{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T07:01:35Z","timestamp":1776150095487,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T00:00:00Z","timestamp":1564099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T00:00:00Z","timestamp":1564099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Future of clinical development is on the verge of a major transformation due to convergence of large new digital data sources, computing power to identify clinically meaningful patterns in the data using efficient artificial intelligence and machine-learning algorithms, and regulators embracing this change through new collaborations. This perspective summarizes insights, recent developments, and recommendations for infusing actionable computational evidence into clinical development and health care from academy, biotechnology industry, nonprofit foundations, regulators, and technology corporations. Analysis and learning from publically available biomedical and clinical trial data sets, real-world evidence from sensors, and health records by machine-learning architectures are discussed. Strategies for modernizing the clinical development process by integration of AI- and ML-based digital methods and secure computing technologies through recently announced regulatory pathways at the United States Food and Drug Administration are outlined. We conclude by discussing applications and impact of digital algorithmic evidence to improve medical care for patients.<\/jats:p>","DOI":"10.1038\/s41746-019-0148-3","type":"journal-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T10:03:04Z","timestamp":1564135384000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":424,"title":["Artificial intelligence and machine learning in clinical development: a translational perspective"],"prefix":"10.1038","volume":"2","author":[{"given":"Pratik","family":"Shah","sequence":"first","affiliation":[]},{"given":"Francis","family":"Kendall","sequence":"additional","affiliation":[]},{"given":"Sean","family":"Khozin","sequence":"additional","affiliation":[]},{"given":"Ryan","family":"Goosen","sequence":"additional","affiliation":[]},{"given":"Jianying","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jason","family":"Laramie","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Ringel","sequence":"additional","affiliation":[]},{"given":"Nicholas","family":"Schork","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,26]]},"reference":[{"key":"148_CR1","unstructured":"FDA. Novel Drug Approvals for 2018. https:\/\/www.fda.gov\/drugs\/developmentapprovalprocess\/druginnovation\/ucm592464.htm (2018)."},{"key":"148_CR2","unstructured":"FDA. Companion Diagnostics. https:\/\/www.fda.gov\/medicaldevices\/productsandmedicalprocedures\/invitrodiagnostics\/ucm407297.htm (2018)."},{"key":"148_CR3","doi-asserted-by":"publisher","first-page":"482","DOI":"10.21037\/atm.2016.12.26","volume":"4","author":"JT Jorgensen","year":"2016","unstructured":"Jorgensen, J. T. & Hersom, M. Companion diagnostics-a tool to improve pharmacotherapy. Ann. Transl. Med. 4, 482 (2016).","journal-title":"Ann. Transl. Med."},{"key":"148_CR4","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pctr.0010009","volume":"1","author":"PM Rothwell","year":"2006","unstructured":"Rothwell, P. M. Factors that can affect the external validity of randomised controlled trials. PLoS Clin. Trials 1, e9 (2006).","journal-title":"PLoS Clin. Trials"},{"key":"148_CR5","doi-asserted-by":"publisher","first-page":"224ra224","DOI":"10.1126\/scitranslmed.3007094","volume":"6","author":"C Bettegowda","year":"2014","unstructured":"Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra224 (2014).","journal-title":"Sci. Transl. Med."},{"key":"148_CR6","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/1743-0003-9-21","volume":"9","author":"S Patel","year":"2012","unstructured":"Patel, S., Park, H., Bonato, P., Chan, L. & Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 9, 21 (2012).","journal-title":"J. Neuroeng. Rehabil."