The Promise Of Artificial Intelligence In Clinical Practice

Exploring the Promise and Challenges of Artificial Intelligence in Biomedical Research and Clinical Practice

Artificial intelligence (AI) is poised to revolutionize how science, and biomedical research in particular, are done. With AI, problem solving and complex tasks using massive data sets can be performed at a much higher rate and dimensionality level compared to humans. With the ability to handle huge data sets and self-learn, AI is already being exploited in drug design, drug repurposing, toxicology, and material identification. AI could also be used in both basic and clinical research in study design, defining outcomes, analyzing data, interpreting findings, and even identifying the most appropriate areas of investigation and funding sources. State-of-the-art AI-based large language models (LLM), such as ChatGPT and Perplexity, are positioned to change forever how science is communicated and how scientists interact with one another and their profession, including post-publication appraisal and critique. Like all revolutions, upheaval will follow and not all outcomes can be predicted, necessitating guardrails at the onset, especially to minimize the untoward impact of the many drawbacks of LLMs, which include lack of confidentiality, risk of hallucinations, and propagation of mainstream albeit potentially mistaken opinions and perspectives. In this review, we highlight areas of biomedical research that are already being reshaped by AI and how AI is likely to impact it further in the near future. We discuss the potential benefits of AI in biomedical research and address possible risks, some surrounding the creative process, that warrant further reflection.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

1. Suleimenov IE, Vitulyova YS, Bakirov AS, Gabrielyan OA. Artificial Intelligence:what is it? Proc 2020 6th Int Conf Comput Technol Appl. 2020;225. https://doi.org/10.1145/3397125.3397141.

2. Davenport T, Kalakota R. The potential for artificial intelligence in Healthcare. Future Healthc J. 2019;6(2):948. https://doi.org/10.7861/futurehosp.6-2-94.

   Article Google Scholar

3. Russell SJ. Artificial intelligence a modern approach. Pearson Education, Inc.; 2010.

4. McCorduck P, Cfe C. Machines who think: a personal inquiry into the history and prospects of Artificial Intelligence. AK Peters; 2004.

5. Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):25560. https://doi.org/10.1126/science.aaa8415.

   Article Google Scholar

6. VanLEHN K. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychol. 2011;46(4):197221. https://doi.org/10.1080/00461520.2011.611369.

   Article Google Scholar

7. Topol EJ. High-performance medicine: the convergence of human and Artificial Intelligence. Nat Med. 2019;25(1):4456. https://doi.org/10.1038/s41591-018-0300-7.

   Article Google Scholar

8. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):1158. https://doi.org/10.1038/nature21056.

   Article Google Scholar

9. Myszczynska MA, Ojamies PN, Lacoste AM, Neil D, Saffari A, Mead R, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative Diseases. Nat Reviews Neurol. 2020;16(8):44056. https://doi.org/10.1038/s41582-020-0377-8.

   Article Google Scholar

10. Ahsan MM, Luna SA, Siddique Z. Machine-learning-based disease diagnosis: a comprehensive review. Healthcare. 2022;10(3):541. https://doi.org/10.3390/healthcare10030541.

    Article Google Scholar

11. McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):8994. https://doi.org/10.1038/s41586-019-1799-6.

    Article Google Scholar

12. Kim H-E, Kim HH, Han B-K, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using Artificial Intelligence: a retrospective, Multireader Study. Lancet Digit Health. 2020;2(3). https://doi.org/10.1016/s2589-7500(20)30003-0.

13. Han SS, Park I, Eun Chang S, Lim W, Kim MS, Park GH, et al. Augmented Intelligence Dermatology: deep neural networks Empower Medical Professionals in diagnosing skin Cancer and Predicting Treatment Options for 134 skin Disorders. J Invest Dermatol. 2020;140(9):175361. https://doi.org/10.1016/j.jid.2020.01.019.

    Article Google Scholar

14. Haenssle HA, Fink C, Schneiderbauer R, Toberer F, Buhl T, Blum A, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018;29(8):183642. https://doi.org/10.1093/annonc/mdy166.

    Article Google Scholar

15. Li S, Zhao R, Zou H. Artificial intelligence for diabetic retinopathy. Chin Med J (Engl). 2021;135(3):25360. https://doi.org/10.1097/CM9.0000000000001816.

