38_1","volume-title":"Exploring the Effects of Machine Learning Literacy Interventions on Laypeople\u2019s Reliance on Machine Learning Models. In 27th International Conference on Intelligent User Interfaces. 148\u2013161","author":"Chiang Chun-Wei","year":"2022","unstructured":"Chun-Wei Chiang and Ming Yin . 2022 . Exploring the Effects of Machine Learning Literacy Interventions on Laypeople\u2019s Reliance on Machine Learning Models. In 27th International Conference on Intelligent User Interfaces. 148\u2013161 . Chun-Wei Chiang and Ming Yin. 2022. Exploring the Effects of Machine Learning Literacy Interventions on Laypeople\u2019s Reliance on Machine Learning Models. In 27th International Conference on Intelligent User Interfaces. 148\u2013161."},{"key":"e_1_3_2_1_39_1","unstructured":"Rohit Chopra Kristen Clarke Charlotte\u00a0A. Burrows and Lina\u00a0M. Khan. 2023. Joint Statement on Enforcement Efforts Against Discrimination and Bias in Automated Systems. https:\/\/files.consumerfinance.gov\/f\/documents\/cfpb_joint-statement-enforcement-against-discrimination-bias-automated-systems_2023-04.pdf. Rohit Chopra Kristen Clarke Charlotte\u00a0A. Burrows and Lina\u00a0M. Khan. 2023. Joint Statement on Enforcement Efforts Against Discrimination and Bias in Automated Systems. https:\/\/files.consumerfinance.gov\/f\/documents\/cfpb_joint-statement-enforcement-against-discrimination-bias-automated-systems_2023-04.pdf."},{"key":"e_1_3_2_1_40_1","unstructured":"Christine Chung. 2023. Mother Accused of Human Trafficking While Flying With Daughter Sues Southwest Airlines. https:\/\/www.nytimes.com\/2023\/08\/07\/travel\/southwest-airlines-lawsuit-racial-profiling-maccarthy.html. Christine Chung. 2023. Mother Accused of Human Trafficking While Flying With Daughter Sues Southwest Airlines. https:\/\/www.nytimes.com\/2023\/08\/07\/travel\/southwest-airlines-lawsuit-racial-profiling-maccarthy.html."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1111\/jocn.13846"},{"key":"e_1_3_2_1_42_1","volume-title":"Decision making in aviation","author":"Cummings L","unstructured":"Mary\u00a0 L Cummings . 2017. Automation bias in intelligent time critical decision support systems . In Decision making in aviation . Routledge , 289\u2013294. Mary\u00a0L Cummings. 2017. Automation bias in intelligent time critical decision support systems. In Decision making in aviation. Routledge, 289\u2013294."},{"key":"e_1_3_2_1_43_1","volume-title":"Disparities in dermatology AI performance on a diverse, curated clinical image set. Science advances 8, 31","author":"Daneshjou Roxana","year":"2022","unstructured":"Roxana Daneshjou , Kailas Vodrahalli , Roberto\u00a0 A Novoa , Melissa Jenkins , Weixin Liang , Veronica Rotemberg , Justin Ko , Susan\u00a0 M Swetter , Elizabeth\u00a0 E Bailey , Olivier Gevaert , 2022. Disparities in dermatology AI performance on a diverse, curated clinical image set. Science advances 8, 31 ( 2022 ), eabq6147. Roxana Daneshjou, Kailas Vodrahalli, Roberto\u00a0A Novoa, Melissa Jenkins, Weixin Liang, Veronica Rotemberg, Justin Ko, Susan\u00a0M Swetter, Elizabeth\u00a0E Bailey, Olivier Gevaert, 2022. Disparities in dermatology AI performance on a diverse, curated clinical image set. Science advances 8, 31 (2022), eabq6147."},{"key":"e_1_3_2_1_44_1","volume-title":"Human Factors in Aviation and Aerospace","author":"Dattel R","unstructured":"Andrew\u00a0 R Dattel , Andrey\u00a0 K Babin , and Hui Wang . 2023. Human factors of flight training and simulation . In Human Factors in Aviation and Aerospace . Elsevier , 217\u2013255. Andrew\u00a0R Dattel, Andrey\u00a0K Babin, and Hui Wang. 2023. Human factors of flight training and simulation. In Human Factors in Aviation and Aerospace. Elsevier, 217\u2013255."},{"key":"e_1_3_2_1_45_1","unstructured":"Department of Health and Human Services. 2021. 42 CFR Parts 403 405 410 411 414 415 423 424 and 425. https:\/\/public-inspection.federalregister.gov\/2021-14973.pdf. Department of Health and Human Services. 2021. 42 CFR Parts 403 405 410 411 414 415 423 424 and 425. https:\/\/public-inspection.federalregister.gov\/2021-14973.pdf."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v38i3.2756"},{"key":"e_1_3_2_1_47_1","volume-title":"NAACP Issues Travel Advisory For American Airlines","author":"Domonoske Camila","year":"2017","unstructured":"Camila Domonoske . 2017. NAACP Issues Travel Advisory For American Airlines ; Company Agrees To Meeting . https:\/\/www.npr.org\/sections\/thetwo-way\/ 2017 \/10\/25\/560047880\/naacp-issues-travel-advisory-for-american-airlines-company-agrees-to-meeting. Camila Domonoske. 2017. NAACP Issues Travel Advisory For American Airlines; Company Agrees To Meeting. https:\/\/www.npr.org\/sections\/thetwo-way\/2017\/10\/25\/560047880\/naacp-issues-travel-advisory-for-american-airlines-company-agrees-to-meeting."},{"key":"e_1_3_2_1_48_1","unstructured":"Keith\u00a0J Dreyer. 2021. Why Does AI Medical Device Transparency Matter?https:\/\/www.acr.org\/Advocacy-and-Economics\/Voice-of-Radiology-Blog\/2021\/10\/21\/Why-Does-AI-Medical-Device-Transparency-Matter. Keith\u00a0J Dreyer. 2021. Why Does AI Medical Device Transparency Matter?https:\/\/www.acr.org\/Advocacy-and-Economics\/Voice-of-Radiology-Blog\/2021\/10\/21\/Why-Does-AI-Medical-Device-Transparency-Matter."},{"key":"e_1_3_2_1_49_1","volume-title":"The application of human factors to the development of expert systems for advanced cockpits. 31, 12","author":"Endsley R","year":"1987","unstructured":"Mica\u00a0 R Endsley . 1987. The application of human factors to the development of expert systems for advanced cockpits. 31, 12 ( 1987 ), 1388\u20131392. Mica\u00a0R Endsley. 1987. The application of human factors to the development of expert systems for advanced cockpits. 31, 12 (1987), 1388\u20131392."},{"key":"e_1_3_2_1_50_1","volume-title":"Situational awareness","author":"Endsley R","unstructured":"Mica\u00a0 R Endsley . 2017. Toward a theory of situation awareness in dynamic systems . In Situational awareness . Routledge , 9\u201342. Mica\u00a0R Endsley. 2017. Toward a theory of situation awareness in dynamic systems. In Situational awareness. Routledge, 9\u201342."},{"key":"e_1_3_2_1_51_1","unstructured":"Lesley Fair. 2022. What\u2019s new \u2013 and what isn\u2019t \u2013 in the FTC\u2019s just-published Health Products Compliance Guidance. https:\/\/www.ftc.gov\/business-guidance\/blog\/2022\/12\/whats-new-what-isnt-ftcs-just-published-health-products-compliance-guidance. Lesley Fair. 2022. What\u2019s new \u2013 and what isn\u2019t \u2013 in the FTC\u2019s just-published Health Products Compliance Guidance. https:\/\/www.ftc.gov\/business-guidance\/blog\/2022\/12\/whats-new-what-isnt-ftcs-just-published-health-products-compliance-guidance."},{"key":"e_1_3_2_1_52_1","unstructured":"Blake Farmer. 2022. Fights over the role of state medical boards. https:\/\/www.npr.org\/2022\/02\/16\/1081247597\/fights-over-the-role-of-state-medical-boards. Blake Farmer. 2022. Fights over the role of state medical boards. https:\/\/www.npr.org\/2022\/02\/16\/1081247597\/fights-over-the-role-of-state-medical-boards."