Skip to content
2000
image of A Review of Software in Clinical Trials: FDA Regulatory Frameworks and Addressing Challenges

Abstract

An essential tool for assessing the efficacy and safety of novel therapies and interventions is the clinical trial. They are crucial for understanding disease causes, treatment effectiveness, and patient care processes. However, traditional clinical trials often suffer from inefficiencies, high costs, and extended timelines. This review explores how artificial intelligence can revolutionize clinical trials by addressing these inefficiencies in trial design, patient recruitment, and data analysis. It also discusses the challenges and solutions for incorporating AI within existing regulatory frameworks. This review is based on a comprehensive analysis of the existing literature on artificial intelligence applications in clinical trials. It includes an evaluation of studies that assess the role of artificial intelligence in enhancing trial efficiency, optimizing patient recruitment, and improving data analysis. Special attention is given to regulatory considerations, with a focus on Food and Drug Administration (FDA) guidelines and their impact on artificial intelligence integration in clinical research. The successful integration of artificial intelligence into clinical trials has the potential to optimize procedures, enhance clinical judgment, and improve patient outcomes. Artificial intelligence can streamline patient stratification, accelerate trial timelines, and enhance data analysis accuracy. However, overcoming challenges related to interpretability, data privacy, and regulatory compliance is crucial. Collaboration between researchers, artificial intelligence developers, and regulatory bodies is essential to establish guidelines ensuring artificial intelligence innovations are safe and effective. Ultimately, artificial intelligence could transform clinical research and pave the way for more personalized healthcare solutions.

Loading

Article metrics loading...

/content/journals/rrct/10.2174/0115748871359356250523033831
2025-05-29
2025-10-31
Loading full text...

Full text loading...

References

  1. Amisha M. Malik P. Pathania M. Rathaur V. Overview of artificial intelligence in medicine. J. Family Med. Prim. Care 2019 8 7 2328 2331 10.4103/jfmpc.jfmpc_440_19 31463251
    [Google Scholar]
  2. Bartels A. Ruchatz T. Brosig S. Intelligence in the automobile of the future. Smart Mobile In-Vehicle Systems. Springer 2013 35 46 10.1007/978‑1‑4614‑9120‑0_3
    [Google Scholar]
  3. Patel S. Aspects of Artificial Intelligence. Learning Outcomes of Classroom Research. INDIA Ordine Nuovo citation 2022
    [Google Scholar]
  4. Wang A. Xiu X. Liu S. Qian Q. Wu S. Characteristics of artificial intelligence clinical trials in the field of healthcare: A cross-sectional study. Int. J. Environ. Res. Public Health 2022 19 20 13691 10.3390/ijerph192013691 36294269
    [Google Scholar]
  5. Miller D.D. Brown E.W. Artificial intelligence in medical practice: The question to the answer? Am. J. Med. 2018 131 2 129 133 10.1016/j.amjmed.2017.10.035 29126825
    [Google Scholar]
  6. Ahmed Z. Mohamed K. Zeeshan S. Dong X. Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database (Oxford) 2020 2020 baaa010 10.1093/database/baaa010 32185396
    [Google Scholar]
  7. CFR Part 11 - Electronic records: Electronic signatures. 2024 Available from: https://www.ecfr.gov/current/title-21/chapter-I/subchapter-A/part-11
  8. FDA General principles of software validation; Final guidance for industry and FDA Staff. 2002 Available from: www.fda.gov/MedicalDevices/