    Article Google Scholar

16. Alfaras M, Soriano MC, Ortn S. A fast machine learning model for ECG-based Heartbeat classification and arrhythmia detection. Front Phys. 2019;7. https://doi.org/10.3389/fphy.2019.00103.

17. Raghunath S, Pfeifer JM, Ulloa-Cerna AE, Nemani A, Carbonati T, Jing L, et al. Deep neural networks can predict new-onset atrial fibrillation from the 12-lead ECG and help identify those at risk of atrial fibrillationrelated stroke. Circulation. 2021;143(13):128798. https://doi.org/10.1161/circulationaha.120.047829.

    Article Google Scholar

18. Becker J, Decker JA, Rmmele C, Kahn M, Messmann H, Wehler M, et al. Artificial intelligence-based detection of pneumonia in chest radiographs. Diagnostics. 2022;12(6):1465. https://doi.org/10.3390/diagnostics12061465.

    Article Google Scholar

19. Mijwil MM, Aggarwal K. A diagnostic testing for people with appendicitis using machine learning techniques. Multimed Tools Appl. 2022;81(5):701123. https://doi.org/10.1007/s11042-022-11939-8.

    Article Google Scholar

20. Undru TR, Uday U, Lakshmi JT, et al. Integrating Artificial Intelligence for Clinical and Laboratory diagnosis - a review. Maedica (Bucur). 2022;17(2):4206. https://doi.org/10.26574/maedica.2022.17.2.420.

    Article Google Scholar

21. Peiffer-Smadja N, Dellire S, Rodriguez C, Birgand G, Lescure FX, Fourati S, et al. Machine learning in the clinical microbiology laboratory: has the time come for routine practice? Clin Microbiol Infect. 2020;26(10):13009. https://doi.org/10.1016/j.cmi.2020.02.006.

    Article Google Scholar

22. Smith KP, Kang AD, Kirby JE. Automated interpretation of Blood Culture Gram Stains by Use of a deep convolutional neural network. J Clin Microbiol. 2018;56(3):e0152117. https://doi.org/10.1128/JCM.01521-17.

    Article Google Scholar

23. Weis CV, Jutzeler CR, Borgwardt K. Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review. Clin Microbiol Infect. 2020;26(10):13107. https://doi.org/10.1016/j.cmi.2020.03.014.

    Article Google Scholar

24. Go T, Kim JH, Byeon H, Lee SJ. Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells. J Biophotonics. 2018;11(9):e201800101. https://doi.org/10.1002/jbio.201800101.

    Article Google Scholar

25. Smith KP, Kirby JE. Image analysis and artificial intelligence in infectious disease diagnostics. Clin Microbiol Infect. 2020;26(10):131823. https://doi.org/10.1016/j.cmi.2020.03.012.

    Article Google Scholar

26. Vandenberg O, Durand G, Hallin M, Diefenbach A, Gant V, Murray P, et al. Consolidation of clinical Microbiology Laboratories and introduction of Transformative Technologies. Clin Microbiol Rev. 2020;33(2). https://doi.org/10.1128/cmr.00057-19.

27. Panch T, Szolovits P, Atun R. Artificial Intelligence, Machine Learning and Health Systems. J Global Health. 2018;8(2). https://doi.org/10.7189/jogh.08.020303.

28. Berlyand Y, Raja AS, Dorner SC, Prabhakar AM, Sonis JD, Gottumukkala RV, et al. How artificial intelligence could transform emergency department operations. Am J Emerg Med. 2018;36(8):15157. https://doi.org/10.1016/j.ajem.2018.01.017.

    Article Google Scholar

29. Matheny ME, Whicher D, Thadaney Israni S. Artificial Intelligence in Health Care: a Report from the National Academy of Medicine. JAMA. 2020;323(6):50910. https://doi.org/10.1001/jama.2019.21579.

    Article Google Scholar

30. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):23043. https://doi.org/10.1136/svn-2017-000101.

    Article Google Scholar

31. Gandhi SO, Sabik L. Emergency department visit classification using the NYU algorithm. Am J Manag Care. 2014;20(4):31520.

    Google Scholar

32. Hautz WE, Kmmer JE, Hautz SC, Sauter TC, Zwaan L, Exadaktylos AK, et al. Diagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room. Scand J Trauma Resusc Emerg Med. 2019;27(1):54. https://doi.org/10.1186/s13049-019-0629-z.

    Article Google Scholar

33. Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023;388(13):12018. https://doi.org/10.1056/NEJMra2302038.