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207179408923072"},{"key":"e_1_3_2_1_54_1","first-page":"N93119","article-title":"Lessons Learned","volume":"800","author":"Federal Aviation Administration","year":"1996","unstructured":"Federal Aviation Administration . 1996 . Lessons Learned : Trans World Airways Flight 800 , N93119 . https:\/\/lessonslearned.faa.gov\/ll_main.cfm?TabID=1&LLID=21. Federal Aviation Administration. 1996. Lessons Learned: Trans World Airways Flight 800, N93119. https:\/\/lessonslearned.faa.gov\/ll_main.cfm?TabID=1&LLID=21.","journal-title":"Trans World Airways Flight"},{"key":"e_1_3_2_1_55_1","volume-title":"Proposed Rules: Terrain Awareness and Warning System. Federal Register 63, 165","author":"Federal Aviation Administration","year":"1998","unstructured":"Federal Aviation Administration . 1998 . Proposed Rules: Terrain Awareness and Warning System. Federal Register 63, 165 (1998). Federal Aviation Administration. 1998. Proposed Rules: Terrain Awareness and Warning System. Federal Register 63, 165 (1998)."},{"key":"e_1_3_2_1_56_1","unstructured":"Federal Aviation Administration. 2017. Instrument Procedure Handbook. https:\/\/www.faa.gov\/regulations_policies\/handbooks_manuals\/aviation\/instrument_procedures_handbook\/media\/FAA-H-8083-16B.pdf. Federal Aviation Administration. 2017. Instrument Procedure Handbook. https:\/\/www.faa.gov\/regulations_policies\/handbooks_manuals\/aviation\/instrument_procedures_handbook\/media\/FAA-H-8083-16B.pdf."},{"key":"e_1_3_2_1_57_1","unstructured":"Federal Aviation Administration. 2019. Advisory Circular 121-24D: Passenger Safety Information Briefing and Briefing Cards. Federal Aviation Administration. 2019. Advisory Circular 121-24D: Passenger Safety Information Briefing and Briefing Cards."},{"key":"e_1_3_2_1_58_1","unstructured":"Federal Aviation Administration. 2020. Form 5100-140 - Performance Report. https:\/\/www.faa.gov\/forms\/index.cfm\/go\/document.information\/documentID\/1027511. Federal Aviation Administration. 2020. Form 5100-140 - Performance Report. https:\/\/www.faa.gov\/forms\/index.cfm\/go\/document.information\/documentID\/1027511."},{"key":"e_1_3_2_1_59_1","unstructured":"Federal Aviation Administration. 2022. Certification. https:\/\/www.faa.gov\/uas\/advanced_operations\/certification. Federal Aviation Administration. 2022. Certification. https:\/\/www.faa.gov\/uas\/advanced_operations\/certification."},{"key":"e_1_3_2_1_60_1","unstructured":"Federal Trade Commission. 2021. Health Breach Notification Rule. https:\/\/www.ftc.gov\/legal-library\/browse\/rules\/health-breach-notification-rule. Federal Trade Commission. 2021. Health Breach Notification Rule. https:\/\/www.ftc.gov\/legal-library\/browse\/rules\/health-breach-notification-rule."},{"key":"e_1_3_2_1_61_1","unstructured":"Federal Trade Commission. 2022. Mobile Health App Interactive Tool. https:\/\/www.ftc.gov\/business-guidance\/resources\/mobile-health-apps-interactive-tool. Federal Trade Commission. 2022. Mobile Health App Interactive Tool. https:\/\/www.ftc.gov\/business-guidance\/resources\/mobile-health-apps-interactive-tool."},{"key":"e_1_3_2_1_62_1","unstructured":"Federal Trade Commission. 2023. FTC Enforcement Action to Bar GoodRx from Sharing Consumers\u2019 Sensitive Health Info for Advertising. https:\/\/www.ftc.gov\/news-events\/news\/press-releases\/2023\/02\/ftc-enforcement-action-bar-goodrx-sharing-consumers-sensitive-health-info-advertising. Federal Trade Commission. 2023. FTC Enforcement Action to Bar GoodRx from Sharing Consumers\u2019 Sensitive Health Info for Advertising. https:\/\/www.ftc.gov\/news-events\/news\/press-releases\/2023\/02\/ftc-enforcement-action-bar-goodrx-sharing-consumers-sensitive-health-info-advertising."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40257-020-00574-4"},{"key":"e_1_3_2_1_64_1","unstructured":"Lee Fleisher Steve Farmer Lori Ashby and Jonathan Blum. 2023. Transforming Medicare Coverage: A New Medicare Coverage Pathway for Emerging Technologies and Revamped Evidence Development Framework. https:\/\/www.cms.gov\/blog\/transforming-medicare-coverage-new-medicare-coverage-pathway-emerging-technologies-and-revamped. Lee Fleisher Steve Farmer Lori Ashby and Jonathan Blum. 2023. Transforming Medicare Coverage: A New Medicare Coverage Pathway for Emerging Technologies and Revamped Evidence Development Framework. https:\/\/www.cms.gov\/blog\/transforming-medicare-coverage-new-medicare-coverage-pathway-emerging-technologies-and-revamped."},{"key":"e_1_3_2_1_65_1","volume-title":"Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ digital medicine 4, 1","author":"Gaube Susanne","year":"2021","unstructured":"Susanne Gaube , Harini Suresh , Martina Raue , Alexander Merritt , Seth\u00a0 J Berkowitz , Eva Lermer , Joseph\u00a0 F Coughlin , John\u00a0 V Guttag , Errol Colak , and Marzyeh Ghassemi . 2021. Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ digital medicine 4, 1 ( 2021 ), 1\u20138. Susanne Gaube, Harini Suresh, Martina Raue, Alexander Merritt, Seth\u00a0J Berkowitz, Eva Lermer, Joseph\u00a0F Coughlin, John\u00a0V Guttag, Errol Colak, and Marzyeh Ghassemi. 2021. Do as AI say: susceptibility in deployment of clinical decision-aids. NPJ digital medicine 4, 1 (2021), 1\u20138."},{"key":"e_1_3_2_1_66_1","volume-title":"Machine learning and health need better values. npj Digital Medicine 5, 1","author":"Ghassemi Marzyeh","year":"2022","unstructured":"Marzyeh Ghassemi and Shakir Mohamed . 2022. Machine learning and health need better values. npj Digital Medicine 5, 1 ( 2022 ), 51. Marzyeh Ghassemi and Shakir Mohamed. 2022. Machine learning and health need better values. npj Digital Medicine 5, 1 (2022), 51."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"crossref","unstructured":"Marzyeh Ghassemi and Elaine\u00a0Okanyene Nsoesie. 2022. In medicine how do we machine learn anything real?Patterns 3 1 (2022) 100392. Marzyeh Ghassemi and Elaine\u00a0Okanyene Nsoesie. 2022. In medicine how do we machine learn anything real?Patterns 3 1 (2022) 100392.","DOI":"10.1016\/j.patter.2021.100392"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(21)00208-9"},{"key":"e_1_3_2_1_69_1","first-page":"e132","article-title":"Antibiotic prescribing by age, sex, race, and ethnicity for patients admitted to the hospital with community-acquired bacterial pneumonia (CABP) in the All of Us database","volume":"7","author":"Gilmore M","year":"2023","unstructured":"Corbyn\u00a0 M Gilmore , Grace\u00a0 C Lee , Susanne Schmidt , and Christopher\u00a0 R Frei . 2023 . Antibiotic prescribing by age, sex, race, and ethnicity for patients admitted to the hospital with community-acquired bacterial pneumonia (CABP) in the All of Us database . Journal of Clinical and Translational Science 7 , 1 (2023), e132 . Corbyn\u00a0M Gilmore, Grace\u00a0C Lee, Susanne Schmidt, and Christopher\u00a0R Frei. 2023. Antibiotic prescribing by age, sex, race, and ethnicity for patients admitted to the hospital with community-acquired bacterial pneumonia (CABP) in the All of Us database. Journal of Clinical and Translational Science 7, 1 (2023), e132.","journal-title":"Journal of Clinical and Translational Science"},{"key":"e_1_3_2_1_70_1","unstructured":"Ira Glass Ben Calhoun and Dana Chivvis. 