1932

Abstract

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

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2024-08-27
2025-02-10
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Literature Cited

  1. 1.
    Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. 2018.. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. . npj Digit. Med. 1::39
    [Crossref] [Google Scholar]
  2. 2.
    Adams R, Henry KE, Sridharan A, Soleimani H, Zhan A, et al. 2022.. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. . Nat. Med. 28:(7):145560
    [Crossref] [Google Scholar]
  3. 3.
    Adler-Milstein J, Jha AK. 2017.. HITECH Act drove large gains in hospital electronic health record adoption. . Health Aff. 36:(8):141622
    [Crossref] [Google Scholar]
  4. 4.
    Alaa A, van der Schaar M. 2018.. Limits of estimating heterogeneous treatment effects: guidelines for practical algorithm design. . In Proceedings of the 35th International Conference on Machine Learning, ed. J Dy, A Krause , pp. 12938. Proc. Mach. Learn. Res. 80 . N.p.:: PMLR
    [Google Scholar]
  5. 5.
    Al-Hilli Z, Noss R, Dickard J, Wei W, Chichura A, et al. 2023.. A randomized trial comparing the effectiveness of pre-test genetic counseling using an artificial intelligence automated chatbot and traditional in-person genetic counseling in women newly diagnosed with breast cancer. . Ann. Surg. Oncol. 30:(10):599096
    [Crossref] [Google Scholar]
  6. 6.
    Arora A, Arora A. 2023.. The promise of large language models in health care. . Lancet 401:(10377):641
    [Crossref] [Google Scholar]
  7. 7.
    Badré A, Zhang L, Muchero W, Reynolds JC, Pan C. 2021.. Deep neural network improves the estimation of polygenic risk scores for breast cancer. . J. Hum. Genet. 66:(4):35969
    [Crossref] [Google Scholar]
  8. 8.
    Bastarache L, Denny JC, Roden DM. 2022.. Phenome-wide association studies. . JAMA 327:(1):7576
    [Crossref] [Google Scholar]
  9. 9.
    Beck RW, Riddlesworth T, Ruedy K, Ahmann A, Bergenstal R, et al. 2017.. Effect of continuous glucose monitoring on glycemic control in adults with type 1 diabetes using insulin injections: the DIAMOND randomized clinical trial. . JAMA 317:(4):37178
    [Crossref] [Google Scholar]
  10. 10.
    Bender EM, Gebru T, McMillan-Major A, Shmitchell S. 2021.. On the dangers of stochastic parrots: Can language models be too big?. In FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 61023. New York:: ACM
    [Google Scholar]
  11. 11.
    Berner ES, Detmer DE, Simborg D. 2005.. Will the wave finally break? A brief view of the adoption of electronic medical records in the United States. . J. Am. Med. Inform. Assoc. 12:(1):37
    [Crossref] [Google Scholar]
  12. 12.
    Bommasani R, Hudson DA, Adeli E, Altman R, Arora S, et al. 2021.. On the opportunities and risks of foundation models. . arXiv:2108.07258 [cs.LG]
  13. 13.
    Bonham VL, Callier SL, Royal CD. 2016.. Will precision medicine move us beyond race?. N. Engl. J. Med. 374:(21):20035
    [Crossref] [Google Scholar]
  14. 14.
    Caswell-Jin JL, Gupta T, Hall E, Petrovchich IM, Mills MA, et al. 2018.. Racial/ethnic differences in multiple-gene sequencing results for hereditary cancer risk. . Genet. Med. 20:(2):23439
    [Crossref] [Google Scholar]
  15. 15.
    Cent. Dis. Control Prev. 2019.. Antibiotic resistance threats in the United States, 2019. Rep. , Cent. Dis. Control Prev., Atlanta:
    [Google Scholar]
  16. 16.
    Chapman EN, Kaatz A, Carnes M. 2013.. Physicians and implicit bias: how doctors may unwittingly perpetuate health care disparities. . J. Gen. Intern. Med. 28:(11):150410
    [Crossref] [Google Scholar]
  17. 17.
    Chen M, Tan X, Padman R. 2020.. Social determinants of health in electronic health records and their impact on analysis and risk prediction: a systematic review. . J. Am. Med. Inform. Assoc. 27:(11):176473
    [Crossref] [Google Scholar]
  18. 18.
    Chung R, Xu Z, Arnold M, Ip S, Harrison H, et al. 2023.. Using polygenic risk scores for prioritizing individuals at greatest need of a cardiovascular disease risk assessment. . J. Am. Heart Assoc. 12:(15):e029296
    [Crossref] [Google Scholar]
  19. 19.
