{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:47:47Z","timestamp":1776084467474,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032006554","type":"print"},{"value":"9783032006561","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-00656-1_8","type":"book-chapter","created":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T03:32:51Z","timestamp":1755660771000},"page":"100-114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["From Clinic to\u00a0Code: Using Clinician Insights to\u00a0Develop a\u00a0Framework for\u00a0Fair and\u00a0Representative Datasets in\u00a0Women\u2019s Health AI"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2447-724X","authenticated-orcid":false,"given":"Andrea","family":"Heaney","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6738-3067","authenticated-orcid":false,"given":"Emma","family":"Murphy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9813-9323","authenticated-orcid":false,"given":"Eugene","family":"Hickey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"key":"8_CR1","doi-asserted-by":"publisher","unstructured":"Adedinsewo, D.A., et al.: Cardiovascular disease screening in women: Leveraging artificial intelligence and digital tools. Circulation Res. 130, 673\u2013690 (2022). https:\/\/doi.org\/10.1161\/CIRCRESAHA.121.319876","DOI":"10.1161\/CIRCRESAHA.121.319876"},{"key":"8_CR2","doi-asserted-by":"publisher","unstructured":"Avery, J.C., et al.: Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence. Fertility Sterility 121, 164\u2013188 (2024). https:\/\/doi.org\/10.1016\/j.fertnstert.2023.12.008","DOI":"10.1016\/j.fertnstert.2023.12.008"},{"key":"8_CR3","doi-asserted-by":"publisher","unstructured":"Braun, V., Clarke, V.: Using thematic analysis in psychology. Qualitative Res. Psychol. 3, 77\u2013101 (2006). https:\/\/doi.org\/10.1191\/1478088706qp063oa","DOI":"10.1191\/1478088706qp063oa"},{"key":"8_CR4","doi-asserted-by":"crossref","unstructured":"Braun, V., Clarke, V.: Thematic Analysis: A Practical Guide. Sage (2023)","DOI":"10.1007\/978-3-031-17299-1_3470"},{"key":"8_CR5","unstructured":"Creswell, J.W., Vicki, P.C.: Designing and conducting mixed methods research. SAGE Publications, 3 edn. (2018)"},{"key":"8_CR6","doi-asserted-by":"publisher","unstructured":"Crump, J., Suker, A., White, L.: Endometriosis: a review of recent evidence and guidelines. Australian J. General Practice 53, 11\u201318 (2024). https:\/\/doi.org\/10.31128\/AJGP\/04-23-6805","DOI":"10.31128\/AJGP\/04-23-6805"},{"key":"8_CR7","doi-asserted-by":"publisher","unstructured":"Dave, D., et al.: Diagnostic test accuracy of ai-assisted mammography for breast imaging: a narrative review. PeerJ Comput. Sci. 11, e2476 (2025). https:\/\/doi.org\/10.7717\/peerj-cs.2476","DOI":"10.7717\/peerj-cs.2476"},{"key":"8_CR8","doi-asserted-by":"publisher","unstructured":"Diciccio, C., Hsu, B., Yu, Y., Nandy, P., Basu, K.: Detection and mitigation of algorithmic bias via predictive parity. In: ACM International Conference Proceeding Series, pp. 1801\u20131816, June 2023. https:\/\/doi.org\/10.1145\/3593013.3594117","DOI":"10.1145\/3593013.3594117"},{"key":"8_CR9","unstructured":"FDA, U.: General-considerations-for-the-clinical-evaluation-of-drugs. Tech. rep., FDA- Department of Health, Education and Welfare Public Health Service (1977). https:\/\/www.fda.gov\/media\/71495\/download"},{"key":"8_CR10","unstructured":"FDA, U.: 1993-fda-guidance-study-and-evaluation-of-gender-differences-in-the-clinical-evaluation-of-drugs. Tech. rep., FDA - Department of Health and Human Services (7 1993), https:\/\/www.fda.gov\/media\/75648\/download"},{"key":"8_CR11","doi-asserted-by":"publisher","unstructured":"Fitzgerald, C., Hurst, S.: Implicit bias in healthcare professionals: a systematic review. BMC Med. Ethics 18, March 2017. https:\/\/doi.org\/10.1186\/S12910-017-0179-8, https:\/\/pubmed.ncbi.nlm.nih.gov\/28249596\/","DOI":"10.1186\/S12910-017-0179-8"},{"key":"8_CR12","doi-asserted-by":"publisher","unstructured":"Franconi, F., Brunelleschi, S., Steardo, L., Cuomo, V.: Gender differences in drug responses. Pharmacol. Res. 55, 81\u201395 (2007). https:\/\/doi.org\/10.1016\/J.PHRS.2006.11.001, https:\/\/pubmed.ncbi.nlm.nih.gov\/17129734\/","DOI":"10.1016\/J.PHRS.2006.11.001"},{"key":"8_CR13","doi-asserted-by":"publisher","unstructured":"Heaney, A., Murphy, E., Hickey, E.: Bias in context: clinicians\u2019 perceptions of women\u2019s healthcare. In: Kondylakis, H., Triantafyllidis, A. (eds.) Pervasive Computing Technologies for Healthcare - 18th EAI International Conference, Pervasive-Health 2024, Proceedings. pp. 179\u2013197. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer Science and Business Media Deutschland GmbH, Germany (2025). https:\/\/doi.org\/10.1007\/978-3-031-85572-6_11","DOI":"10.1007\/978-3-031-85572-6_11"},{"key":"8_CR14","doi-asserted-by":"publisher","unstructured":"Horgan, R., Nehme, L., Abuhamad, A.: Artificial intelligence in obstetric ultrasound: A scoping review. Prenatal Diagnosis 43, 1176\u20131219 (2023). https:\/\/doi.org\/10.1002\/pd.6411","DOI":"10.1002\/pd.6411"},{"key":"8_CR15","unstructured":"Jeff, L., Surya, M., Lauren, K., Julia, A.: How we analyzed the compas recidivism algorithm \u2014 propublica, May 2016. https:\/\/www.propublica.org\/article\/how-we-analyzed-the-compas-recidivism-algorithm"},{"key":"8_CR16","doi-asserted-by":"publisher","unstructured":"Mauvais-Jarvis, F., et al.: Sex and gender: modifiers of health, disease, and medicine. The Lancet 396, 565\u2013582 (2020). https:\/\/doi.org\/10.1016\/S0140-6736(20)31561-0","DOI":"10.1016\/S0140-6736(20)31561-0"},{"key":"8_CR17","doi-asserted-by":"publisher","unstructured":"Merone, L., Tsey, K., Russell, D., Nagle, C.: \u201ci just want to feel safe going to a doctor\u201d: Experiences of female patients with chronic conditions in australia. Women\u2019s Health Reports 3, \u00a01016 (2022). https:\/\/doi.org\/10.1089\/WHR.2022.0052, \/pmc\/articles\/PMC9811844\/ \/pmc\/articles\/PMC9811844\/?report=abstracthttps:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC9811844\/","DOI":"10.1089\/WHR.2022.0052"},{"key":"8_CR18","doi-asserted-by":"publisher","unstructured":"Offenwanger, A., Milligan, A.J., Chang, M., Bullard, J., Yoon, D.: Diagnosing bias in the gender representation of hci research participants: how it happens and where we are. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1\u201318. ACM, May 2021. https:\/\/doi.org\/10.1145\/3411764.3445383","DOI":"10.1145\/3411764.3445383"},{"key":"8_CR19","doi-asserted-by":"publisher","unstructured":"Ortona, E., Pierdominici, M., Maseli, A., Veroni, C., Aloisi, F., Shoenfeld, Y.: Sex-based differences in autoimmune diseases. Annali dell\u2019Istituto superiore di sanita 52, 205\u2013212 (2016). https:\/\/doi.org\/10.4415\/ANN_16_02_12, https:\/\/pubmed.ncbi.nlm.nih.gov\/27364395\/","DOI":"10.4415\/ANN_16_02_12"},{"key":"8_CR20","doi-asserted-by":"publisher","unstructured":"Pichon, A., Jackman, K.B., Winkler, I.T., Bobel, C., Elhadad, N.: The messiness of the menstruator: assessing personas and functionalities of menstrual tracking apps. J. Am. Med. Inform. Assoc. 