  9. Harrer S. Shah P. Antony B. Hu J. Artificial intelligence for clinical trial design. Trends Pharmacol. Sci. 2019 40 8 577 591 10.1016/j.tips.2019.05.005 31326235
    [Google Scholar]
  10. Delso G. Cirillo D. Kaggie J.D. Valencia A. Metser U. Veit-Haibach P. How to design AI-driven clinical trials in nuclear medicine. Semin. Nucl. Med. 2021 51 2 112 119 10.1053/j.semnuclmed.2020.09.003 33509367
    [Google Scholar]
  11. Askin S. Burkhalter D. Calado G. El Dakrouni S. Artificial intelligence applied to clinical trials: Opportunities and challenges. Health Technol. (Berl.) 2023 13 2 203 213 10.1007/s12553‑023‑00738‑2 36923325
    [Google Scholar]
  12. Davidson R. eClinical trials: Planning and implementation. 2003 https://www.appliedclinicaltrialsonline.com/view/eclinical-trials-planning-implementation
  13. McFadden E.T. LoPresti F. Bailey L.R. Clarke E. Wilkins P.C. Approaches to data management. Control. Clin. Trials 1995 16 2 Suppl. 30 65 10.1016/0197‑2456(94)00093‑I 7789142
    [Google Scholar]
  14. Kiuchi T. Kaihara S. Automated generation of a World Wide Web-based data entry and check program for medical applications. Comput. Methods Programs Biomed. 1997 52 2 129 138 10.1016/S0169‑2607(96)01793‑2 9034677
    [Google Scholar]
  15. Litchfield J. Freeman J. Schou H. Elsley M. Fuller R. Chubb B. Is the future for clinical trials internet-based? A cluster randomized clinical trial. Clin. Trials 2005 2 1 72 79 10.1191/1740774505cn069oa 16279581
    [Google Scholar]
  16. Comparison of electronic data capture with paper data collection –Is there really an advantage? 2003 Available from: https://www.dreamslab.it/media/docs/eclinica [1].pdf
  17. El Emam K. Jonker E. Sampson M. Krleža-Jerić K. Neisa A. The use of electronic data capture tools in clinical trials: Web-survey of 259 Canadian trials. J. Med. Internet Res. 2009 11 1 e8 10.2196/jmir.1120 19275984
    [Google Scholar]
  18. Cruz Rivera S. Liu X. Hughes S.E. Dunster H. Manna E. Denniston A.K. Calvert M.J. Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies. Lancet Digit. Health 2023 5 3 e168 e173 10.1016/S2589‑7500(22)00252‑7 36828609
    [Google Scholar]
  19. Calvert M. Kyte D. Price G. Valderas J.M. Hjollund N.H. Maximising the impact of patient reported outcome assessment for patients and society. BMJ 2019 364 k5267 10.1136/bmj.k5267 30679170
    [Google Scholar]
  20. Harris J. Randomization and trial supply management systems in clinical trials. 2022 Available from: https://clinicalpursuit.com/randomization-and-trial-supply-management-systems-in-clinical-trials/
  21. What is a Clinical Trial Management System (CTMS)? 2024 Available from: https://realtime-eclinical.com/2024/02/14/what -is-a-clinical-trial-management-system-ctms/
  22. What is a CTMS? The Clinical Trial Management Key? 2024 Available from: https://www.integrait.co/what-is-ctms-clinical-trial-management-system/
  23. khan B. Fatima H. Qureshi A. Kumar S. Hanan A. Hussain J. Abdullah S. Drawbacks of artificial intelligence and their potential solutions in the healthcare sector. Biomed. Mater. Devices 2023 1 2 731 738 10.1007/s44174‑023‑00063‑2 36785697
    [Google Scholar]
  24. Ji S. Gu Q. Weng H. Liu Q. Zhou P. Chen J. Li Z. Beyah R. Wang T. De-Health: All your online health information are belong to us. 2020 IEEE 36th International Conference on Data Engineering, ICDE 2020 Dallas, TX, USA, 20-24 April 2020, pp. 1609-1620 10.1109/ICDE48307.2020.00143
    [Google Scholar]
  25. Lubarsky B. Re-identification of “Anonymized” data. GEO. L. TECH. REV 2017 202 https://georgetownlawtechreview.org/wp-content/uploads/2017/04/Lubarsky-1-GEO.-L.-TECH.-REV.-202. pdf
    [Google Scholar]