    Curth A, van der Schaar M. 2021.. Nonparametric estimation of heterogeneous treatment effects: from theory to learning algorithms. . In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, ed. A Banerjee, K Fukumizu , pp. 181018. Proc. Mach. Learn. Res. 130 . N.p.:: PMLR
    [Google Scholar]
  20. 20.
    Cutler DM. 2020.. Early returns from the era of precision medicine. . JAMA 323:(2):10910
    [Crossref] [Google Scholar]
  21. 21.
    Döhner H, Wei AH, Löwenberg B. 2021.. Towards precision medicine for AML. . Nat. Rev. Clin. Oncol. 18:(9):57790
    [Crossref] [Google Scholar]
  22. 22.
    Escobar GJ, Liu VX, Schuler A, Lawson B, Greene JD, Kipnis P. 2020.. Automated identification of adults at risk for in-hospital clinical deterioration. . N. Engl. J. Med. 383:(20):195160
    [Crossref] [Google Scholar]
  23. 23.
    Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, et al. 2017.. Dermatologist-level classification of skin cancer with deep neural networks. . Nature 542:(7639):11518
    [Crossref] [Google Scholar]
  24. 24.
    Faden RR, Kass NE, Goodman SN, Pronovost P, Tunis S, Beauchamp TL. 2013.. An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics. . Hastings Cent. Rep. 43:(S1):S1627
    [Crossref] [Google Scholar]
  25. 25.
    Fatumo S, Chikowore T, Choudhury A, Ayub M, Martin AR, Kuchenbaecker K. 2022.. A roadmap to increase diversity in genomic studies. . Nat. Med. 28:(2):24350
    [Crossref] [Google Scholar]
  26. 26.
    Ferruz N, Höcker B. 2022.. Controllable protein design with language models. . Nat. Mach. Intell. 4:(6):52132
    [Crossref] [Google Scholar]
  27. 27.
    Floridi L, Chiriatti M. 2020.. GPT-3: its nature, scope, limits, and consequences. . Minds Mach. 30:(4):68194
    [Crossref] [Google Scholar]
  28. 28.
    Ford D, Easton DF. 1995.. The genetics of breast and ovarian cancer. . Br. J. Cancer 72:(4):80512
    [Crossref] [Google Scholar]
  29. 29.
    Fox I, Lee J, Pop-Busui R, Wiens J. 2020.. Deep reinforcement learning for closed-loop blood glucose control. . In Proceedings of the 5th Machine Learning for Healthcare Conference, ed. F Doshi-Velez, J Fackler, K Jung, D Kale, R Ranganath, et al. , pp. 50836. Proc. Mach. Learn. Res. 126 . N.p.:: PMLR
    [Google Scholar]
  30. 30.
    Frangoul H, Altshuler D, Cappellini MD, Chen Y-S, Domm J, et al. 2021.. CRISPR-Cas9 gene editing for sickle cell disease and β-thalassemia. . N. Engl. J. Med. 384:(3):25260
    [Crossref] [Google Scholar]
  31. 31.
    Gauderman WJ, Mukherjee B, Aschard H, Hsu L, Lewinger JP, et al. 2017.. Update on the state of the science for analytical methods for gene-environment interactions. . Am. J. Epidemiol. 186:(7):76270
    [Crossref] [Google Scholar]
  32. 32.
    Gavan SP, Thompson AJ, Payne K. 2018.. The economic case for precision medicine. . Expert Rev. Precis. Med. Drug Dev. 3:(1):19
    [Crossref] [Google Scholar]
  33. 33.
    Geiger HJ. 2003.. Racial and ethnic disparities in diagnosis and treatment: a review of the evidence and a consideration of causes. . In Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, ed. BD Smedley, AY Stith, AR Nelson , pp. 41754. Washington, DC:: Natl. Acad. Press
    [Google Scholar]
  34. 34.
    Ghassemi M, Naumann T, Doshi-Velez F, Brimmer N, Joshi R, et al. 2014.. Unfolding physiological state: mortality modelling in intensive care units. . In KDD ’14: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 7584. New York:: ACM
    [Google Scholar]
  35. 35.
    Gichoya JW, Banerjee I, Bhimireddy AR, Burns JL, Celi LA, et al. 2022.. AI recognition of patient race in medical imaging: a modelling study. . Lancet Digit. Health 4:(6):e40614
    [Crossref] [Google Scholar]
  36. 36.