    Article Google Scholar

34. Abubaker Bagabir S, Ibrahim NK, Abubaker Bagabir H, Hashem Ateeq R. Covid-19 and Artificial Intelligence: genome sequencing, drug development and vaccine discovery. J Infect Public Health. 2022;15(2):28996. https://doi.org/10.1016/j.jiph.2022.01.011.

    Article Google Scholar

35. Pudjihartono N, Fadason T, Kempa-Liehr AW, OSullivan JM. A review of feature selection methods for machine learning-based Disease Risk Prediction. Front Bioinform. 2022;2:927312. https://doi.org/10.3389/fbinf.2022.927312. Published 2022 Jun 27.

    Article Google Scholar

36. Widen E, Raben TG, Lello L, Hsu SDH. Machine learning prediction of biomarkers from SNPs and of Disease risk from biomarkers in the UK Biobank. Genes (Basel). 2021;12(7):991. https://doi.org/10.3390/genes12070991. Published 2021 Jun 29.

    Article Google Scholar

37. Wang H, Avillach P. Diagnostic classification and prognostic prediction using common genetic variants in autism spectrum disorder: genotype-based Deep Learning. JMIR Med Inf. 2021;9(4). https://doi.org/10.2196/24754.

38. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci. 2001;98:1086974. https://doi.org/10.1073/pnas.191367098.

    Article Google Scholar

39. Yersal O. Biological subtypes of breast cancer: prognostic and therapeutic implications. World J Clin Oncol. 2014;5(3):41224. https://doi.org/10.5306/wjco.v5.i3.412.

    Article Google Scholar

40. eek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet. 2010;11:7339. https://doi.org/10.1038/nrg2825.

    Article Google Scholar

41. Blanco-Gonzlez A, Cabezn A, Seco-Gonzlez A, Conde-Torres D, Antelo-Riveiro P, Pieiro , et al. The role of AI in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals. 2023;16(6):891. https://doi.org/10.3390/ph16060891.

    Article Google Scholar

42. Tran TTV, Surya Wibowo A, Tayara H, Chong KT. Artificial Intelligence in Drug Toxicity Prediction: recent advances, Challenges, and future perspectives. J Chem Inf Model. 2023;63(9):262843. https://doi.org/10.1021/acs.jcim.3c00200.

    Article Google Scholar

43. Tran TTV, Tayara H, Chong KT. Artificial Intelligence in Drug Metabolism and Excretion Prediction: recent advances, Challenges, and future perspectives. Pharmaceutics. 2023;15(4):1260. https://doi.org/10.3390/pharmaceutics15041260.

    Article Google Scholar

44. Guedj M, Swindle J, Hamon A, Hubert S, Desvaux E, Laplume J, et al. Industrializing AI-powered drug discovery: Lessons learned from the patrimony computing platform. Expert Opin Drug Discov. 2022;17(8):81524. https://doi.org/10.1080/17460441.2022.2095368.

    Article Google Scholar

45. Ahmed F, Kang IS, Kim KH, Asif A, Rahim CS, Samantasinghar A, et al. Drug repurposing for viral cancers: a paradigm of machine learning, Deep Learning, and virtual screening-based approaches. J Med Virol. 2023;95(4). https://doi.org/10.1002/jmv.28693.

46. Singh DP, Kaushik B. A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques. Chem Biol Drug Des. 2023;101(1):17594. https://doi.org/10.1111/cbdd.14164.

    Article Google Scholar

47. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med Oncol. 2022;39(2):120. https://doi.org/10.1007/s12032-022-01711-1.

    Article Google Scholar

48. Subramanian M, Wojtusciszyn A, Favre L, Boughorbel S, Shan J, Letaief KB, et al. Precision medicine in the era of artificial intelligence: implications in chronic disease management. J Transl Med. 2020;18(1):472. https://doi.org/10.1186/s12967-020-02658-5.

    Article Google Scholar

49. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision Medicine, AI, and the future of Personalized Health Care. Clin Transl Sci. 2021;14(1):8693. https://doi.org/10.1111/cts.12884.

    Article Google Scholar

50. Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL, Ramirez AH, et al. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin Pharmacol Ther. 2012;92(1):8795. https://doi.org/10.1038/clpt.2011.371.

    Article Google Scholar

51. Huang C, Clayton EA, Matyunina LV, McDonald LD, Benigno BB, Vannberg F, et al. Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Sci Rep. 2018;8(1):16444. https://doi.org/10.1038/s41598-018-34753-5.