},{"key":"148_CR7","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2017-018774","volume":"8","author":"P Shah","year":"2018","unstructured":"Shah, P. et al. Technology-enabled examinations of cardiac rhythm, optic nerve, oral health, tympanic membrane, gait and coordination evaluated jointly with routine health screenings: an observational study at the 2015 Kumbh Mela in India. BMJ Open 8, e018774 (2018).","journal-title":"BMJ Open"},{"key":"148_CR8","unstructured":"Yauney, G. & Shah, P. Reinforcement learning with action-derived rewards for chemotherapy and clinical trial dosing regimen selection. Proc. Mach. Learn. Res. 85, 161\u2013226 (2018)."},{"key":"148_CR9","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1056\/NEJMsb1609216","volume":"375","author":"RE Sherman","year":"2016","unstructured":"Sherman, R. E. et al. Real-world evidence\u2014what is it and what can it tell us? N. Engl. J. Med. 375, 2293\u20132297 (2016).","journal-title":"N. Engl. J. Med."},{"key":"148_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84\u201390 (2017).","journal-title":"Commun. ACM"},{"key":"148_CR11","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1038\/s41591-018-0300-7","volume":"25","author":"EJ Topol","year":"2019","unstructured":"Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44\u201356 (2019).","journal-title":"Nat. Med"},{"key":"148_CR12","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1002\/minf.201501008","volume":"35","author":"E Gawehn","year":"2016","unstructured":"Gawehn, E., Hiss, J. A. & Schneider, G. Deep learning in drug discovery. Mol. Inf. 35, 3\u201314 (2016).","journal-title":"Mol. Inf."},{"key":"148_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-019-0024-5","author":"J Vamathevan","year":"2019","unstructured":"Vamathevan, J. et al. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Disco. https:\/\/doi.org\/10.1038\/s41573-019-0024-5 (2019).","journal-title":"Nat. Rev. Drug Disco"},{"key":"148_CR14","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017).","journal-title":"Nature"},{"key":"148_CR15","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316, 2402\u20132410 (2016).","journal-title":"JAMA"},{"key":"148_CR16","doi-asserted-by":"publisher","unstructured":"Rana, A., Yauney, G., Lowe, A. & Shah, P. Computational histological staining and destaining of prostate core biopsy RGB images with generative adversarial neural networks. 17\n                           th IEEE International Conference on Machine Learning and Applications (ICMLA). (IEEE, Orlando, FL, USA, 2018). https:\/\/doi.org\/10.1109\/ICMLA.2018.00133.","DOI":"10.1109\/ICMLA.2018.00133"},{"key":"148_CR17","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1038\/nbt.3300","volume":"33","author":"B Alipanahi","year":"2015","unstructured":"Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831\u2013838 (2015).","journal-title":"Nat. Biotechnol."},{"key":"148_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-018-0029-1","volume":"1","author":"A Rajkomar","year":"2018","unstructured":"Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. npj Digit. Med. 1, 18 (2018).","journal-title":"npj Digit. Med."},{"key":"148_CR19","unstructured":"Marcus, G. Deep learning: a critical appraisal. Preprint at https:\/\/ui.adsabs.harvard.edu\/\/#abs\/2018arXiv180100631M (2018)."},{"key":"148_CR20","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1016\/S0895-4356(96)00002-9","volume":"49","author":"JV Tu","year":"1996","unstructured":"Tu, J. V. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J. Clin. Epidemiol. 49, 1225\u20131231 (1996).","journal-title":"J. Clin. Epidemiol."