    Giordanengo A, Årsand E, Woldaregay AZ, Bradway M, Grottland A, et al. 2019.. Design and prestudy assessment of a dashboard for presenting self-collected health data of patients with diabetes to clinicians: iterative approach and qualitative case study. . JMIR Diabetes 4:(3):e14002
    [Crossref] [Google Scholar]
  37. 37.
    Gottesman O, Johansson F, Komorowski M, Faisal A, Sontag D, et al. 2019.. Guidelines for reinforcement learning in healthcare. . Nat. Med. 25:(1):1618
    [Crossref] [Google Scholar]
  38. 38.
    Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, et al. 2016.. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. . JAMA 316:(22):240210
    [Crossref] [Google Scholar]
  39. 39.
    Hallowell N, Cooke S, Crawford G, Lucassen A, Parker M. 2009.. Distinguishing research from clinical care in cancer genetics: theoretical justifications and practical strategies. . Soc. Sci. Med. 68:(11):201017
    [Crossref] [Google Scholar]
  40. 40.
    Heale R, Forbes D. 2013.. Understanding triangulation in research. . Evid.-Based Nurs. 16:(4):98
    [Crossref] [Google Scholar]
  41. 41.
    Henry KE, Hager DN, Pronovost PJ, Saria S. 2015.. A targeted real-time early warning score (TREWScore) for septic shock. . Sci. Transl. Med. 7:(299):299ra122
    [Crossref] [Google Scholar]
  42. 42.
    Hu Y, Huerta J, Cordella N, Mishuris RG, Paschalidis IC. 2023.. Personalized hypertension treatment recommendations by a data-driven model. . BMC Med. Inform. Decis. Mak. 23::44
    [Crossref] [Google Scholar]
  43. 43.
    Jabbour S, Fouhey D, Kazerooni E, Sjoding MW, Wiens J. 2020.. Deep learning applied to chest X-rays: exploiting and preventing shortcuts. . In Proceedings of the 5th Machine Learning for Healthcare Conference, ed. F Doshi-Velez, J Fackler, K Jung, D Kale, R Ranganath, et al ., pp. 75082. Proc. Mach. Learn. Res. 126 . N.p.:: PMLR
    [Google Scholar]
  44. 44.
    Jabbour S, Fouhey D, Kazerooni E, Wiens J, Sjoding MW. 2022.. Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure. . J. Am. Med. Inform. Assoc. 29:(6):106068
    [Crossref] [Google Scholar]
  45. 45.
    Jagsi R, Suresh K, Krenz CD, Jones RD, Griffith KA, et al. 2023.. Health data sharing perspectives of patients receiving care in CancerLinQ-participating oncology practices. . JCO Oncol. Pract. 19:(8):62636
    [Crossref] [Google Scholar]
  46. 46.
    Joffe S, Weeks JC. 2002.. Views of American oncologists about the purposes of clinical trials. . J. Natl. Cancer Inst. 94:(24):184753
    [Crossref] [Google Scholar]
  47. 47.
    Johnson AEW, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. 2016.. Machine learning and decision support in critical care. . Proc. IEEE 104:(2):44466
    [Crossref] [Google Scholar]
  48. 48.
    Johnson AEW, Pollard TJ, Shen L, Lehman L-WH, Feng M, et al. 2016.. MIMIC-III, a freely accessible critical care database. . Sci. Data 3::160035
    [Crossref] [Google Scholar]
  49. 49.
    Kachuri L, Graff RE, Smith-Byrne K, Meyers TJ, Rashkin SR, et al. 2020.. Pan-cancer analysis demonstrates that integrating polygenic risk scores with modifiable risk factors improves risk prediction. . Nat. Commun. 11::6084
    [Crossref] [Google Scholar]
  50. 50.
    Kamineni M, Ötleş Meng E, Oh J, Rao K, Young VB, et al. 2022.. Prospective evaluation of data-driven models to predict daily risk of Clostridioides difficile infection at 2 large academic health centers. . Infect. Control Hosp. Epidemiol. 44:(7):116366
    [Crossref] [Google Scholar]
  51. 51.
    Kamran F, Tang S, Otles E, McEvoy DS, Saleh SN, et al. 2022.. Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study. . BMJ 376::e068576
    [Crossref] [Google Scholar]
  52. 52.
    Kaushal A, Altman R, Langlotz C. 2020.. Geographic distribution of US cohorts used to train deep learning algorithms. . JAMA 324:(12):121213
    [Crossref] [Google Scholar]
  53. 53.