2020. Trust Me I\u2019m a Doctor. https:\/\/www.thisamericanlife.org\/719\/trust-me-im-a-doctor. Ira Glass Ben Calhoun and Dana Chivvis. 2020. Trust Me I\u2019m a Doctor. https:\/\/www.thisamericanlife.org\/719\/trust-me-im-a-doctor."},{"key":"e_1_3_2_1_71_1","unstructured":"Elaine Glusac. 2016. Vigilance Gone Awry: When Math Gets Mistaken for Terrorism. https:\/\/www.nytimes.com\/2016\/05\/22\/travel\/american-airlines-passenger-terrorism.html. Elaine Glusac. 2016. Vigilance Gone Awry: When Math Gets Mistaken for Terrorism. https:\/\/www.nytimes.com\/2016\/05\/22\/travel\/american-airlines-passenger-terrorism.html."},{"key":"e_1_3_2_1_72_1","unstructured":"John Goglia. 2015. Operator Of Flights For Major Airlines Faces $1.23M FAA Fine For Safety Violations. https:\/\/www.forbes.com\/sites\/johngoglia\/2015\/07\/14\/skywest-faa-fine\/?sh=2edd28428ee1. Date Accessed: 2023-03-13. John Goglia. 2015. Operator Of Flights For Major Airlines Faces $1.23M FAA Fine For Safety Violations. https:\/\/www.forbes.com\/sites\/johngoglia\/2015\/07\/14\/skywest-faa-fine\/?sh=2edd28428ee1. Date Accessed: 2023-03-13."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1800097115"},{"key":"e_1_3_2_1_74_1","volume-title":"Greve and Vibha Gaba","author":"R.","year":"2019","unstructured":"Henrich\u00a0 R. Greve and Vibha Gaba . 2019 . Research : Why Struggling Airlines Spend More on Safety. Harvard Business Review ( 2019). https:\/\/hbr.org\/2019\/03\/research-why-struggling-airlines-spend-more-on-safety Henrich\u00a0R. Greve and Vibha Gaba. 2019. Research: Why Struggling Airlines Spend More on Safety. Harvard Business Review (2019). https:\/\/hbr.org\/2019\/03\/research-why-struggling-airlines-spend-more-on-safety"},{"key":"e_1_3_2_1_75_1","volume-title":"Crew resource management training in healthcare: a systematic review of intervention design, training conditions and evaluation. BMJ open 9, 2","author":"Gross Benedict","year":"2019","unstructured":"Benedict Gross , Leonie Rusin , Jan Kiesewetter , Jan\u00a0 M Zottmann , Martin\u00a0 R Fischer , Stephan Pr\u00fcckner , and Alexandra Zech . 2019. Crew resource management training in healthcare: a systematic review of intervention design, training conditions and evaluation. BMJ open 9, 2 ( 2019 ), e025247. Benedict Gross, Leonie Rusin, Jan Kiesewetter, Jan\u00a0M Zottmann, Martin\u00a0R Fischer, Stephan Pr\u00fcckner, and Alexandra Zech. 2019. Crew resource management training in healthcare: a systematic review of intervention design, training conditions and evaluation. BMJ open 9, 2 (2019), e025247."},{"key":"e_1_3_2_1_76_1","unstructured":"Susan Hofer and Mica\u00a0Nguyen Worthy. 2020. What Information From An NTSB Report is Admissible Evidence in Court?https:\/\/www.jdsupra.com\/legalnews\/what-information-from-an-ntsb-report-is-31087\/. Susan Hofer and Mica\u00a0Nguyen Worthy. 2020. What Information From An NTSB Report is Admissible Evidence in Court?https:\/\/www.jdsupra.com\/legalnews\/what-information-from-an-ntsb-report-is-31087\/."},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2022.24683"},{"key":"e_1_3_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2021.100241"},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462527"},{"key":"e_1_3_2_1_80_1","volume-title":"AI is no better at detecting covid-19 than simple symptom survey. NewScientist","author":"Hsu Jeremy","year":"2023","unstructured":"Jeremy Hsu . 2023. AI is no better at detecting covid-19 than simple symptom survey. NewScientist ( 2023 ). Jeremy Hsu. 2023. AI is no better at detecting covid-19 than simple symptom survey. NewScientist (2023)."},{"key":"e_1_3_2_1_81_1","unstructured":"Ryan Huerto and E Lindo. 2020. Minority patients benefit from having minority doctors but that\u2019sa hard match to make. https:\/\/theconversation.com\/minority-patients-benefit-from-having-minority-doctors-but-thats-a-hard-match-to-make-130504. Ryan Huerto and E Lindo. 2020. Minority patients benefit from having minority doctors but that\u2019sa hard match to make. https:\/\/theconversation.com\/minority-patients-benefit-from-having-minority-doctors-but-thats-a-hard-match-to-make-130504."},{"key":"e_1_3_2_1_82_1","volume-title":"How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection. Translational psychiatry 11, 1","author":"Jacobs Maia","year":"2021","unstructured":"Maia Jacobs , Melanie\u00a0 F Pradier , Thomas\u00a0 H McCoy , Roy\u00a0 H Perlis , Finale Doshi-Velez , and Krzysztof\u00a0 Z Gajos . 2021. How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection. Translational psychiatry 11, 1 ( 2021 ), 1\u20139. Maia Jacobs, Melanie\u00a0F Pradier, Thomas\u00a0H McCoy, Roy\u00a0H Perlis, Finale Doshi-Velez, and Krzysztof\u00a0Z Gajos. 2021. How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection. Translational psychiatry 11, 1 (2021), 1\u20139."},{"key":"e_1_3_2_1_83_1","volume-title":"Large-Scale Study of Temporal Shift in Health Insurance Claims. In Conference on Health, Inference, and Learning. PMLR, 243\u2013278","author":"Ji X","year":"2023","unstructured":"Christina\u00a0 X Ji , Ahmed\u00a0 M Alaa , and David Sontag . 2023 . Large-Scale Study of Temporal Shift in Health Insurance Claims. In Conference on Health, Inference, and Learning. PMLR, 243\u2013278 . Christina\u00a0X Ji, Ahmed\u00a0M Alaa, and David Sontag. 2023. Large-Scale Study of Temporal Shift in Health Insurance Claims. In Conference on Health, Inference, and Learning. PMLR, 243\u2013278."},{"key":"e_1_3_2_1_84_1","unstructured":"Elisa Jillson. 2021. Aiming for truth fairness and equity in your company\u2019s use of AI. https:\/\/www.ftc.gov\/business-guidance\/blog\/2021\/04\/aiming-truth-fairness-equity-your-companys-use-ai. Elisa Jillson. 2021. Aiming for truth fairness and equity in your company\u2019s use of AI. https:\/\/www.ftc.gov\/business-guidance\/blog\/2021\/04\/aiming-truth-fairness-equity-your-companys-use-ai."},{"key":"e_1_3_2_1_85_1","doi-asserted-by":"publisher","DOI":"10.1145\/3490238"},{"key":"e_1_3_2_1_86_1","unstructured":"Justia. 1996. Medtronic Inc. v. Lohr 518 U.S. 470 (1996). https:\/\/supreme.justia.com\/cases\/federal\/us\/518\/470\/. Justia. 1996. Medtronic Inc. v. Lohr 518 U.S. 470 (1996). https:\/\/supreme.justia.com\/cases\/federal\/us\/518\/470\/."},{"key":"e_1_3_2_1_87_1","volume-title":"Riegel v. Medtronic","year":"2008","unstructured":"Justia. 2008. Riegel v. Medtronic , Inc., 552 U.S. 312 ( 2008 ). https:\/\/supreme.justia.com\/cases\/federal\/us\/552\/312\/. Justia. 2008. Riegel v. Medtronic, Inc., 552 U.S. 312 (2008). https:\/\/supreme.justia.com\/cases\/federal\/us\/552\/312\/."},{"key":"e_1_3_2_1_88_1","volume-title":"Aviation and healthcare: a comparative review with implications for patient safety. JRSM open 7, 1","author":"Kapur Narinder","year":"2015","unstructured":"Narinder Kapur , Anam Parand , Tayana Soukup , Tom Reader , and Nick Sevdalis . 2015. Aviation and healthcare: a comparative review with implications for patient safety. JRSM open 7, 1 ( 2015 ), 2054270415616548. Narinder Kapur, Anam Parand, Tayana Soukup, Tom Reader, and Nick Sevdalis. 2015. Aviation and healthcare: a comparative review with implications for patient safety. JRSM open 7, 1 (2015), 2054270415616548."