    Article Google Scholar

52. Sheu YH, Magdamo C, Miller M, Das S, Blacker D, Smoller JW. AI-assisted prediction of differential response to antidepressant classes using electronic health records. npj Digit Med. 2023;6:73. https://doi.org/10.1038/s41746-023-00817-8.

    Article Google Scholar

53. Martin GL, Jouganous J, Savidan R, Bellec A, Goehrs C, Benkebil M, et al. Validation of Artificial Intelligence to support the automatic coding of patient adverse drug reaction reports, using Nationwide Pharmacovigilance Data. Drug Saf. 2022;45(5):53548. https://doi.org/10.1007/s40264-022-01153-8.

    Article Google Scholar

54. Lee H, Kim HJ, Chang HW, Kim DJ, Mo J, Kim JE. Development of a system to support warfarin dose decisions using deep neural networks. Sci Rep. 2021;11(1):14745. Published 2021 Jul 20. https://doi.org/10.1038/s41598-021-94305-2.

55. Blasiak A, Truong A, Jeit W, Tan L, Kumar KS, Tan SB, et al. PRECISE CURATE.AI: a prospective feasibility trial to dynamically modulate personalized chemotherapy dose with artificial intelligence. J Clin Oncol. 2022;40(16suppl):15744. https://doi.org/10.1200/JCO.2022.40.16_suppl.1574.

    Article Google Scholar

56. Sjvall F, Lanckohr C, Bracht H. Whats new in therapeutic drug monitoring of antimicrobials? Intensive care Med. 2023 May 3. https://doi.org/10.1007/s00134-023-07060-5.

57. Partin A, Brettin TS, Zhu Y, Narykov O, Clyde A, Overbeek J, Stevens RL. Deep learning methods for drug response prediction in cancer: predominant and emerging trends. Front Med (Lausanne). 2023;10:1086097. https://doi.org/10.3389/fmed.2023.1086097.

    Article Google Scholar

58. Zhang H, Chen Y, Li F. Predicting Anticancer Drug Response with Deep Learning constrained by signaling pathways. Front Bioinform. 2021;1:639349. https://doi.org/10.3389/fbinf.2021.639349.

    Article Google Scholar

59. Han K, Cao P, Wang Y, Xie F, Ma J, Yu M, Wang J, Xu Y, Zhang Y, Wan J. A review of approaches for Predicting Drug-Drug interactions based on machine learning. Front Pharmacol. 2022;12:814858. https://doi.org/10.3389/fphar.2021.814858.

    Article Google Scholar

60. Liu JYH, Rudd JA. Predicting drug adverse effects using a new Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD). Sci Rep. 2023;13(1):6935. https://doi.org/10.1038/s41598-023-33655-5.

    Article Google Scholar

61. Nelson KM, Chang ET, Zulman DM, Rubenstein LV, Kirkland FD, Fihn SD. Using Predictive Analytics to Guide Patient Care and Research in a National Health System. J Gen Intern Med. 2019;34(8):137980. https://doi.org/10.1007/s11606-019-04961-4.

    Article Google Scholar

62. Amarasingham R, Patzer RE, Huesch M, Nguyen NQ, Xie B. Implementing electronic health care predictive analytics: considerations and challenges. Health Aff (Millwood). 2014;33(7):114854. https://doi.org/10.1377/hlthaff.2014.0352.

    Article Google Scholar

63. Ansari MS, Alok AK, Jain D, et al. Predictive model based on Health Data Analysis for Risk of Readmission in Disease-Specific cohorts. Perspect Health Inf Manag. 2021;18(Spring):1j. Published 2021 Mar 15.

    Google Scholar

64. Donz J, Aujesky D, Williams D, Schnipper JL. Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Intern Med. 2013;173:6328.

    Article Google Scholar

65. Predictive Analytics in Healthcare | Reveal. https://www.revealbi.io/blog/predictive-analytics-in-healthcare. Accessed 6.20.2023.

66. Alotaibi S, Mehmood R, Katib I, Rana O, Albeshri A, Sehaa. A Big Data Analytics Tool for Healthcare symptoms and Diseases Detection using Twitter, Apache Spark, and machine learning. Appl Sci. 2020;10:1398. https://doi.org/10.3390/app10041398.