    Klonoff DC, Ahn D, Drincic A. 2017.. Continuous glucose monitoring: a review of the technology and clinical use. . Diabetes Res. Clin. Pract. 133::17892
    [Crossref] [Google Scholar]
  54. 54.
    Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. 2018.. The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care. . Nat. Med. 24:(11):171620
    [Crossref] [Google Scholar]
  55. 55.
    Lambert SA, Gil L, Jupp S, Ritchie SC, Xu Y, et al. 2021.. The Polygenic Score Catalog as an open database for reproducibility and systematic evaluation. . Nat. Genet. 53:(4):42025
    [Crossref] [Google Scholar]
  56. 56.
    Lee SS-J, Appelbaum PS, Chung WK. 2022.. Challenges and potential solutions to health disparities in genomic medicine. . Cell 185:(12):200710
    [Crossref] [Google Scholar]
  57. 57.
    Levine S, Malone E, Lekiachvili A, Briss P. 2019.. health care industry insights: why the use of preventive services is still low. . Prev. Chronic Dis. 16::E30
    [Crossref] [Google Scholar]
  58. 58.
    Lewis CM, Vassos E. 2020.. Polygenic risk scores: from research tools to clinical instruments. . Genome Med. 12::44
    [Crossref] [Google Scholar]
  59. 59.
    Li J, Li X, Zhang S, Snyder M. 2019.. Gene-environment interaction in the era of precision medicine. . Cell 177:(1):3844
    [Crossref] [Google Scholar]
  60. 60.
    Lidz CW, Appelbaum PS. 2002.. The therapeutic misconception: problems and solutions. . Med. Care 40:(9):V5563
    [Crossref] [Google Scholar]
  61. 61.
    Liu VX, Lu Y, Carey KA, Gilbert ER, Afshar M, et al. 2020.. Comparison of early warning scoring systems for hospitalized patients with and without infection at risk for in-hospital mortality and transfer to the intensive care unit. . JAMA Netw. Open 3:(5):e205191
    [Crossref] [Google Scholar]
  62. 62.
    Lynch HF, Wolf LE, Barnes M. 2019.. Implementing regulatory broad consent under the revised common rule: clarifying key points and the need for evidence. . J. Law Med. Ethics 47:(2):21331
    [Crossref] [Google Scholar]
  63. 63.
    Maas P, Barrdahl M, Joshi AD, Auer PL, Gaudet MM, et al. 2016.. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. . JAMA Oncol. 2:(10):1295302
    [Crossref] [Google Scholar]
  64. 64.
    Manolio TA, Goodhand P, Ginsburg G. 2020.. The International Hundred Thousand Plus Cohort Consortium: integrating large-scale cohorts to address global scientific challenges. . Lancet Digit. Health 2:(11):e56768
    [Crossref] [Google Scholar]
  65. 65.
    Manrai AK, Funke BH, Rehm HL, Olesen MS, Maron BA, et al. 2016.. Genetic misdiagnoses and the potential for health disparities. . N. Engl. J. Med. 375:(7):65565
    [Crossref] [Google Scholar]
  66. 66.
    Maxwell KN, Domchek SM, Nathanson KL, Robson ME. 2016.. Population frequency of germline BRCA1/2 mutations. . J. Clin. Oncol. 34:(34):418385
    [Crossref] [Google Scholar]
  67. 67.
    Messer LH, Addala A, Weinzimer SA. 2023.. Real-world diabetes technology: overcoming barriers and disparities. . Diabetes Technol. Ther. 25:(S1):S17690
    [Crossref] [Google Scholar]
  68. 68.
    Moerkerke B, Vansteelandt S, Lange C. 2010.. A doubly robust test for gene-environment interaction in family-based studies of affected offspring. . Biostatistics 11:(2):21325
    [Crossref] [Google Scholar]
  69. 69.
    Moody GB. 2022.. PhysioNet. . In Encyclopedia of Computational Neuroscience, ed. D Jaeger, R Jung , pp. 28068. New York:: Springer
    [Google Scholar]
  70. 70.
    Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, et al. 2023.. Foundation models for generalist medical artificial intelligence. . Nature 616:(7956):25965
    [Crossref] [Google Scholar]
  71. 71.
    Morse B, Kim KK, Xu Z, Matsumoto CG, Schilling LM, et al. 2023.. Patient and researcher stakeholder preferences for use of electronic health record data: a qualitative study to guide the design and development of a platform to honor patient preferences. . J. Am. Med. Inform. Assoc. 30:(6):113749
    [Crossref] [Google Scholar]
  72. 72.