  26. Baowaly M.K. Lin C.C. Liu C.L. Chen K.T. Synthesizing electronic health records using improved generative adversarial networks. J. Am. Med. Inform. Assoc. 2019 26 3 228 241 10.1093/jamia/ocy142 30535151
    [Google Scholar]
  27. Hamid S. The opportunities and risks of artificial intelligence in medicine and healthcare. Communications. 2018 10.17863/CAM.25624
    [Google Scholar]
  28. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. 2018 Available from: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye
  29. Dentons - Regulating artificial intelligence in the EU: top 10 issues for businesses to consider. 2021 Available from: https://www.dentons.com/en/insights/articles/2021/june/28/regulating-artificial-intelligence-in-the-eu-top-10-issues-for-businesses-to-consider
  30. Neill D.B. Using artificial intelligence to improve hospital inpatient care. IEEE Intell. Syst. 2013 28 2 92 95 10.1109/MIS.2013.51
    [Google Scholar]
  31. Fernandes M. Vieira S.M. Leite F. Palos C. Finkelstein S. Sousa J.M.C. Clinical decision support systems for triage in the emergency department using intelligent systems: A review. Artif. Intell. Med. 2020 102 101762 10.1016/j.artmed.2019.101762 31980099
    [Google Scholar]
  32. Gama F. Tyskbo D. Nygren J. Barlow J. Reed J. Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: Scoping review. J. Med. Internet Res. 2022 24 1 e32215 10.2196/32215 35084349
    [Google Scholar]
  33. Wolff J. Pauling J. Keck A. Baumbach J. Systematic review of economic impact studies of artificial intelligence in health care. J. Med. Internet Res. 2020 22 2 e16866 10.2196/16866 32130134
    [Google Scholar]
  34. Schmidt-Erfurth U. Bogunovic H. Sadeghipour A. Schlegl T. Langs G. Gerendas B.S. Osborne A. Waldstein S.M. Machine learning to analyze the prognostic value of current imaging biomarkers in neovascular age-related macular degeneration. Ophthalmol. Retina 2018 2 1 24 30 10.1016/j.oret.2017.03.015 31047298
    [Google Scholar]
  35. Lee S.I. Celik S. Logsdon B.A. Lundberg S.M. Martins T.J. Oehler V.G. Estey E.H. Miller C.P. Chien S. Dai J. Saxena A. Blau C.A. Becker P.S. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia. Nat. Commun. 2018 9 1 42 10.1038/s41467‑017‑02465‑5 29298978
    [Google Scholar]
  36. Sordo M. Introduction to neural networks in healthcare. Open Clin 2002 https://www.researchgate.net/citation/228820949_Introduction_to_neural_networks_in_healthcare
    [Google Scholar]
  37. Reed J.E. Howe C. Doyle C. Bell D. Simple rules for evidence translation in complex systems: A qualitative study. BMC Med. 2018 16 1 92 10.1186/s12916‑018‑1076‑9 29921274
    [Google Scholar]
  38. Alami H. Lehoux P. Denis J.L. Motulsky A. Petitgand C. Savoldelli M. Rouquet R. Gagnon M.P. Roy D. Fortin J.P. Organizational readiness for artificial intelligence in health care: Insights for decision-making and practice. J. Health Organ. Manag. 2020 35 1 106 114 10.1108/JHOM‑03‑2020‑0074 33258359
    [Google Scholar]
  39. Capes N. Patel H. Sarhan I. Ashwood N. Dekker A. Shehata R. Artificial intelligence: A pragmatic approach to implementation in medicine, a review of the literature and a survey of local practice in midlands in UK. Int. J. Intell. Sci. 2023 13 3 63 79 10.4236/ijis.2023.133005
    [Google Scholar]
  40. Díaz Ó. Dalton J.A.R. Giraldo J. Artificial intelligence: A novel approach for drug discovery. Trends Pharmacol. Sci. 2019 40 8 550 551 10.1016/j.tips.2019.06.005 31279568
    [Google Scholar]
  41. Denti L. Hemlin S. Leadership and innovation in organizations: A systematic review of factors that mediate or moderate the relationship. Int. J. Innov. Manage. 2012 16 3 1240007 10.1142/S1363919612400075