},{"key":"e_1_3_2_1_89_1","unstructured":"Kate Kaye. 2022. The FTC\u2019s new enforcement weapon spells death for algorithms. https:\/\/www.protocol.com\/policy\/ftc-algorithm-destroy-data-privacy. Kate Kaye. 2022. The FTC\u2019s new enforcement weapon spells death for algorithms. https:\/\/www.protocol.com\/policy\/ftc-algorithm-destroy-data-privacy."},{"key":"e_1_3_2_1_90_1","doi-asserted-by":"crossref","unstructured":"Michael Knop Sebastian Weber Marius Mueller and Bjoern Niehaves. 2022. Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence\u2013Enabled Clinical Decision Support Systems: Literature Review. JMIR Human Factors 9 (2022). Michael Knop Sebastian Weber Marius Mueller and Bjoern Niehaves. 2022. Human Factors and Technological Characteristics Influencing the Interaction of Medical Professionals With Artificial Intelligence\u2013Enabled Clinical Decision Support Systems: Literature Review. JMIR Human Factors 9 (2022).","DOI":"10.2196\/28639"},{"key":"e_1_3_2_1_91_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1525-1497.2006.00523.x"},{"key":"e_1_3_2_1_92_1","unstructured":"Daniela\u00a0J. Lamas. 2023. There\u2019s One Hard Question My Fellow Doctors and I Will Need to Answer Soon. https:\/\/www.nytimes.com\/2023\/07\/06\/opinion\/artificial-intelligence-medicine-healthcare.html Daniela\u00a0J. Lamas. 2023. There\u2019s One Hard Question My Fellow Doctors and I Will Need to Answer Soon. https:\/\/www.nytimes.com\/2023\/07\/06\/opinion\/artificial-intelligence-medicine-healthcare.html"},{"key":"e_1_3_2_1_93_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz682"},{"key":"e_1_3_2_1_94_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(22)00070-X"},{"key":"e_1_3_2_1_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/3411764.3445522"},{"key":"e_1_3_2_1_96_1","first-page":"2347","article-title":"The effects of aviation accidents on public perception toward an airline","volume":"11","author":"Li Chen-Wei","year":"2015","unstructured":"Chen-Wei Li , Veng\u00a0Kheang Phun , Mio Suzuki , and Tetsuo Yai . 2015 . The effects of aviation accidents on public perception toward an airline . Journal of the Eastern Asia Society for Transportation Studies 11 (2015), 2347 \u2013 2362 . Chen-Wei Li, Veng\u00a0Kheang Phun, Mio Suzuki, and Tetsuo Yai. 2015. The effects of aviation accidents on public perception toward an airline. Journal of the Eastern Asia Society for Transportation Studies 11 (2015), 2347\u20132362.","journal-title":"Journal of the Eastern Asia Society for Transportation Studies"},{"key":"e_1_3_2_1_97_1","volume-title":"Pengshan Cai","author":"Li Fei","year":"2019","unstructured":"Fei Li , Yonghao Jin , Weisong Liu , Bhanu Pratap\u00a0Singh Rawat , Pengshan Cai , Hong Yu, 2019 . Fine-tuning bidirectional encoder representations from transformers (BERT)\u2013based models on large-scale electronic health record notes: an empirical study. JMIR medical informatics 7, 3 (2019), e14830. Fei Li, Yonghao Jin, Weisong Liu, Bhanu Pratap\u00a0Singh Rawat, Pengshan Cai, Hong Yu, 2019. Fine-tuning bidirectional encoder representations from transformers (BERT)\u2013based models on large-scale electronic health record notes: an empirical study. JMIR medical informatics 7, 3 (2019), e14830."},{"key":"e_1_3_2_1_98_1","first-page":"315","article-title":"Error in medicine: the role of the morbidity and mortality conference","volume":"7","author":"Liu Vincent","year":"2005","unstructured":"Vincent Liu . 2005 . Error in medicine: the role of the morbidity and mortality conference . AMA Journal of Ethics 7 , 4 (2005), 315 \u2013 319 . Vincent Liu. 2005. Error in medicine: the role of the morbidity and mortality conference. AMA Journal of Ethics 7, 4 (2005), 315\u2013319.","journal-title":"AMA Journal of Ethics"},{"key":"e_1_3_2_1_99_1","volume-title":"Reproducibility in machine learning for health research: Still a ways to go. Science Translational Medicine 13, 586","author":"McDermott BA","year":"2021","unstructured":"Matthew\u00a0 BA McDermott , Shirly Wang , Nikki Marinsek , Rajesh Ranganath , Luca Foschini , and Marzyeh Ghassemi . 2021. Reproducibility in machine learning for health research: Still a ways to go. Science Translational Medicine 13, 586 ( 2021 ), eabb1655. Matthew\u00a0BA McDermott, Shirly Wang, Nikki Marinsek, Rajesh Ranganath, Luca Foschini, and Marzyeh Ghassemi. 2021. Reproducibility in machine learning for health research: Still a ways to go. Science Translational Medicine 13, 586 (2021), eabb1655."},{"key":"e_1_3_2_1_100_1","volume-title":"Automation bias: Decision making and performance in high-tech cockpits. The International journal of aviation psychology 8, 1","author":"Mosier L","year":"1998","unstructured":"Kathleen\u00a0 L Mosier , Linda\u00a0 J Skitka , Susan Heers , and Mark Burdick . 1998. Automation bias: Decision making and performance in high-tech cockpits. The International journal of aviation psychology 8, 1 ( 1998 ), 47\u201363. Kathleen\u00a0L Mosier, Linda\u00a0J Skitka, Susan Heers, and Mark Burdick. 1998. Automation bias: Decision making and performance in high-tech cockpits. The International journal of aviation psychology 8, 1 (1998), 47\u201363."},{"key":"e_1_3_2_1_101_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i5.20469"},{"key":"e_1_3_2_1_102_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.resuscitation.2006.08.011"},{"key":"e_1_3_2_1_103_1","unstructured":"National Aeronautics and Space Administration. 2023. Aviation Safety Reporting System. https:\/\/asrs.arc.nasa.gov\/. National Aeronautics and Space Administration. 2023. Aviation Safety Reporting System. https:\/\/asrs.arc.nasa.gov\/."},{"key":"e_1_3_2_1_104_1","unstructured":"National Transportation Safety Board. 1979. Aircraft Accident Report. https:\/\/www.ntsb.gov\/investigations\/AccidentReports\/Reports\/AAR7917.pdf. National Transportation Safety Board. 1979. Aircraft Accident Report. https:\/\/www.ntsb.gov\/investigations\/AccidentReports\/Reports\/AAR7917.pdf."},{"key":"e_1_3_2_1_105_1","unstructured":"National Transportation Safety Board. 2022. 49 CFR \u00a7 830.2 - Definitions.https:\/\/www.law.cornell.edu\/cfr\/text\/49\/830.2. National Transportation Safety Board. 2022. 49 CFR \u00a7 830.2 - Definitions.https:\/\/www.law.cornell.edu\/cfr\/text\/49\/830.2."},{"key":"e_1_3_2_1_106_1","unstructured":"National Transportation Safety Board. 2023. NTSB Go Team. https:\/\/www.ntsb.gov\/investigations\/process\/Pages\/goteam.aspx. National Transportation Safety Board. 2023. NTSB Go Team. https:\/\/www.ntsb.gov\/investigations\/process\/Pages\/goteam.aspx."},{"key":"e_1_3_2_1_107_1","doi-asserted-by":"crossref","unstructured":"NIST. 2023. Artificial Intelligence Risk Management Framework (AI RMF 1.0). https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.100-1.PDF. NIST. 2023. Artificial Intelligence Risk Management Framework (AI RMF 1.0). https:\/\/nvlpubs.nist.gov\/nistpubs\/ai\/NIST.AI.100-1.PDF.","DOI":"10.6028\/NIST.AI.100-1.jpn"},{"key":"e_1_3_2_1_108_1","unstructured":"National Transportation Safety\u00a0Board (NTSB). 2000. NTSB Identification: NYC99MA178. https:\/\/www.ntsb.gov\/about\/employment\/_layouts\/15\/ntsb.aviation\/brief2.aspx?akey=1&ev_id=20001212X19354&ntsbno=NYC99MA178. National Transportation Safety\u00a0Board (NTSB). 