    Murphy KP. 2012.. Machine Learning: A Probabilistic Perspective, Vol. 1. Cambridge, MA:: MIT Press
    [Google Scholar]
  73. 73.
    Nero C, Ciccarone F, Boldrini L, Lenkowicz J, Paris I, et al. 2020.. Germline BRCA 1–2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study). . Sci. Rep. 10::16511
    [Crossref] [Google Scholar]
  74. 74.
    Nori H, King N, McKinney SM, Carignan D, Horvitz E. 2023.. Capabilities of GPT-4 on medical challenge problems. . arXiv:2303.13375 [cs.CL]
  75. 75.
    Obermeyer Z, Powers B, Vogeli C, Mullainathan S. 2019.. Dissecting racial bias in an algorithm used to manage the health of populations. . Science 366:(6464):44753
    [Crossref] [Google Scholar]
  76. 76.
    O'Rourke P, Spector-Bagdady K, Wendler D. 2022.. Waivers and alterations. . Rethinking Clinical Trials. https://rethinkingclinicaltrials.org/chapters/ethics-and-regulatory/consent-waiver-of-consent-and-notification/waivers-and-alterations
    [Google Scholar]
  77. 77.
    Parbhoo S, Bogojeska J, Zazzi M, Roth V, Doshi-Velez F. 2017.. Combining kernel and model based learning for HIV therapy selection. . In 2017 AMIA Joint Summit on Translational Science, pp. 23948. Washington, DC:: Am. Med. Inform. Assoc.
    [Google Scholar]
  78. 78.
    Park J, Choi J-Y, Choi J, Chung S, Song N, et al. 2021.. Gene-environment interactions relevant to estrogen and risk of breast cancer: Can gene-environment interactions be detected only among candidate SNPs from genome-wide association studies?. Cancers 13:(10):2370
    [Crossref] [Google Scholar]
  79. 79.
    Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, et al. 2019.. Large-scale assessment of a smartwatch to identify atrial fibrillation. . N. Engl. J. Med. 381:(20):190917
    [Crossref] [Google Scholar]
  80. 80.
    Perez-Pozuelo I, Spathis D, Clifton EAD, Mascolo C. 2021.. Wearables, smartphones, and artificial intelligence for digital phenotyping and health. . In Digital Health: Mobile and Wearable Devices for Participatory Health Applications, ed. S Syed-Abdul, X Zhu, L Fernandez-Luque , pp. 3354. Amsterdam:: Elsevier
    [Google Scholar]
  81. 81.
    Pierdomenico SD, Lapenna D, Di Tommaso R, Di Carlo S, Esposito AL, et al. 2006.. Blood pressure variability and cardiovascular risk in treated hypertensive patients. . Am. J. Hypertens. 19:(10):99197
    [Crossref] [Google Scholar]
  82. 82.
    Pollard TJ, Johnson AEW, Raffa JD, Celi LA, Mark RG, Badawi O. 2018.. The eICU Collaborative Research Database, a freely available multi-center database for critical care research. . Sci. Data 5::180178
    [Crossref] [Google Scholar]
  83. 83.
    Predel C, Steger F. 2020.. Ethical challenges with smartwatch-based screening for atrial fibrillation: putting users at risk for marketing purposes?. Front. Cardiovasc Med. 7::615927
    [Crossref] [Google Scholar]
  84. 84.
    Price WN II. 2019.. Medical AI and contextual bias. . Harv. J. Law Technol. 33:(1):65116
    [Google Scholar]
  85. 85.
    Raj M, De Vries R, Nong P, Kardia SLR, Platt JE. 2020.. Do people have an ethical obligation to share their health information? Comparing narratives of altruism and health information sharing in a nationally representative sample. . PLOS ONE 15:(12):e0244767
    [Crossref] [Google Scholar]
  86. 86.
    Rajpurkar P, Chen E, Banerjee O, Topol EJ. 2022.. AI in health and medicine. . Nat. Med. 28:(1):3138
    [Crossref] [Google Scholar]
  87. 87.
    Ray P, Birolleau S, Lefort Y, Becquemin M-H, Beigelman C, et al. 2006.. Acute respiratory failure in the elderly: etiology, emergency diagnosis and prognosis. . Crit. Care 10:(3):R82
    [Crossref] [Google Scholar]
  88. 88.