},{"issue":"6368","key":"148_CR21","doi-asserted-by":"publisher","first-page":"eaag2612","DOI":"10.1126\/science.aag2612","volume":"358","author":"Dileep George","year":"2017","unstructured":"George, D. et al. A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs. Science 358, https:\/\/doi.org\/10.1126\/science.aag2612 (2017).","journal-title":"Science"},{"key":"148_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva, A. et al. A guide to deep learning in healthcare. Nat. Med. 25, 24\u201329 (2019).","journal-title":"Nat. Med."},{"key":"148_CR23","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41591-018-0320-3","volume":"25","author":"B Norgeot","year":"2019","unstructured":"Norgeot, B., Glicksberg, B. S. & Butte, A. J. A call for deep-learning healthcare. Nat. Med. 25, 14\u201315 (2019).","journal-title":"Nat. Med."},{"key":"148_CR24","unstructured":"FDA. Software as a Medical Device. https:\/\/www.fda.gov\/MedicalDevices\/DigitalHealth\/SoftwareasaMedicalDevice\/ucm20086412.htm (2018)."},{"key":"148_CR25","unstructured":"FDA. Digital Health Innovation Action Plan. https:\/\/www.fda.gov\/medicaldevices\/digitalhealth\/ (2018)."},{"key":"148_CR26","unstructured":"FDA. FDA Permits Marketing of Artificial Intelligence-based Device to Detect Certain Diabetes-related Eye Problems. https:\/\/www.fda.gov\/newsevents\/newsroom\/pressannouncements\/ucm604357.htm (2018)."},{"key":"148_CR27","unstructured":"FDA. FDA Permits Marketing of Clinical Decision Support Software for Alerting Providers of a Potential Stroke in Patients. https:\/\/www.fda.gov\/newsevents\/newsroom\/pressannouncements\/ucm596575.htm (2018)."},{"key":"148_CR28","unstructured":"FDA. Telephone Electrocardiograph Transmitter and Receiver. https:\/\/www.accessdata.fda.gov\/scripts\/cdrh\/cfdocs\/cfpcd\/classification.cfm?id=815 (2019)."},{"key":"148_CR29","unstructured":"Rogers, S. Zebra Medical Gains FDA Approval for AI-powered Heart Disease Detection. https:\/\/venturebeat.com\/2018\/07\/12\/zebra-medical-gains-fda-approval-for-ai-powered-heart-disease-detection\/ (2018)."},{"key":"148_CR30","unstructured":"Center, U. N. Public-Private Consortium Aims to Cut Preclinical Cancer Drug Discovery from Six Years to Just One. https:\/\/www.ucsf.edu\/news\/2017\/10\/408841\/public-private-consortium-aims-cut-preclinical-cancer-drug-discovery-six-years (2017)."},{"key":"148_CR31","unstructured":"FDA. Framework for FDA\u2019s Real-World Evidence Program. https:\/\/www.fda.gov\/science-research\/science-and-research-special-topics\/real-world-evidence (2018)."},{"key":"148_CR32","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41586-018-0579-z","volume":"562","author":"C Bycroft","year":"2018","unstructured":"Bycroft, C. et al. TheUK Biobank resource with deep phenotyping and genomic data. Nature 562, 203\u2013209 (2018).","journal-title":"Nature"},{"key":"148_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2016.35","volume":"3","author":"AE Johnson","year":"2016","unstructured":"Johnson, A. E. et al. MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016).","journal-title":"Sci. Data"},{"key":"148_CR34","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1093\/ije\/dyv098","volume":"44","author":"E Herrett","year":"2015","unstructured":"Herrett, E. et al. Data resource profile: clinical practice research datalink (CPRD). Int J. Epidemiol. 44, 827\u2013836 (2015).","journal-title":"Int J. Epidemiol."},{"key":"148_CR35","unstructured":"Office, M. N. Abdul Latif Jameel Clinic for Machine Learning in Health at MIT aims to revolutionize disease prevention, detection, and treatment. http:\/\/news.mit.edu\/2018\/abdul-latif-jameel-clinic-machine-learning-health-0917 (2018)."},{"key":"148_CR36","unstructured":"Office, M. N. FAQ on the Newly Established MIT Stephen A. Schwarzman College of Computing. http:\/\/news.mit.edu\/2018\/faq-mit-stephen-schwarzman-college-of-computing-1015 (2018)."},{"key":"148_CR37","unstructured":"University, S. Partnership in AI-Assisted Care. https:\/\/aicare.stanford.edu\/index.php (2018)."