2000. NTSB Identification: NYC99MA178. https:\/\/www.ntsb.gov\/about\/employment\/_layouts\/15\/ntsb.aviation\/brief2.aspx?akey=1&ev_id=20001212X19354&ntsbno=NYC99MA178."},{"key":"e_1_3_2_1_109_1","volume-title":"Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464","author":"Obermeyer Ziad","year":"2019","unstructured":"Ziad Obermeyer , Brian Powers , Christine Vogeli , and Sendhil Mullainathan . 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 ( 2019 ), 447\u2013453. Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. 2019. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 6464 (2019), 447\u2013453."},{"key":"e_1_3_2_1_110_1","volume-title":"Optimizing human-centered AI for healthcare in the Global South. Patterns","author":"Okolo T","year":"2022","unstructured":"Chinasa\u00a0 T Okolo . 2022. Optimizing human-centered AI for healthcare in the Global South. Patterns ( 2022 ), 100421. Chinasa\u00a0T Okolo. 2022. Optimizing human-centered AI for healthcare in the Global South. Patterns (2022), 100421."},{"key":"e_1_3_2_1_111_1","doi-asserted-by":"publisher","DOI":"10.1097\/ACM.0000000000003901"},{"key":"e_1_3_2_1_112_1","volume-title":"Humans and automation: Use, misuse, disuse, abuse. Human factors 39, 2","author":"Parasuraman Raja","year":"1997","unstructured":"Raja Parasuraman and Victor Riley . 1997. Humans and automation: Use, misuse, disuse, abuse. Human factors 39, 2 ( 1997 ), 230\u2013253. Raja Parasuraman and Victor Riley. 1997. Humans and automation: Use, misuse, disuse, abuse. Human factors 39, 2 (1997), 230\u2013253."},{"key":"e_1_3_2_1_113_1","doi-asserted-by":"publisher","DOI":"10.1518\/155534308X284417"},{"key":"e_1_3_2_1_114_1","volume-title":"Twenty years of evidence on the outcomes of malpractice claims. Clinical orthopaedics and related research 467","author":"Peters G","year":"2009","unstructured":"Philip\u00a0 G Peters . 2009. Twenty years of evidence on the outcomes of malpractice claims. Clinical orthopaedics and related research 467 ( 2009 ), 352\u2013357. Philip\u00a0G Peters. 2009. Twenty years of evidence on the outcomes of malpractice claims. Clinical orthopaedics and related research 467 (2009), 352\u2013357."},{"key":"e_1_3_2_1_115_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-01192-7"},{"key":"e_1_3_2_1_116_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2019.15064"},{"key":"e_1_3_2_1_117_1","volume-title":"How much can potential jurors tell us about liability for medical artificial intelligence?Journal of Nuclear Medicine 62, 1","author":"Price W\u00a0Nicholson","year":"2021","unstructured":"W\u00a0Nicholson Price , Sara Gerke , and I\u00a0Glenn Cohen . 2021. How much can potential jurors tell us about liability for medical artificial intelligence?Journal of Nuclear Medicine 62, 1 ( 2021 ), 15\u201316. https:\/\/jnm.snmjournals.org\/content\/jnumed\/62\/1\/15.full.pdf. W\u00a0Nicholson Price, Sara Gerke, and I\u00a0Glenn Cohen. 2021. How much can potential jurors tell us about liability for medical artificial intelligence?Journal of Nuclear Medicine 62, 1 (2021), 15\u201316. https:\/\/jnm.snmjournals.org\/content\/jnumed\/62\/1\/15.full.pdf."},{"key":"e_1_3_2_1_118_1","doi-asserted-by":"publisher","DOI":"10.1145\/3512930"},{"key":"e_1_3_2_1_119_1","doi-asserted-by":"publisher","DOI":"10.1109\/HRI.2016.7451740"},{"key":"e_1_3_2_1_120_1","volume-title":"IBM pitched its Watson supercomputer as a revolution in cancer care. It\u2019s nowhere close. Stat","author":"Ross Casey","year":"2017","unstructured":"Casey Ross and Ike Swetlitz . 2017. IBM pitched its Watson supercomputer as a revolution in cancer care. It\u2019s nowhere close. Stat ( 2017 ). Casey Ross and Ike Swetlitz. 2017. IBM pitched its Watson supercomputer as a revolution in cancer care. It\u2019s nowhere close. Stat (2017)."},{"key":"e_1_3_2_1_121_1","volume-title":"IBM\u2019s Watson supercomputer recommended \u2018unsafe and incorrect","author":"Ross Casey","year":"2018","unstructured":"Casey Ross and Ike Swetlitz . 2018. IBM\u2019s Watson supercomputer recommended \u2018unsafe and incorrect \u2019 cancer treatments, internal documents show. Stat 25 ( 2018 ). Casey Ross and Ike Swetlitz. 2018. IBM\u2019s Watson supercomputer recommended \u2018unsafe and incorrect\u2019 cancer treatments, internal documents show. Stat 25 (2018)."},{"key":"e_1_3_2_1_122_1","unstructured":"Royal Australian and New Zealand College of Radiologists. 2020. Standards of Practice for Artificial Intelligence. https:\/\/www.ranzcr.com\/college\/document-library\/standards-of-practice-for-artificial-intelligence. Royal Australian and New Zealand College of Radiologists. 2020. Standards of Practice for Artificial Intelligence. https:\/\/www.ranzcr.com\/college\/document-library\/standards-of-practice-for-artificial-intelligence."},{"key":"e_1_3_2_1_123_1","volume-title":"Effects of the Premier Hospital Quality Incentive Demonstration on Medicare patient mortality and cost. Health services research 44, 3","author":"Ryan M","year":"2009","unstructured":"Andrew\u00a0 M Ryan . 2009. Effects of the Premier Hospital Quality Incentive Demonstration on Medicare patient mortality and cost. Health services research 44, 3 ( 2009 ), 821\u2013842. Andrew\u00a0M Ryan. 2009. Effects of the Premier Hospital Quality Incentive Demonstration on Medicare patient mortality and cost. Health services research 44, 3 (2009), 821\u2013842."},{"key":"e_1_3_2_1_124_1","volume-title":"2015 10th ACM\/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 1\u20138.","author":"Salem Maha","year":"2015","unstructured":"Maha Salem , Gabriella Lakatos , Farshid Amirabdollahian , and Kerstin Dautenhahn . 2015 . Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust . In 2015 10th ACM\/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 1\u20138. Maha Salem, Gabriella Lakatos, Farshid Amirabdollahian, and Kerstin Dautenhahn. 2015. Would you trust a (faulty) robot? Effects of error, task type and personality on human-robot cooperation and trust. In 2015 10th ACM\/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 1\u20138."},{"key":"e_1_3_2_1_125_1","doi-asserted-by":"publisher","DOI":"10.1080\/10447318.2022.2081282"},{"key":"e_1_3_2_1_126_1","doi-asserted-by":"publisher","DOI":"10.1097\/ALN.0b013e318280a40f"},{"key":"e_1_3_2_1_127_1","volume-title":"Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature medicine 27, 12","author":"Seyyed-Kalantari Laleh","year":"2021","unstructured":"Laleh Seyyed-Kalantari , Haoran Zhang , Matthew\u00a0 BA McDermott , Irene\u00a0 Y Chen , and Marzyeh Ghassemi . 2021. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature medicine 27, 12 ( 2021 ), 2176\u20132182. Laleh Seyyed-Kalantari, Haoran Zhang, Matthew\u00a0BA McDermott, Irene\u00a0Y Chen, and Marzyeh Ghassemi. 2021. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature medicine 27, 12 (2021), 2176\u20132182."},{"key":"e_1_3_2_1_128_1","unstructured":"Jamila Sherif. 2021. The impact of unconscious bias on Muslim womens\u2019 experiences of healthcare. https:\/\/bjgplife.com\/the-impact-of-unconscious-bias-on-muslim-womens-experiences-of-healthcare\/. Jamila Sherif. 