    Rini C, Henderson GE, Evans JP, Berg JS, Foreman AKM, et al. 2020.. Genomic knowledge in the context of diagnostic exome sequencing: changes over time, persistent subgroup differences, and associations with psychological sequencing outcomes. . Genet. Med. 22:(1):6068
    [Crossref] [Google Scholar]
  89. 89.
    Rudin C. 2019.. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. . Nat. Mach. Intell. 1:(5):20615
    [Crossref] [Google Scholar]
  90. 90.
    Russell S, Norvig P. 2016.. Artificial Intelligence: A Modern Approach. Self-Publ., CreateSpace Indep. Publ. Platf.:
    [Google Scholar]
  91. 91.
    Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, et al. 2022.. Mendelian randomization. . Nat. Rev. Methods Primers 2::6
    [Crossref] [Google Scholar]
  92. 92.
    Schalkoff RJ. 1990.. Artificial Intelligence: An Engineering Approach. New York:: McGraw-Hill
    [Google Scholar]
  93. 93.
    Shalit U, Johansson FD, Sontag D. 2017.. Estimating individual treatment effect: generalization bounds and algorithms. . In Proceedings of the 34th International Conference on Machine Learning, ed. D Precup, YW Teh , pp. 307685. Proc. Mach. Learn. Res. 70 . N.p.:: PMLR
    [Google Scholar]
  94. 94.
    Shao J, Ma J, Zhang Q, Li W, Wang C. 2023.. Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. . Semin. Cancer Biol. 91::115
    [Crossref] [Google Scholar]
  95. 95.
    Shuman AG, Gornick MC, Brummel C, Kent M, Spector-Bagdady K, et al. 2020.. Patient and provider perspectives regarding enrollment in head and neck cancer research. . Otolaryngol. Head Neck Surg. 162:(1):7378
    [Crossref] [Google Scholar]
  96. 96.
    Silva PP, Gaudillo JD, Vilela JA, Roxas-Villanueva RML, Tiangco BJ, et al. 2022.. A machine learning-based SNP-set analysis approach for identifying disease-associated susceptibility loci. . Sci. Rep. 12::15817
    [Crossref] [Google Scholar]
  97. 97.
    Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, et al. 2017.. Mastering the game of Go without human knowledge. . Nature 550:(7676):35459
    [Crossref] [Google Scholar]
  98. 98.
    Singh K, Valley TS, Tang S, Li BY, Kamran F, et al. 2021.. Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19. . Ann. Am. Thorac. Soc. 18:(7):112937
    [Crossref] [Google Scholar]
  99. 99.
    Smith MA, Adelaine S, Bednarz L, Patterson BW, Pothof J, Liao F. 2021.. Predictive solutions in learning health systems: the critical need to systematize implementation of prediction to action to intervention. . NEJM Catal. 2:(5). https://doi.org/10.1056/CAT.20.0650
    [Google Scholar]
  100. 100.
    Smith RD, Levy P, Ferrario CM (Consid. Noninvasive Hemodyn. Monit. Target Reduct. Blood Press. Levels Study Group). 2006.. Value of noninvasive hemodynamics to achieve blood pressure control in hypertensive subjects. . Hypertension 47:(4):77177
    [Crossref] [Google Scholar]
  101. 101.
    Spector-Bagdady K. 2023.. Generative-AI-generated challenges for health data research. . Am. J. Bioeth. 23:(10):15
    [Crossref] [Google Scholar]
  102. 102.
    Spector-Bagdady K, De Vries RG, Gornick MG, Shuman AG, Kardia S, Platt J. 2018.. Encouraging participation and transparency in biobank research. . Health Aff. 37:(8):131320
    [Crossref] [Google Scholar]
  103. 103.
    Spector-Bagdady K, Kent M, Krenz CD, Brummel C, Swiecicki PL, et al. 2022.. Patient and provider perspectives on enrollment in precision oncology research: qualitative ethical analysis. . JMIR Cancer 8:(2):e35033
    [Crossref] [Google Scholar]
  104. 104.
    Spector-Bagdady K, Tang S, Jabbour S, Price WN II, Bracic A, et al. 2021.. Respecting autonomy and enabling diversity: the effect of eligibility and enrollment on research data demographics. . Health Aff. 40:(12):189299
    [Crossref] [Google Scholar]
  105. 105.
    Spector-Bagdady K, Trinidad G, Kardia S, Krenz CD, Nong P, et al. 2022.. Reported interest in notification regarding use of health information and biospecimens. . JAMA 328:(5):47476
    [Crossref] [Google Scholar]
  106. 106.