    [Google Scholar]
  42. Damschroder L.J. Aron D.C. Keith R.E. Kirsh S.R. Alexander J.A. Lowery J.C. Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implement. Sci. 2009 4 1 50 10.1186/1748‑5908‑4‑50 19664226
    [Google Scholar]
  43. Davenport T. Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc. J. 2019 6 2 94 98 10.7861/futurehosp.6‑2‑94 31363513
    [Google Scholar]
  44. Hagendorff T. The ethics of AI ethics: An evaluation of guidelines. Minds Mach. 2020 30 1 99 120 10.1007/s11023‑020‑09517‑8
    [Google Scholar]
  45. Schönberger D. Artificial intelligence in healthcare: A critical analysis of the legal and ethical implications. Int. J. Law Inf. Technol. 2019 27 2 171 203 10.1093/ijlit/eaz004
    [Google Scholar]
  46. Anderson M. Anderson S.L. How should AI be developed, validated, and implemented in patient care? AMA J. Ethics 2019 21 2 E125 E130 10.1001/amajethics.2019.125 30794121
    [Google Scholar]
  47. Cath C. Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philos. Trans.- Royal Soc., Math. Phys. Eng. Sci. 2018 376 2133 20180080 10.1098/rsta.2018.0080 30322996
    [Google Scholar]
  48. Rigby M.J. Ethical dimensions of using artificial intelligence in health care. AMA J. Ethics 2019 21 2 E121 E124 10.1001/amajethics.2019.121
    [Google Scholar]
  49. Thierer A.D. Castillo A. Russell R. Artificial intelligence and public policy. SSRN 2017 10.2139/ssrn.3021135
    [Google Scholar]
  50. Guidance for Industry - Computerized systems used in clinical trials. 1999 Available from: https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/fda-bioresearch-monitoring-information/guidance-industry-computerized-systems-used-clinical-trials
  51. Electronic source data in clinical investigations. 2024 Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/electronic-source-data-clinical-investigations
  52. Use of electronic health record data in clinical investigations guidance for industry. 2021 Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-electronic-health-record-data-clinical-investigations-guidance-industry
  53. Clinical decision support software. 2024 Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software
  54. FDA. U.S. Food and Drug Administration Use of electronic informed consent in clinical investigations – Questions and Answers. 2024 Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-electronic-informed-consent-clinical-investigations-questions-and-answers
  55. Kandi V. Vadakedath S. Clinical trials and clinical research: A comprehensive review. Cureus 2023 15 2 e35077 10.7759/cureus.35077 36938261
    [Google Scholar]
  56. El-Hagrassy M.M. Duarte D. Thibaut A. Lucena M.F.G. Fregni F. Principles of designing a clinical trial: Optimizing chances of trial success. Curr. Behav. Neurosci. Rep. 2018 5 2 143 152 10.1007/s40473‑018‑0152‑y 30467533
    [Google Scholar]
  57. Mayorga-Ruiz   I.     Jiménez-Pastor   A.   Fos-Guarinos   B. López-González R. García-Castro F. Alberich-Bayarri Á. The role of AI in clinical trials. Artificial Intelligence in Medical Imaging. Springer International Publishing 2019 231 243 10.1007/978‑3‑319‑94878‑2_16
    [Google Scholar]
  58. Shin H.C. Roth H.R. Gao M. Lu L. Xu Z. Nogues I. Yao J. Mollura D. Summers R.M. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 2016 35 5 1285 1298 10.1109/TMI.2016.2528162 26886976
    [Google Scholar]
  59. Majie A. Artificial intelligence powered. ScienceOpen Posters 2021 10.14293/S2199‑1006.1.SOR‑.PPSORGE.v1
    [Google Scholar]
  60. The importance of clinical trial transparency and FDA Oversight. 2024 Available from: https://www.fda.gov/news-events/fda-voices/importance-clinical-trial-transparency-and-fda-oversight