2021. The impact of unconscious bias on Muslim womens\u2019 experiences of healthcare. https:\/\/bjgplife.com\/the-impact-of-unconscious-bias-on-muslim-womens-experiences-of-healthcare\/."},{"key":"e_1_3_2_1_129_1","volume-title":"BioMegatron: Larger biomedical domain language model. arXiv preprint arXiv:2010.06060","author":"Shin Hoo-Chang","year":"2020","unstructured":"Hoo-Chang Shin , Yang Zhang , Evelina Bakhturina , Raul Puri , Mostofa Patwary , Mohammad Shoeybi , and Raghav Mani . 2020. BioMegatron: Larger biomedical domain language model. arXiv preprint arXiv:2010.06060 ( 2020 ). Hoo-Chang Shin, Yang Zhang, Evelina Bakhturina, Raul Puri, Mostofa Patwary, Mohammad Shoeybi, and Raghav Mani. 2020. BioMegatron: Larger biomedical domain language model. arXiv preprint arXiv:2010.06060 (2020)."},{"key":"e_1_3_2_1_130_1","volume-title":"Maternal Deaths in the US Spiked","author":"Simmons-Duffin Selena","year":"2021","unstructured":"Selena Simmons-Duffin and Carmel Wroth . 2023. Maternal Deaths in the US Spiked in 2021 , CDC Reports . https:\/\/www.npr.org\/sections\/health-shots\/2023\/03\/16\/1163786037\/maternal-deaths-in-the-u-s-spiked-in-2021-cdc-reports. Selena Simmons-Duffin and Carmel Wroth. 2023. Maternal Deaths in the US Spiked in 2021, CDC Reports. https:\/\/www.npr.org\/sections\/health-shots\/2023\/03\/16\/1163786037\/maternal-deaths-in-the-u-s-spiked-in-2021-cdc-reports."},{"key":"e_1_3_2_1_131_1","volume-title":"Large Language Models Encode Clinical Knowledge. arXiv preprint arXiv:2212.13138","author":"Singhal Karan","year":"2022","unstructured":"Karan Singhal , Shekoofeh Azizi , Tao Tu , S\u00a0Sara Mahdavi , Jason Wei , Hyung\u00a0Won Chung , Nathan Scales , Ajay Tanwani , Heather Cole-Lewis , Stephen Pfohl , 2022. Large Language Models Encode Clinical Knowledge. arXiv preprint arXiv:2212.13138 ( 2022 ). Karan Singhal, Shekoofeh Azizi, Tao Tu, S\u00a0Sara Mahdavi, Jason Wei, Hyung\u00a0Won Chung, Nathan Scales, Ajay Tanwani, Heather Cole-Lewis, Stephen Pfohl, 2022. Large Language Models Encode Clinical Knowledge. arXiv preprint arXiv:2212.13138 (2022)."},{"key":"e_1_3_2_1_132_1","unstructured":"SKYbrary Aviation Safety. 2023. Crew Resource Management (CRM). https:\/\/skybrary.aero\/articles\/crew-resource-management-crm. SKYbrary Aviation Safety. 2023. Crew Resource Management (CRM). https:\/\/skybrary.aero\/articles\/crew-resource-management-crm."},{"key":"e_1_3_2_1_133_1","unstructured":"Lauren Smiley. 2023. The Legal Saga of Uber\u2019s Fatal Self-Driving Car Crash Is Over. https:\/\/www.wired.com\/story\/ubers-fatal-self-driving-car-crash-saga-over-operator-avoids-prison\/. Lauren Smiley. 2023. The Legal Saga of Uber\u2019s Fatal Self-Driving Car Crash Is Over. https:\/\/www.wired.com\/story\/ubers-fatal-self-driving-car-crash-saga-over-operator-avoids-prison\/."},{"key":"e_1_3_2_1_134_1","doi-asserted-by":"publisher","DOI":"10.1038\/d41586-023-00023-2"},{"key":"e_1_3_2_1_135_1","unstructured":"Allianz Global Corporate\u00a0& Specialty. 2014. Global Aviation Safety Study: A review of 60 years of improvement in aviation safety. https:\/\/www.agcs.allianz.com\/content\/dam\/onemarketing\/agcs\/agcs\/reports\/AGCS-Global-Aviation-Safety-2014-report.pdf. Allianz Global Corporate\u00a0& Specialty. 2014. Global Aviation Safety Study: A review of 60 years of improvement in aviation safety. https:\/\/www.agcs.allianz.com\/content\/dam\/onemarketing\/agcs\/agcs\/reports\/AGCS-Global-Aviation-Safety-2014-report.pdf."},{"key":"e_1_3_2_1_136_1","unstructured":"Liam Stack. 2016. College Student Is Removed From Flight After Speaking Arabic on Plane. https:\/\/www.nytimes.com\/2016\/04\/17\/us\/student-speaking-arabic-removed-southwest-airlines-plane.html. Liam Stack. 2016. College Student Is Removed From Flight After Speaking Arabic on Plane. https:\/\/www.nytimes.com\/2016\/04\/17\/us\/student-speaking-arabic-removed-southwest-airlines-plane.html."},{"key":"e_1_3_2_1_137_1","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Stadler Theresa","year":"2022","unstructured":"Theresa Stadler , Bristena Oprisanu , and Carmela Troncoso . 2022 . Synthetic data\u2013anonymisation groundhog day . In 31st USENIX Security Symposium (USENIX Security 22) . 1451\u20131468. Theresa Stadler, Bristena Oprisanu, and Carmela Troncoso. 2022. Synthetic data\u2013anonymisation groundhog day. In 31st USENIX Security Symposium (USENIX Security 22). 1451\u20131468."},{"key":"e_1_3_2_1_138_1","volume-title":"Human factors challenges for the safe use of artificial intelligence in patient care. BMJ health & care informatics 26, 1","author":"Sujan Mark","year":"2019","unstructured":"Mark Sujan , Dominic Furniss , Kath Grundy , Howard Grundy , David Nelson , Matthew Elliott , Sean White , Ibrahim Habli , and Nick Reynolds . 2019. Human factors challenges for the safe use of artificial intelligence in patient care. BMJ health & care informatics 26, 1 ( 2019 ). Mark Sujan, Dominic Furniss, Kath Grundy, Howard Grundy, David Nelson, Matthew Elliott, Sean White, Ibrahim Habli, and Nick Reynolds. 2019. Human factors challenges for the safe use of artificial intelligence in patient care. BMJ health & care informatics 26, 1 (2019)."},{"key":"e_1_3_2_1_139_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394231.3397922"},{"key":"e_1_3_2_1_140_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Suriyakumar M","year":"2023","unstructured":"Vinith\u00a0 M Suriyakumar , Marzyeh Ghassemi , and Berk Ustun . 2023 . When personalization harms: Reconsidering the use of group attributes in prediction . In International Conference on Machine Learning (ICML). Vinith\u00a0M Suriyakumar, Marzyeh Ghassemi, and Berk Ustun. 2023. When personalization harms: Reconsidering the use of group attributes in prediction. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_141_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445934"},{"key":"e_1_3_2_1_142_1","volume-title":"Developing a general-purpose clinical language inference model from a large corpus of clinical notes. arXiv preprint arXiv:2210.06566","author":"Sushil Madhumita","year":"2022","unstructured":"Madhumita Sushil , Dana Ludwig , Atul\u00a0 J Butte , and Vivek\u00a0 A Rudrapatna . 2022. Developing a general-purpose clinical language inference model from a large corpus of clinical notes. arXiv preprint arXiv:2210.06566 ( 2022 ). Madhumita Sushil, Dana Ludwig, Atul\u00a0J Butte, and Vivek\u00a0A Rudrapatna. 2022. Developing a general-purpose clinical language inference model from a large corpus of clinical notes. arXiv preprint arXiv:2210.06566 (2022)."},{"key":"e_1_3_2_1_144_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00881-0"},{"key":"e_1_3_2_1_145_1","unstructured":"The New York City Council. 2021. Automated employment decision tools. https:\/\/legistar.council.nyc.gov\/LegislationDetail.aspx?ID=4344524&GUID=B051915D-A9AC-451E-81F8-6596032FA3F9&Options=Advanced&Search. The New York City Council. 2021. Automated employment decision tools. https:\/\/legistar.council.nyc.gov\/LegislationDetail.aspx?ID=4344524&GUID=B051915D-A9AC-451E-81F8-6596032FA3F9&Options=Advanced&Search."},{"key":"e_1_3_2_1_146_1","unstructured":"The White House. 