    SPRINT Res. Group. 2015.. A randomized trial of intensive versus standard blood-pressure control. . N. Engl. J. Med. 373:(22):210316
    [Crossref] [Google Scholar]
  107. 107.
    Stern AD, Alexander BM, Chandra A. 2017.. How economics can shape precision medicines. . Science 355:(6330):113133
    [Crossref] [Google Scholar]
  108. 108.
    Subhan MA, Parveen F, Shah H, Yalamarty SSK, Ataide JA, Torchilin VP. 2023.. Recent advances with precision medicine treatment for breast cancer including triple-negative sub-type. . Cancers 15:(8):2204
    [Crossref] [Google Scholar]
  109. 109.
    Sud A, Turnbull C, Houlston R. 2021.. Will polygenic risk scores for cancer ever be clinically useful?. npj Precis. Oncol. 5::40
    [Crossref] [Google Scholar]
  110. 110.
    Sundström J, Lind L, Nowrouzi S, Hagström E, Held C, et al. 2023.. Heterogeneity in blood pressure response to 4 antihypertensive drugs: a randomized clinical trial. . JAMA 329:(14):116069
    [Crossref] [Google Scholar]
  111. 111.
    Sutton RS, Barto AG. 2018.. Reinforcement Learning: An Introduction. Cambridge, MA:: MIT Press. , 2nd ed..
    [Google Scholar]
  112. 112.
    Szymczak S, Biernacka JM, Cordell HJ, González-Recio O, König IR, et al. 2009.. Machine learning in genome-wide association studies. . Genet. Epidemiol. 33:(Suppl. 1):S5157
    [Google Scholar]
  113. 113.
    Tang S, Davarmanesh P, Song Y, Koutra D, Sjoding MW, Wiens J. 2020.. Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data. . J. Am. Med. Inform. Assoc. 27:(12):192134
    [Crossref] [Google Scholar]
  114. 114.
    Tang S, Makar M, Sjoding M, Doshi-Velez F, Wiens J. 2022.. Leveraging factored action spaces for efficient offline reinforcement learning in healthcare. . In Advances in Neural Information Processing Systems 35, ed. S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho, A Oh , pp. 3427286. Red Hook, NY:: Curran
    [Google Scholar]
  115. 115.
    Tang S, Modi A, Sjoding M, Wiens J. 2020.. Clinician-in-the-loop decision making: reinforcement learning with near-optimal set-valued policies. . In Proceedings of the 37th International Conference on Machine Learning, ed. H Daumé III, A Singh , pp. 938796. Proc. Mach. Learn. Res. 119 . N.p.:: PMLR
    [Google Scholar]
  116. 116.
    Tang S, Wiens J. 2021.. Model selection for offline reinforcement learning: practical considerations for healthcare settings. . In Proceedings of the 6th Machine Learning for Healthcare Conference, ed. K Jung, S Yeung, M Sendak, M Sjoding, R Ranganath , pp. 235. Proc. Mach. Learn. Res. 149 . N.p.:: PMLR
    [Google Scholar]
  117. 117.
    Topol EJ. 2023.. As artificial intelligence goes multimodal, medical applications multiply. . Science 381:(6663):adk6139
    [Crossref] [Google Scholar]
  118. 118.
    Trinidad MG, Ryan KA, Krenz CD, Roberts JS, McGuire AL, et al. 2023.. “ Extremely slow and capricious”: a qualitative exploration of genetic researcher priorities in selecting shared data resources. . Genet. Med. 25:(1):11524
    [Crossref] [Google Scholar]
  119. 119.
    Tsoi KKF, Chan NB, Yiu KKL, Poon SKS, Lin B, Ho K. 2020.. Machine learning clustering for blood pressure variability applied to Systolic Blood Pressure Intervention Trial (SPRINT) and the Hong Kong Community Cohort. . Hypertension 76:(2):56976
    [Crossref] [Google Scholar]
  120. 120.
    Uffelmann E, Huang QQ, Munung NS, de Vries J, Okada Y, et al. 2021.. Genome-wide association studies. . Nat. Rev. Methods Primers 1::59
    [Crossref] [Google Scholar]
  121. 121.
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, et al. 2017.. Attention is all you need. . In Advances in Neural Information Processing Systems 30, ed. I Guyon, U Von Luxburg, S Bengio, H Wallach, R Fergus, et al. , pp. 59996009. Red Hook, NY:: Curran
    [Google Scholar]
  122. 122.