  61. FDA’s Role: Clinicaltrials.gov information. 2025 Available from: https://www.fda.gov/science-research/clinical-trials-and-human-subject-protection/fdas-role-clinicaltrialsgov-information
  62. Kapczynski A. Kim J. Clinical trial transparency: The FDA should and can do more. J. Law Med. Ethics 2017 45 S2 33 38 10.1177/1073110517750618
    [Google Scholar]
  63. Wiljer D. Hakim Z. Developing an artificial intelligence–enabled health care practice: Rewiring health care professions for better care. J. Med. Imaging Radiat. Sci. 2019 50 4 Suppl. 2 S8 S14 10.1016/j.jmir.2019.09.010 31791914
    [Google Scholar]
  64. Kang S.K. Lee C.I. Pandharipande P.V. Sanelli P.C. Recht M.P. Residents’ introduction to comparative effectiveness research and big data analytics. J. Am. Coll. Radiol. 2017 14 4 534 536 10.1016/j.jacr.2016.10.032 28139415
    [Google Scholar]
  65. McCoy L.G. Nagaraj S. Morgado F. Harish V. Das S. Celi L.A. What do medical students actually need to know about artificial intelligence? NPJ Digit. Med. 2020 3 1 86 10.1038/s41746‑020‑0294‑7 32577533
    [Google Scholar]
  66. Paranjape K. Schinkel M. Nannan Panday R. Car J. Nanayakkara P. Introducing artificial intelligence training in medical education. JMIR Med. Educ. 2019 5 2 e16048 10.2196/16048 31793895
    [Google Scholar]
  67. Hutson M. How AI is being used to accelerate clinical trials. Nature 2024 627 8003 S2 S5 10.1038/d41586‑024‑00753‑x 38480968
    [Google Scholar]
  68. Weissler E.H. Naumann T. Andersson T. Ranganath R. nlmo O. Luo Y. Freitag D.F. Benoit J. Hughes M.C. Khan F. Slater P. Shameer K. Roe M. Hutchison E. Kollins S.H. Broedl U. Meng Z. Wong J.L. Curtis L. Huang E. Ghassemi M. The role of machine learning in clinical research: transforming the future of evidence generation. Trials 2021 22 1 537 10.1186/s13063‑021‑05489‑x 34399832
    [Google Scholar]
  69. FDA. U.S. Food and Drug Administration The role of artificial intelligence in clinical trial design and research with Dr. ElZarrad. 2024 Available from: https://www.fda.gov/drugs/news-events-human-drugs/role-artificial-intelligence-clinical-trial-design-and-research-dr-elzarrad
  70. Wadhwani P. AI in Clinical Trials Market Size - By Component (Software, Service), By technology (Machine Learning (ML), Natural Language Processing (NLP), Computer Vision, Contextual Bots), by application, by end user and forecast, 2024 – 2032. 2024 Available from: https://www.gminsights.com/industry-analysis/ai-in-clinical-trials-market
  71. Research X. Unlocking efficiency in clinical trials: The evolving landscape of RTSM (Randomization and Trial Supply Management). 2024 Available from: https://xceneinnovate.com/the-evolving-landscape-of-rtsm/
  72. Arnera V. Why paper diaries should be banned in clinical trials. 2009 Available from: https://www.slideshare.net/slideshow/why-paper-diaries-should-be-banned-in-clinical-trials/1532165
  73. José N.C. Langel K. ePRO vs. Paper. EBSCOhost. 2010 Available from: https://openurl.ebsco.com/EPDB%3Agcd%3A7%3A770 6503/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3 Agcd%3A51692354&crl=c&link_origin=scholar.google.com
  74. Hufford M.R. Stone A.A. Shiffman S. Schwartz J.E. Broderick J.E. Paper vs. Electronic diaries. 2002 Available from: https://openurl.ebsco.com/EPDB%3Agcd%3A2%3A7704482/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A7163356&crl=c&link_origin=scholar.google.com
  75. Stone A.A. Shiffman S. Schwartz J.E. Broderick J.E. Hufford M.R. Patient non-compliance with paper diaries. BMJ 2002 324 7347 1193 1194 10.1136/bmj.324.7347.1193 12016186
    [Google Scholar]