2023. Blueprint for an AI Bill of Rights. https:\/\/www.whitehouse.gov\/ostp\/ai-bill-of-rights\/. The White House. 2023. Blueprint for an AI Bill of Rights. https:\/\/www.whitehouse.gov\/ostp\/ai-bill-of-rights\/."},{"key":"e_1_3_2_1_147_1","unstructured":"Angela\u00a0D Thomas and Allan Fong. 2023. The Role of NLP in Addressing Maternal Harm through a Patient Safety and Equity Lens. https:\/\/www.aamchealthjustice.org\/our-work\/maternal-health-equity\/maternal-health-workshop\/recording Angela\u00a0D Thomas and Allan Fong. 2023. The Role of NLP in Addressing Maternal Harm through a Patient Safety and Equity Lens. https:\/\/www.aamchealthjustice.org\/our-work\/maternal-health-equity\/maternal-health-workshop\/recording"},{"key":"e_1_3_2_1_148_1","volume-title":"When does physician use of AI increase liability?Journal of Nuclear Medicine 62, 1","author":"Tobia Kevin","year":"2021","unstructured":"Kevin Tobia , Aileen Nielsen , and Alexander Stremitzer . 2021. When does physician use of AI increase liability?Journal of Nuclear Medicine 62, 1 ( 2021 ), 17\u201321. Kevin Tobia, Aileen Nielsen, and Alexander Stremitzer. 2021. When does physician use of AI increase liability?Journal of Nuclear Medicine 62, 1 (2021), 17\u201321."},{"key":"e_1_3_2_1_149_1","volume-title":"High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 25, 1","author":"Topol J","year":"2019","unstructured":"Eric\u00a0 J Topol . 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 25, 1 ( 2019 ), 44\u201356. Eric\u00a0J Topol. 2019. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine 25, 1 (2019), 44\u201356."},{"key":"e_1_3_2_1_150_1","volume-title":"Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nature Communications 14, 2102","author":"Tsai Pei-Chen","year":"2023","unstructured":"Pei-Chen Tsai , Tsung-Hua Lee , Kun-Chi Kuo , Fang-Yi Su , Tsung- Lu\u00a0Michael Lee , Eliana Marostica , Tomotaka Ugai , Melissa Zhao , Mai\u00a0Chan Lau , Juha\u00a0 P. V\u00e4yrynen , Marios Giannakis , Yasutoshi Takashima , Seyed\u00a0Mousavi Kahaki , Kana Wu , Mingyang Song , Jeffrey\u00a0 A. Meyerhardt , Andrew\u00a0 T. Chan , Jung-Hsien Chiang , Jonathan Nowak , Shuji Ogino , and Kun-Hsing Yu. 2023. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nature Communications 14, 2102 ( 2023 ). Pei-Chen Tsai, Tsung-Hua Lee, Kun-Chi Kuo, Fang-Yi Su, Tsung-Lu\u00a0Michael Lee, Eliana Marostica, Tomotaka Ugai, Melissa Zhao, Mai\u00a0Chan Lau, Juha\u00a0P. V\u00e4yrynen, Marios Giannakis, Yasutoshi Takashima, Seyed\u00a0Mousavi Kahaki, Kana Wu, Mingyang Song, Jeffrey\u00a0A. Meyerhardt, Andrew\u00a0T. Chan, Jung-Hsien Chiang, Jonathan Nowak, Shuji Ogino, and Kun-Hsing Yu. 2023. Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nature Communications 14, 2102 (2023)."},{"key":"e_1_3_2_1_151_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0942-0"},{"key":"e_1_3_2_1_152_1","volume-title":"Bureau of Transportation Statistics","author":"United States Department of Transportation","year":"2021","unstructured":"United States Department of Transportation : Bureau of Transportation Statistics . 2021 . U.S. General Aviation Safety Data . https:\/\/www.bts.gov\/content\/us-general-aviationa-safety-data. United States Department of Transportation: Bureau of Transportation Statistics. 2021. U.S. General Aviation Safety Data. https:\/\/www.bts.gov\/content\/us-general-aviationa-safety-data."},{"key":"e_1_3_2_1_153_1","unstructured":"U.S. Department of Health & Human Services. 2022. HHS Announces Proposed Rule to Strengthen Nondiscrimination in Health Care. https:\/\/www.hhs.gov\/about\/news\/2022\/07\/25\/hhs-announces-proposed-rule-to-strengthen-nondiscrimination-in-health-care.html. U.S. Department of Health & Human Services. 2022. HHS Announces Proposed Rule to Strengthen Nondiscrimination in Health Care. https:\/\/www.hhs.gov\/about\/news\/2022\/07\/25\/hhs-announces-proposed-rule-to-strengthen-nondiscrimination-in-health-care.html."},{"key":"e_1_3_2_1_154_1","unstructured":"U.S. Food and Drug Administration. 2018. What We Do. https:\/\/www.fda.gov\/about-fda\/what-we-do. U.S. Food and Drug Administration. 2018. What We Do. https:\/\/www.fda.gov\/about-fda\/what-we-do."},{"key":"e_1_3_2_1_155_1","unstructured":"U.S. Food and Drug Administration. 2019. Adverse Event Reporting Data Files. https:\/\/www.fda.gov\/medical-devices\/medical-device-reporting-mdr-how-report-medical-device-problems\/adverse-event-reporting-data-files. U.S. Food and Drug Administration. 2019. Adverse Event Reporting Data Files. https:\/\/www.fda.gov\/medical-devices\/medical-device-reporting-mdr-how-report-medical-device-problems\/adverse-event-reporting-data-files."},{"key":"e_1_3_2_1_156_1","unstructured":"U.S. Food and Drug Administration. 2019. Proposed Regulatory Framework for Modifications to Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback. https:\/\/www.fda.gov\/media\/122535\/download. U.S. Food and Drug Administration. 2019. Proposed Regulatory Framework for Modifications to Artificial Intelligence\/Machine Learning (AI\/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback. https:\/\/www.fda.gov\/media\/122535\/download."},{"key":"e_1_3_2_1_157_1","unstructured":"U.S. Food and Drug Administration. 2020. Classify Your Medical Device. https:\/\/www.fda.gov\/medical-devices\/overview-device-regulation\/classify-your-medical-device. U.S. Food and Drug Administration. 2020. Classify Your Medical Device. https:\/\/www.fda.gov\/medical-devices\/overview-device-regulation\/classify-your-medical-device."},{"key":"e_1_3_2_1_158_1","unstructured":"U.S. Food and Drug Administration. 2021. FDA Adverse Event Reporting System (FAERS) Public Dashboard. https:\/\/www.fda.gov\/drugs\/questions-and-answers-fdas-adverse-event-reporting-system-faers\/fda-adverse-event-reporting-system-faers-public-dashboard. U.S. Food and Drug Administration. 2021. FDA Adverse Event Reporting System (FAERS) Public Dashboard. https:\/\/www.fda.gov\/drugs\/questions-and-answers-fdas-adverse-event-reporting-system-faers\/fda-adverse-event-reporting-system-faers-public-dashboard."},{"key":"e_1_3_2_1_159_1","unstructured":"U.S. Food and Drug Administration. 2021. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/good-machine-learning-practice-medical-device-development-guiding-principles. U.S. Food and Drug Administration. 2021. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/good-machine-learning-practice-medical-device-development-guiding-principles."},{"key":"e_1_3_2_1_160_1","unstructured":"U.S. Food and Drug Administration. 2021. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/good-machine-learning-practice-medical-device-development-guiding-principles. U.S. Food and Drug Administration. 2021. Good Machine Learning Practice for Medical Device Development: Guiding Principles. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/good-machine-learning-practice-medical-device-development-guiding-principles."},{"key":"e_1_3_2_1_161_1","unstructured":"U.S. Food and Drug Administration. 2022. Artificial Intelligence and Machine Learning (AI\/ML)-Enabled Medical Devices. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices. U.S. Food and Drug Administration. 