    Article Google Scholar

67. Crossnohere NL, Elsaid M, Paskett J, Bose-Brill S, Bridges JFP. Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks. J Med Internet Res. 2022;24(8):e36823. https://doi.org/10.2196/36823.

    Article Google Scholar

68. Rivera SC, Liu X, Chan A, Denniston AK, Calvert MJ, SPIRIT-AICONSORT-AI. Working Group Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. BMJ. 2020;370:m3210. https://doi.org/10.1136/bmj.m3210.

    Article Google Scholar

69. Beam YuK, Kohane AL. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):71931. https://doi.org/10.1038/s41551-018-0305-z.

    Article Google Scholar

70. Vollmer S, Mateen BA, Bohner G, Kirly FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368:l6927. https://doi.org/10.1136/bmj.l6927.

    Article Google Scholar

71. Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008. https://doi.org/10.1136/bmjopen-2020-048008.

    Article Google Scholar

72. Liu Y, Chen PC, Krause J, Peng L. How to read articles that use machine learning: users guides to the medical literature. JAMA. 2019;322(18):180616. https://doi.org/10.1001/jama.2019.16489.2754798.

    Article Google Scholar

73. Artificial Intelligence and Machine Learning in Software as a Medical Device. US Food and Drug Administration. Released 2021. FDA website: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device. Accessed 6.20.2023.

74. White Paper on Artificial Intelligence, European Commission. A European approach to excellence and trust. 2020. Feb 19, https://ec.europa.eu/info/publications/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en. Accessed 6.20.2023.

75. Radanliev P, De Roure D. Disease X vaccine production and supply chains: risk assessing healthcare systems operating with artificial intelligence and industry 4.0. Health Technol (Berl). 2023;13(1):115. https://doi.org/10.1007/s12553-022-00722-2.

    Article Google Scholar

76. Regulatory Science Strategy to 2025. European Medicines Agency. Released 2020. https://www.ema.europa.eu/en/about-us/how-we-work/regulatory-science-strategy#regulatory-science-strategy-to-2025-section. Accessed 6.20.2023.

77. Dasta J. Application of artificial intelligence to pharmacy and medicine. Hosp Pharm. 1992;27(4):3125.

    Google Scholar

78. Pharma News Intelligence. Available from: https://pharmanewsintel.com/. Accessed 6.20.2023.

79. Chatbots. Medicine Delivery.; Available from: https://hellotars.com/chatbot-templates/healthcare/Hk8N4h/medicine-ordering-chatbot. Accessed 6.20.2023.

80. Li LR, Du B, Liu HQ, Chen C. Artificial Intelligence for Personalized Medicine in thyroid Cancer: current status and future perspectives. Front Oncol. 2021;10:604051. https://doi.org/10.3389/fonc.2020.604051.

    Article Google Scholar

81. Davoudi A, Malhotra KR, Shickel B, Siegel S, Williams S, Ruppert M et al. The intelligent ICU pilot study: using artificial intelligence technology for autonomous patient monitoring. https://doi.org/10.48550/arXiv.1804.10201.

82. Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018;68(668):1434. https://doi.org/10.3399/bjgp18X695213.

    Article Google Scholar

83. Curtis RG, Bartel B, Ferguson T, Blake HT, Northcott C, Virgara R, et al. Improving user experience of virtual Health Assistants: scoping review. J Med Internet Res. 2021;23(12):e31737. https://doi.org/10.2196/31737.

    Article Google Scholar

84. Ghosh PK, Jain P, Wankhede S, Preethi M, Kannan MK. Virtual nursing Assistant. J Geog Sci. 2021;8:27985. 20.18001.GSJ.2021.V8I3.21.36690.

    Google Scholar

85. Burgess M. The NHS is trialling an AI chatbot to answer your medical questions. Wired. 2017. Jan 5, http://www.wired.co.uk/article/babylon-nhs-chatbot-app. Accessed 20 June 2023.

86. Pavel Jik. Inspiring Applications of Digital Virtual Assistants in Healthcare. July 22., 2022. https://www.phonexia.com/blog/inspiring-applications-of-digital-virtual-assistants-in-healthcare/. Accessed 20 June 2023.

87. Kim JW, Jones KL, DAngelo E. How to prepare prospective psychiatrists in the era of Artificial Intelligence. Acad Psychiatry. 2019;43(3):3379. https://doi.org/10.1007/s40596-019-01025-x.