    Wager S, Athey S. 2018.. Estimation and inference of heterogeneous treatment effects using random forests. . J. Am. Stat. Assoc. 113:(523):122842
    [Crossref] [Google Scholar]
  123. 123.
    Wang J, Jabbour S, Makar M, Sjoding M, Wiens J. 2022.. Learning concept credible models for mitigating shortcuts. . In Advances in Neural Information Processing Systems 35, ed. S Koyejo, S Mohamed, A Agarwal, D Belgrave, K Cho, A Oh , pp. 3334356. Red Hook, NY:: Curran
    [Google Scholar]
  124. 124.
    Wang S, McDermott MBA, Chauhan G, Ghassemi M, Hughes MC, Naumann T. 2020.. MIMIC-Extract: a data extraction, preprocessing, and representation pipeline for MIMIC-III. . In CHIL ’20: Proceedings of the ACM Conference on Health, Inference, and Learning, pp. 22235. New York:: ACM
    [Google Scholar]
  125. 125.
    Wang X, Zou C, Zhang Y, Li X, Wang C, et al. 2021.. Prediction of BRCA gene mutation in breast cancer based on deep learning and histopathology images. . Front. Genet. 12::661109
    [Crossref] [Google Scholar]
  126. 126.
    Watson DS. 2022.. Interpretable machine learning for genomics. . Hum. Genet. 141:(9):14991513
    [Crossref] [Google Scholar]
  127. 127.
    Wiens J, Creary M, Sjoding MW. 2022.. AI models in health care are not colour blind and we should not be either. . Lancet Digit. Health 4:(6):e399400
    [Crossref] [Google Scholar]
  128. 128.
    Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, et al. 2019.. Do no harm: a roadmap for responsible machine learning for health care. . Nat. Med. 25:(9):133740
    [Crossref] [Google Scholar]
  129. 129.
    Williams DR, Wyatt R. 2015.. Racial bias in health care and health: challenges and opportunities. . JAMA 314:(6):55556
    [Crossref] [Google Scholar]
  130. 130.
    Wong A, Otles E, Donnelly JP, Krumm A, McCullough J, et al. 2021.. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. . JAMA Intern. Med. 181:(8):106570
    [Crossref] [Google Scholar]
  131. 131.
    Wong JA, Conen D, Van Gelder IC, McIntyre WF, Crijns HJ, et al. 2018.. Progression of device-detected subclinical atrial fibrillation and the risk of heart failure. . J. Am. Coll. Cardiol. 71:(23):260311
    [Crossref] [Google Scholar]
  132. 132.
    Wornow M, Gyang Ross E, Callahan A, Shah NH. 2023.. APLUS: a Python library for usefulness simulations of machine learning models in healthcare. . J. Biomed. Inform. 139::104319
    [Crossref] [Google Scholar]
  133. 133.
    Wu M, Ma S. 2019.. Robust genetic interaction analysis. . Brief. Bioinform. 20:(2):62437
    [Crossref] [Google Scholar]
  134. 134.
    Wu S, Xu Y, Zhang Q, Ma S. 2023.. Gene-environment interaction analysis via deep learning. . Genet. Epidemiol. 47:(3):26186
    [Crossref] [Google Scholar]
  135. 135.
    Yang S-R, Schultheis AM, Yu H, Mandelker D, Ladanyi M, Büttner R. 2022.. Precision medicine in non-small cell lung cancer: current applications and future directions. . Semin. Cancer Biol. 84::18498
    [Crossref] [Google Scholar]
  136. 136.
    Yang T, Li X, Montazeri Z, Little J, Farrington SM, et al. 2019.. Gene-environment interactions and colorectal cancer risk: an umbrella review of systematic reviews and meta-analyses of observational studies. . Int. J. Cancer 145:(9):231529
    [Crossref] [Google Scholar]
  137. 137.
    Yazici H, Odemis DA, Aksu D, Erdogan OS, Tuncer SB, et al. 2020.. New approach for risk estimation algorithms of BRCA1/2 negativeness detection with modelling supervised machine learning techniques. . Dis. Mark. 2020::8594090
    [Google Scholar]
  138. 138.
    Zhou W, Kanai M, Wu K-HH, Rasheed H, Tsuo K, et al. 2022.. Global Biobank Meta-Analysis Initiative: powering genetic discovery across human disease. . Cell Genom. 2:(10):100192
    [Crossref] [Google Scholar]
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