2022. Artificial Intelligence and Machine Learning (AI\/ML)-Enabled Medical Devices. https:\/\/www.fda.gov\/medical-devices\/software-medical-device-samd\/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices."},{"key":"e_1_3_2_1_162_1","unstructured":"U.S. Food and Drug Administration. 2022. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. https:\/\/www.fda.gov\/media\/109618\/download. U.S. Food and Drug Administration. 2022. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. https:\/\/www.fda.gov\/media\/109618\/download."},{"key":"e_1_3_2_1_163_1","unstructured":"U.S. Food and Drug Administration. 2022. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/clinical-decision-support-software. U.S. Food and Drug Administration. 2022. Clinical Decision Support Software: Guidance for Industry and Food and Drug Administration Staff. https:\/\/www.fda.gov\/regulatory-information\/search-fda-guidance-documents\/clinical-decision-support-software."},{"key":"e_1_3_2_1_164_1","unstructured":"U.S. Food and Drug Administration. 2022. Digital Health Software Precertification (Pre-Cert) Pilot Program. https:\/\/www.fda.gov\/medical-devices\/digital-health-center-excellence\/digital-health-software-precertification-pre-cert-pilot-program. U.S. Food and Drug Administration. 2022. Digital Health Software Precertification (Pre-Cert) Pilot Program. https:\/\/www.fda.gov\/medical-devices\/digital-health-center-excellence\/digital-health-software-precertification-pre-cert-pilot-program."},{"key":"e_1_3_2_1_165_1","unstructured":"U.S. Food and Drug Administration. 2023. Breakthrough Devices Program. https:\/\/www.fda.gov\/medical-devices\/how-study-and-market-your-device\/breakthrough-devices-program. U.S. Food and Drug Administration. 2023. Breakthrough Devices Program. https:\/\/www.fda.gov\/medical-devices\/how-study-and-market-your-device\/breakthrough-devices-program."},{"key":"e_1_3_2_1_166_1","unstructured":"U.S. Food and Drug Administration. 2023. CDRH Issues Draft Guidance on Predetermined Change Control Plans for Artificial Intelligence\/Machine Learning-Enabled Medical Devices. https:\/\/www.fda.gov\/medical-devices\/medical-devices-news-and-events\/cdrh-issues-draft-guidance-predetermined-change-control-plans-artificial-intelligencemachine. U.S. Food and Drug Administration. 2023. CDRH Issues Draft Guidance on Predetermined Change Control Plans for Artificial Intelligence\/Machine Learning-Enabled Medical Devices. https:\/\/www.fda.gov\/medical-devices\/medical-devices-news-and-events\/cdrh-issues-draft-guidance-predetermined-change-control-plans-artificial-intelligencemachine."},{"key":"e_1_3_2_1_167_1","unstructured":"U.S. Food and Drug Administration. 2023. CFR - Code of Federal Regulations Title 21 Part 803. https:\/\/www.accessdata.fda.gov\/scripts\/cdrh\/cfdocs\/cfcfr\/CFRSearch.cfm?CFRPart=803. U.S. Food and Drug Administration. 2023. CFR - Code of Federal Regulations Title 21 Part 803. https:\/\/www.accessdata.fda.gov\/scripts\/cdrh\/cfdocs\/cfcfr\/CFRSearch.cfm?CFRPart=803."},{"key":"e_1_3_2_1_168_1","unstructured":"U.S. Food and Drug Administration. 2023. CFR - Code of Federal Regulations Title 21 Part 808 Section 1 (d) (10). https:\/\/www.accessdata.fda.gov\/scripts\/cdrh\/cfdocs\/cfcfr\/CFRSearch.cfm?fr=808.1. U.S. Food and Drug Administration. 2023. CFR - Code of Federal Regulations Title 21 Part 808 Section 1 (d) (10). https:\/\/www.accessdata.fda.gov\/scripts\/cdrh\/cfdocs\/cfcfr\/CFRSearch.cfm?fr=808.1."},{"key":"e_1_3_2_1_169_1","unstructured":"U.S. Food and Drug Administration. 2023. MedWatch Online Voluntary Reporting Form. https:\/\/www.accessdata.fda.gov\/scripts\/medwatch\/. U.S. Food and Drug Administration. 2023. MedWatch Online Voluntary Reporting Form. https:\/\/www.accessdata.fda.gov\/scripts\/medwatch\/."},{"key":"e_1_3_2_1_170_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMms2004740"},{"key":"e_1_3_2_1_171_1","unstructured":"Bob Wachter. 2015. How technology led a hospital to give a patient 38 times his dosage. https:\/\/www.wired.com\/2015\/03\/how-technology-led-a-hospital-to-give-a-patient-38-times-his-dosage\/. Bob Wachter. 2015. How technology led a hospital to give a patient 38 times his dosage. https:\/\/www.wired.com\/2015\/03\/how-technology-led-a-hospital-to-give-a-patient-38-times-his-dosage\/."},{"key":"e_1_3_2_1_172_1","unstructured":"Wei Wang Shu Cole and Charles Chancellor. 2016. Media\u2019s Impact on People\u2019s Anxiety Levels toward Air Travel. (2016). Wei Wang Shu Cole and Charles Chancellor. 2016. Media\u2019s Impact on People\u2019s Anxiety Levels toward Air Travel. (2016)."},{"key":"e_1_3_2_1_173_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2021.2626"},{"key":"e_1_3_2_1_174_1","volume-title":"Toward robust mammography-based models for breast cancer risk. Science Translational Medicine 13, 578","author":"Yala Adam","year":"2021","unstructured":"Adam Yala , Peter\u00a0 G Mikhael , Fredrik Strand , Gigin Lin , Kevin Smith , Yung-Liang Wan , Leslie Lamb , Kevin Hughes , Constance Lehman , and Regina Barzilay . 2021. Toward robust mammography-based models for breast cancer risk. Science Translational Medicine 13, 578 ( 2021 ), eaba4373. Adam Yala, Peter\u00a0G Mikhael, Fredrik Strand, Gigin Lin, Kevin Smith, Yung-Liang Wan, Leslie Lamb, Kevin Hughes, Constance Lehman, and Regina Barzilay. 2021. Toward robust mammography-based models for breast cancer risk. Science Translational Medicine 13, 578 (2021), eaba4373."},{"key":"e_1_3_2_1_175_1","volume-title":"A large language model for electronic health records. npj Digital Medicine 5, 1","author":"Yang Xi","year":"2022","unstructured":"Xi Yang , Aokun Chen , Nima PourNejatian , Hoo\u00a0Chang Shin , Kaleb\u00a0 E Smith , Christopher Parisien , Colin Compas , Cheryl Martin , Anthony\u00a0 B Costa , Mona\u00a0 G Flores , 2022. A large language model for electronic health records. npj Digital Medicine 5, 1 ( 2022 ), 1\u20139. Xi Yang, Aokun Chen, Nima PourNejatian, Hoo\u00a0Chang Shin, Kaleb\u00a0E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Anthony\u00a0B Costa, Mona\u00a0G Flores, 2022. A large language model for electronic health records. npj Digital Medicine 5, 1 (2022), 1\u20139."}],"event":{"name":"EAAMO '23: Equity and Access in Algorithms, Mechanisms, and Optimization","location":"Boston MA USA","acronym":"EAAMO '23","sponsor":["SIGAI ACM Special Interest Group on Artificial Intelligence","SIGecom Special Interest Group on Economics and Computation"]},"container-title":["Equity and Access in Algorithms, Mechanisms, and Optimization"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617694.3623224","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3617694.3623224","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:32Z","timestamp":1750178192000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3617694.3623224"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,30]]},"references-count":174,"alternative-id":["10.1145\/3617694.3623224","10.1145\/3617694"],"URL":"https:\/\/doi.org\/10.1145\/3617694.3623224","relation":{},"subject":[],"published":{"date-parts":[[2023,10,30]]},"assertion":[{"value":"2023-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}