    Article Google Scholar

88. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, et al. Artificial Intelligence for Mental Health and Mental Illnesses: an overview. Curr Psychiatry Rep. 2019;21(11):116. https://doi.org/10.1007/s11920-019-1094-0.

    Article Google Scholar

89. Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (woebot): a randomized controlled trial. JMIR Mental Health. 2017;4(2):e19.

    Article Google Scholar

90. Williams AD, Andrews G. The effectiveness of internet cognitive behavioural therapy (iCBT) for depression in primary care: a quality assurance study. PLoS ONE. 2013;8(2):e57447.

    Article Google Scholar

91. Luxton DD. Artificial intelligence in psychological practice: current and future applications and implications. Prof Psychol Res Pract. 2014;45(5):3329. https://doi.org/10.1037/a0034559.

    Article Google Scholar

92. Prochaska J, Vogel E, Chieng A, Kendra M, Baiocchi M, Pajarito S, Robinson A. A therapeutic Relational Agent for reducing problematic substance use (woebot): Development and Usability Study. J Med Internet Res. 2021;23(3):e24850.

    Article Google Scholar

93. Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim HC, et al. Artificial Intelligence for Mental Health Care: clinical applications, barriers, facilitators, and Artificial Wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging. 2021;6(9):85664. https://doi.org/10.1016/j.bpsc.2021.02.001.

    Article Google Scholar

94. Artificial Intelligence in Healthcare. 39 Examples Improving the Future of Medicine. Emerj. https://emerj.com/ai-sector-overviews/artificial-intelligence-in-healthcare-39-examples-improving-the-future-of-medicine/. Published September 21, 2021. Accessed June 19, 2023.

95. Chew HSJ. The Use of Artificial Intelligence-Based conversational agents (Chatbots) for weight loss: scoping review and practical recommendations. JMIR Med Inform. 2022;10(4):e32578. https://doi.org/10.2196/32578.

    Article Google Scholar

96. Zhang J, Oh YJ, Lange P, Yu Z, Fukuoka Y. Artificial Intelligence Chatbot Behavior Change Model for Designing Artificial Intelligence Chatbots to promote physical activity and a healthy Diet: viewpoint. J Med Internet Res. 2020;22(9):e22845. https://doi.org/10.2196/22845.

    Article Google Scholar

97. Wang H, Zhang Z, Ip M, Lau J. T.F. Social mediabased conversational agents for health management and interventions. J Med Internet Res. 2018;20(8):e261. https://doi.org/10.2196/jmir.9275.

    Article Google Scholar

98. Bombard Y, Baker GR, Orlando E, Fancott C, Bhatia P, Casalino S, et al. Engaging patients to improve quality of care: a systematic review. Implement Sci. 2018;13(1):98. https://doi.org/10.1186/s13012-018-0784-z.

    Article Google Scholar

99. Wong CK, Yeung DY, Ho HC, Tse KP, Lam CY. Chinese older adults internet use for health information. J Appl Gerontol. 2014;33(3):31635. https://doi.org/10.1177/0733464812463430.

    Article Google Scholar

100. Aggarwal A, Tam CC, Wu D, Li X, Qiao S. Artificial Intelligence-Based chatbots for promoting health behavioral changes: systematic review. J Med Internet Res. 2023;25:e40789. https://doi.org/10.2196/40789.

     Article Google Scholar

101. Grtz M, Baumgrtner K, Schmid T, Muschko M, Woessner P, Gerlach A, et al. An artificial intelligence-based chatbot for prostate cancer education: design and patient evaluation study. Digit Health. 2023;9:20552076231173304. https://doi.org/10.1177/20552076231173304.

     Article Google Scholar

102. Nakhleh A, Spitzer S, Shehadeh N. ChatGPTs response to the diabetes knowledge questionnaire: implications for Diabetes Education. Diabetes Technol Ther. 2023 Apr;16. https://doi.org/10.1089/dia.2023.0134.

103. irchner GJ, Kim RY, Weddle JB, Bible JE. Can Artificial Intelligence improve the readability of Patient Education Materials? Clin Orthop Relat Res 2023 Apr 28. https://doi.org/10.1097/CORR.0000000000002668.

104. Lee D, Yoon SN. Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges. Int J Environ Res Public Health. 2021;18(1):271. https://doi.org/10.3390/ijerph18010271.

     Article Google Scholar

105. Kaptchuk TJ, Miller FG. Placebo Effects in Medicine. N Engl J Med. 2015;373(1):89. https://doi.org/10.1056/NEJMp1504023.