  76. Clinical trials compliance triples with electronic diaries. 2020 Available from: https://www.appliedclinicaltrialsonline.com/view/clinical-trials-compliance-triples-electronic-diaries
  77. Patient-reported outcome measures: Use in medical product development to support labeling claims. 2024 Available from: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/patient-reported-outcome-measures-use-medical-product-development-support-labeling-claims
  78. Regulatory guidance for the use of health-related quality of life (HRQL) measures in the evaluation of medicinal products - Scientific guideline. 2025 Available from: https://www.ema.europa.eu/en/regulatory-guidance-use-health-related-quality-life-hrql-measures-evaluation-medicinal-products-scientific-guideline
  79. Dickson M. Gagnon J.P The cost of new drug discovery and development. Discov Med. 2009 4 22 172 10.1002/mde.1360
    [Google Scholar]
  80. DiMasi J.A. Grabowski H.G. The cost of biopharmaceutical R&D: is biotech different? MDE. Manage. Decis. Econ. 2007 28 4-5 469 479 20704981
    [Google Scholar]
  81. CVS Prescription drug spending in the U.S. Health Care System. 2020 Available from: https://www.phrma.org/-/media/Project/PhRMA/PhRMA-Org/PhRMA-Org/PDF/P-R/PhRMA_LTAC_PrescriptionMedicineSpending.pdf
  82. Fultinavičiūtė U. AI benefits in patient identification and clinical trial recruitment has challenges in sight. 2022 Available from: https://www.clinicaltrialsarena.com/features/ai-clinical-trial-recruitment/
  83. Solutions M. AI-powered solutions: Enhancing clinical trial data. Medidata Solutions. 2024 Available from: https://www.facebook.com/MedidataSolutions/ https://www.medidata.com/en/life-science-resources/medidata-blog/ai-powered-solutions-enhancing-clinical-trial-data/
  84. The role of AI in optimizing clinical trials. 2025 Available from: https://www.hcltech.com/trends-and-insights/role-ai-optimizing-clinical-trials
  85. Youssef A. Nichol A.A. Martinez-Martin N. Larson D.B. Abramoff M. Wolf R.M. Char D. Ethical considerations in the design and conduct of clinical trials of artificial intelligence. JAMA Netw. Open 2024 7 9 e2432482 10.1001/jamanetworkopen.2024.32482 39240560
    [Google Scholar]
  86. 10 real-world examples of AI in healthcare. 2022 Available from: https://www.philips.com/a-w/about/news/archive/features/2022/20221124-10-real-world-examples-of-ai-in-healthcare.html
  87. Implementation A.I. AI implementation in healthcare: 10 challenges and solutions I Scalefocu. 2024 Available from: https://www.scalefocus.com/blog/ai-implementation-in-healthcare-10-challenges-and-solutions
  88. AI in Healthcare examples - 5 Powerful real-world use cases. 2023 Available from: https://www.xevensolutions.com/blog/ai -in-healthcare-examples-5-powerful-real-world-use-cases/
  89. Choi W.J. An J.K. Woo J.J. Kwak H.Y. Comparison of diagnostic performance in mammography assessment: Radiologist with reference to clinical information versus standalone artificial intelligence detection. Diagnostics 2022 13 1 117 10.3390/diagnostics13010117 36611409
    [Google Scholar]
  90. Zavaleta-Monestel E. Quesada-Villaseñor R. Arguedas-Chacón S. García-Montero J. Barrantes-López M. Salas-Segura J. Anchía-Alfaro A. Nieto-Bernal D. Diaz-Juan D.E. Revolutionizing healthcare: Qure.AI’s innovations in medical diagnosis and treatment. Cureus 2024 16 6 e61585 10.7759/cureus.61585 38962585
    [Google Scholar]
  91. Charow R. Jeyakumar T. Younus S. Dolatabadi E. Salhia M. Al-Mouaswas D. Anderson M. Balakumar S. Clare M. Dhalla A. Gillan C. Haghzare S. Jackson E. Lalani N. Mattson J. Peteanu W. Tripp T. Waldorf J. Williams S. Tavares W. Wiljer D. Artificial intelligence education programs for health care professionals: Scoping review. JMIR Med. Educ. 2021 7 4 e31043 10.2196/31043 34898458