29, 385\u2013399 (2022). https:\/\/doi.org\/10.1093\/jamia\/ocab212","DOI":"10.1093\/jamia\/ocab212"},{"key":"8_CR21","doi-asserted-by":"publisher","unstructured":"Rajendran, A., Minhas, A.S., Kazzi, B., Varma, B., Choi, E., Thakkar, A., Michos, E.D.: Sex-specific differences in cardiovascular risk factors and implications for cardiovascular disease prevention in women. Atherosclerosis 384, 117269 (2023). https:\/\/doi.org\/10.1016\/j.atherosclerosis.2023.117269","DOI":"10.1016\/j.atherosclerosis.2023.117269"},{"key":"8_CR22","doi-asserted-by":"publisher","unstructured":"Rajkomar, A., Hardt, M., Howell, M.D., Corrado, G., Chin, M.H.: Ensuring fairness in machine learning to advance health equity. Annal. Internal Med. 169, 866\u2013872 (2018). https:\/\/doi.org\/10.7326\/M18-1990","DOI":"10.7326\/M18-1990"},{"key":"8_CR23","unstructured":"Saleiro, P., et al.: Aequitas: a bias and fairness audit toolkit, November 2018. http:\/\/arxiv.org\/abs\/1811.05577"},{"key":"8_CR24","doi-asserted-by":"publisher","unstructured":"Sawan, M.A., Steinberg, R.S., Sayegh, M.N., Devlin, C., Behbahani-Nejad, O., Wenger, N.K.: Chest pain in women: Gender- and sex-based differences in the presentation and diagnosis of heart disease. US Cardiol. Rev. 17, November 2023. https:\/\/doi.org\/10.15420\/usc.2022.30","DOI":"10.15420\/usc.2022.30"},{"key":"8_CR25","doi-asserted-by":"publisher","unstructured":"Tsirgiotis, J.M., Young, R.L., Weber, N.: A mixed-methods investigation of diagnostician sex\/gender-bias and challenges in assessing females for autism spectrum disorder. J. Autism Dev. Disorders 52, 4474\u20134489 (2022). https:\/\/doi.org\/10.1007\/s10803-021-05300-5","DOI":"10.1007\/s10803-021-05300-5"},{"key":"8_CR26","doi-asserted-by":"publisher","unstructured":"Virani, S.S., et al.: Gender disparities in evidence-based statin therapy in patients with cardiovascular disease. Am. J. Cardiol. 115, 21\u201326 ( 2015). https:\/\/doi.org\/10.1016\/j.amjcard.2014.09.041","DOI":"10.1016\/j.amjcard.2014.09.041"},{"key":"8_CR27","doi-asserted-by":"publisher","unstructured":"Yohannis, A., Kolovos, D.: Towards model-based bias mitigation in machine learning. In: Proceedings - 25th ACM\/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS 2022, pp. 143\u2013153, October 2022. https:\/\/doi.org\/10.1145\/3550355.3552401","DOI":"10.1145\/3550355.3552401"},{"key":"8_CR28","doi-asserted-by":"publisher","unstructured":"Zack, T., et al.: Assessing the potential of gpt-4 to perpetuate racial and gender biases in health care: a model evaluation study. The Lancet Digital Health 6, e12\u2013e22 (2024). https:\/\/doi.org\/10.1016\/S2589-7500(23)00225-X","DOI":"10.1016\/S2589-7500(23)00225-X"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00656-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T22:03:21Z","timestamp":1756418601000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00656-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"ISBN":["9783032006554","9783032006561"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00656-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"20 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"AIiH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on AI in Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiih2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aiih.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}