},{"key":"148_CR38","unstructured":"Office, U. o. C. N. UCI Center to Advance the Use of Artificial Intelligence in Healthcare. http:\/\/www.ucihealth.org\/news\/2018\/07\/ai-center-for-diagnostic-medicine (2018)."},{"key":"148_CR39","unstructured":"Hospital, M. G. MGH & BWH Center for Clinical Data Science. https:\/\/www.massgeneral.org\/imaging\/research\/researchlab.aspx?id=1759 (2018)."},{"key":"148_CR40","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1634\/theoncologist.2015-0054","volume":"20","author":"K Abdallah","year":"2015","unstructured":"Abdallah, K., Hugh-Jones, C., Norman, T., Friend, S. & Stolovitzky, G. The prostate cancer DREAM challenge: a community-wide effort to use open clinical trial data for the quantitative prediction of outcomes in metastatic prostate cancer. Oncologist 20, 459\u2013460 (2015).","journal-title":"Oncologist"},{"key":"148_CR41","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1056\/NEJMsb1702054","volume":"376","author":"MM Bertagnolli","year":"2017","unstructured":"Bertagnolli, M. M. et al. Advantages of a truly open-access data-sharing model. N. Engl. J. Med. 376, 1178\u20131181 (2017).","journal-title":"N. Engl. J. Med."},{"key":"148_CR42","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1056\/NEJMc1314515","volume":"370","author":"M Ferri","year":"2014","unstructured":"Ferri, M. & Abdallah, K. Preparing for responsible sharing of clinical trial data. N. Engl. J. Med. 370, 484\u2013485 (2014).","journal-title":"N. Engl. J. Med."},{"key":"148_CR43","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1038\/nrd4437","volume":"13","author":"D Gill","year":"2014","unstructured":"Gill, D. Re-inventing clinical trials through TransCelerate. Nat. Rev. Drug Disco. 13, 787\u2013788 (2014).","journal-title":"Nat. Rev. Drug Disco."},{"key":"148_CR44","unstructured":"Shah, P. Health 0.0. https:\/\/www.media.mit.edu\/groups\/health-0-0\/overview\/ (2019)."},{"key":"148_CR45","unstructured":"Office, M. N. MIT Media Lab to Participate in $27 Million Initiative on AI Ethics and Governance. http:\/\/news.mit.edu\/2017\/mit-media-lab-to-participate-in-ai-ethics-and-governance-initiative-0110 (2017)."},{"key":"148_CR46","unstructured":"Gottlieb, S. Transforming FDA\u2019s Approach to Digital Health. https:\/\/www.fda.gov\/newsevents\/speeches\/ucm605697.htm (2018)."},{"key":"148_CR47","unstructured":"Gottlieb, S. FDA Budget Matters: A Cross-Cutting Data Enterprise for Real World Evidence. https:\/\/blogs.fda.gov\/fdavoice\/index.php\/2018\/07\/fda-budget-matters-a-cross-cutting-data-enterprise-for-real-world-evidence\/ (2018)."},{"key":"148_CR48","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1038\/nrd.2017.26","volume":"16","author":"S Khozin","year":"2017","unstructured":"Khozin, S., Kim, G. & Pazdur, R. REGULATORY WATCH From big data to smart data: FDA\u2019s INFORMED initiative. Nat. Rev. Drug Disco. 16, 306\u2013306 (2017).","journal-title":"Nat. Rev. Drug Disco."},{"key":"148_CR49","unstructured":"Office, M. N. IBM and MIT to Pursue Joint Research in Artificial Intelligence, Establish new MIT\u2013IBM Watson AI Lab. http:\/\/news.mit.edu\/2017\/ibm-mit-joint-research-watson-artificial-intelligence-lab-0907 (2017)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0148-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0148-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0148-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T18:30:42Z","timestamp":1671301842000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-019-0148-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,26]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,12]]}},"alternative-id":["148"],"URL":"https:\/\/doi.org\/10.1038\/s41746-019-0148-3","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,26]]},"assertion":[{"value":"26 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"69"}}