     Article Google Scholar

106. Lupton M. Some ethical and legal consequences of the application of artificial intelligence in the field of medicine. Trends in Medicine. 2018;18(4). https://doi.org/10.15761/tim.1000147.

107. Pezzo MV, Beckstead JW. Patients prefer artificial intelligence to a human provider, provided the AI is better than the human: A commentary on Longoni, Bonezzi and Morewedge (2019). Judgment and Decision Making. Cambridge University Press; 2020;15(3):4435. https://doi.org/10.1017/S1930297500007221.

108. How Americans View Use of AI in Health Care and Medical Research. https://www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care/. Accessed 19 June 2023.

109. Esmaeilzadeh P. Use of AI-based tools for healthcare purposes: a survey study from consumers perspectives. BMC Med Inform Decis Mak. 2020;20(1):170. Published 2020 Jul 22. https://doi.org/10.1186/s12911-020-01191-1.

110. Khullar D, Casalino LP, Qian Y, Lu Y, Krumholz HM, Aneja S. Perspectives of patients about Artificial Intelligence in Health Care. JAMA Netw Open. 2022;5(5):e2210309. https://doi.org/10.1001/jamanetworkopen.2022.10309. Published 2022 May 2.

     Article Google Scholar

111. Russo S, Jongerius C, Faccio F, et al. Understanding patients preferences: a systematic review of Psychological Instruments used in patients preference and decision studies. Value Health. 2019;22(4):491501. https://doi.org/10.1016/j.jval.2018.12.007.

     Article Google Scholar

112. Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021;3(9):e599e611. https://doi.org/10.1016/S2589-7500(21)00132-1.

     Article Google Scholar

113. West SM, Whittaker M, Crawford K. Discriminating Systems: gender, race and power in AI. AI Now Institute; 2019.

114. Maynez J, Narayan S, Bohnet B, McDonald R. On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020; https://doi.org/10.18653/v1/2020.acl-main.173.

115. deBurca S. The learning health care organization. Int J Qual Health Care. 2000;12(6):4578. https://doi.org/10.1093/intqhc/12.6.457.

     Article Google Scholar

116. IOM (Institute of Medicine). Measuring the impact of Interprofessional Education on collaborative practice and patient outcomes. Washington, DC: The National Academies Press; 2015. p. 182.

     Google Scholar

117. Alqahtani T, Badreldin HA, Alrashed M, Alshaya AI, Alghamdi SS, bin Saleh K, et al. The emergent role of Artificial Intelligence, natural learning processing, and large language models in higher education and research. Res Social Administrative Pharm. 2023. https://doi.org/10.1016/j.sapharm.2023.05.016.

     Article Google Scholar

118. Pinto dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R, et al. Medical students attitude towards Artificial Intelligence: a multicentre survey. Eur Radiol. 2018;29(4):16406. https://doi.org/10.1007/s00330-018-5601-1.

     Article Google Scholar

119. Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif Intell Healthc. 2020;295336. https://doi.org/10.1016/b978-0-12-818438-7.00012-5.

120. Cohen IG, Mello MM. HIPAA and protecting health information in the 21st Century. JAMA. 2018;320(3):231. https://doi.org/10.1001/jama.2018.5630.

     Article Google Scholar

121. Yuan B, Li J. The policy effect of the General Data Protection Regulation (GDPR) on the digital public health sector in the European Union: an empirical investigation. Int J Environ Res Public Health. 2019;16(6):1070. https://doi.org/10.3390/ijerph16061070.

     Article Google Scholar

122. Abdel-Hameed Al-Mistarehi M, Mijwil M. ; Youssef Filali; Mariem Bounabi; Guma Ali; Mostafa Abotaleb. Artificial Intelligence Solutions for Health 4.0: Overcoming Challenges and Surveying Applications. MJAIH 2023, 2023, 1520.

123. Radanliev P, De Roure D. Epistemological and bibliometric analysis of Ethics and Shared responsibilityhealth policy and IoT Systems. Sustainability. 2021;13(15):8355.

     Article Google Scholar

124. Radanliev P, De Roure D, Ani U, Carvalho G. The ethics of shared Covid-19 risks: an epistemological framework for ethical health technology assessment of risk in vaccine supply chain infrastructures. Health Technol (Berl). 2021;11(5):108391.

     Article Google Scholar