    [Google Scholar]
  92. van Hartskamp M. Consoli S. Verhaegh W. Petkovic M. van de Stolpe A. Artificial intelligence in clinical health care applications: Viewpoint. Interact. J. Med. Res. 2019 8 2 e12100 10.2196/12100 30950806
    [Google Scholar]
  93. McShane L.M. Cavenagh M.M. Lively T.G. Eberhard D.A. Bigbee W.L. Williams P.M. Mesirov J.P. Polley M.Y.C. Kim K.Y. Tricoli J.V. Taylor J.M.G. Shuman D.J. Simon R.M. Doroshow J.H. Conley B.A. Criteria for the use of omics-based predictors in clinical trials: Explanation and elaboration. BMC Med. 2013 11 1 220 10.1186/1741‑7015‑11‑220 24228635
    [Google Scholar]
  94. He J. Baxter S.L. Xu J. Xu J. Zhou X. Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat. Med. 2019 25 1 30 36 10.1038/s41591‑018‑0307‑0 30617336
    [Google Scholar]
  95. Sussillo D. Barak O. Opening the black box: Low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput. 2013 25 3 626 649 10.1162/NECO_a_00409 23272922
    [Google Scholar]
  96. Zhu L. Ikeda K. Pang S. Ban T. Sarrafzadeh A. Merging weighted SVMs for parallel incremental learning. Neural Netw. 2018 100 25 38 10.1016/j.neunet.2018.01.001 29432992
    [Google Scholar]
  97. Lee T.T. Kesselheim A.S. U.S. Food and drug administration precertification pilot program for digital health software: Weighing the benefits and risks. Ann. Intern. Med. 2018 168 10 730 732 10.7326/M17‑2715 29632953
    [Google Scholar]
  98. Esteva A. Robicquet A. Ramsundar B. Kuleshov V. DePristo M. Chou K. Cui C. Corrado G. Thrun S. Dean J. A guide to deep learning in healthcare. Nat. Med. 2019 25 1 24 29 10.1038/s41591‑018‑0316‑z 30617335
    [Google Scholar]
  99. Ghahramani Z. Probabilistic machine learning and artificial intelligence. Nature 2015 521 7553 452 459 10.1038/nature14541 26017444
    [Google Scholar]
  100. LeCun Y. Bengio Y. Hinton G. Deep learning. Nature 2015 521 7553 436 444 10.1038/nature14539 26017442
    [Google Scholar]
  101. Jiang F. Jiang Y. Zhi H. Dong Y. Li H. Ma S. Wang Y. Dong Q. Shen H. Wang Y. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc. Neurol. 2017 2 4 230 243 10.1136/svn‑2017‑000101 29507784
    [Google Scholar]
  102. Zarringhalam K. Enayetallah A. Reddy P. Ziemek D. Robust clinical outcome prediction based on Bayesian analysis of transcriptional profiles and prior causal networks. Bioinformatics 2014 30 12 i69 i77 10.1093/bioinformatics/btu272 24932007
    [Google Scholar]
  103. Chopra H. Annu Shin D.K. Munjal K. Priyanka Dhama K. Emran T.B. Revolutionizing clinical trials: the role of ai in accelerating medical breakthroughs. Int. J. Surg. 2023 109 12 4211 4220 10.1097/JS9.0000000000000705 38259001
    [Google Scholar]
  104. Key challenges of AI implementation in healthcare. 2023 Available from: https://www.xevensolutions.com/blog/top-5-challenges-of-implementing-ai-in-healthcare/
  105. Luong K. Challenges of AI Integration in healthcare. Homepagexeven. 2024 Available from: https://www.ominext.com/en/blog/challenges-of-ai-integration-in-healthcare
  106. AI in healthcare: Real-world examples and applications. 2024 Available from: https://openloophealth.com/blog/real-world-examples-and-applications-of-ai-in-healthcare
  107. Taylor N.P. FDA creates transparency principles for AI in medical devices. 2024 Available from: https://www.medtechdive.com/news/fda-transparency-principles-machine-learning-enabled-medical-de/718961/
/content/journals/rrct/10.2174/0115748871359356250523033831
Loading
/content/journals/rrct/10.2174/0115748871359356250523033831
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test