Revolutionizing healthcare: the role of artificial intelligence in clinical practice | BMC Medical Education
Suleimenov IE, Vitulyova YS, Bakirov AS, Gabrielyan OA. Artificial Intelligence:what is it? Proc 2020 6th Int Conf Comput Technol Appl. 2020;22–5. https://doi.org/10.1145/3397125.3397141.
Davenport T, Kalakota R. The potential for artificial intelligence in Healthcare. Future Healthc J. 2019;6(2):94–8. https://doi.org/10.7861/futurehosp.6-2-94.
Google Scholar
Russell SJ. Artificial intelligence a modern approach. Pearson Education, Inc.; 2010.
McCorduck P, Cfe C. Machines who think: a personal inquiry into the history and prospects of Artificial Intelligence. AK Peters; 2004.
Jordan MI, Mitchell TM. Machine learning: Trends, perspectives, and prospects. Science. 2015;349(6245):255–60. https://doi.org/10.1126/science.aaa8415.
Google Scholar
VanLEHN K. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychol. 2011;46(4):197–221. https://doi.org/10.1080/00461520.2011.611369.
Google Scholar
Topol EJ. High-performance medicine: the convergence of human and Artificial Intelligence. Nat Med. 2019;25(1):44–56. https://doi.org/10.1038/s41591-018-0300-7.
Google Scholar
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):115–8. https://doi.org/10.1038/nature21056.
Google Scholar
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):440–56. https://doi.org/10.1038/s41582-020-0377-8.
Google Scholar
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.
Google Scholar
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):89–94. https://doi.org/10.1038/s41586-019-1799-6.
Google Scholar
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.
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):1753–61. https://doi.org/10.1016/j.jid.2020.01.019.
Google Scholar
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):1836–42. https://doi.org/10.1093/annonc/mdy166.
Google Scholar
Li S, Zhao R, Zou H. Artificial intelligence for diabetic retinopathy. Chin Med J (Engl). 2021;135(3):253–60. https://doi.org/10.1097/CM9.0000000000001816.
Google Scholar
Alfaras M, Soriano MC, Ortín 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.
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 fibrillation–related stroke. Circulation. 2021;143(13):1287–98. https://doi.org/10.1161/circulationaha.120.047829.
Google Scholar
Becker J, Decker JA, Römmele 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.
Google Scholar
Mijwil MM, Aggarwal K. A diagnostic testing for people with appendicitis using machine learning techniques. Multimed Tools Appl. 2022;81(5):7011–23. https://doi.org/10.1007/s11042-022-11939-8.
Google Scholar
Undru TR, Uday U, Lakshmi JT, et al. Integrating Artificial Intelligence for Clinical and Laboratory diagnosis – a review. Maedica (Bucur). 2022;17(2):420–6. https://doi.org/10.26574/maedica.2022.17.2.420.
Google Scholar
Peiffer-Smadja N, Dellière 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):1300–9. https://doi.org/10.1016/j.cmi.2020.02.006.
Google Scholar
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):e01521–17. https://doi.org/10.1128/JCM.01521-17.
Google Scholar
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):1310–7. https://doi.org/10.1016/j.cmi.2020.03.014.
Google Scholar
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.
Google Scholar
Smith KP, Kirby JE. Image analysis and artificial intelligence in infectious disease diagnostics. Clin Microbiol Infect. 2020;26(10):1318–23. https://doi.org/10.1016/j.cmi.2020.03.012.
Google Scholar
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.
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.
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):1515–7. https://doi.org/10.1016/j.ajem.2018.01.017.
Google Scholar
Matheny ME, Whicher D, Thadaney Israni S. Artificial Intelligence in Health Care: a Report from the National Academy of Medicine. JAMA. 2020;323(6):509–10. https://doi.org/10.1001/jama.2019.21579.
Google Scholar
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):230–43. https://doi.org/10.1136/svn-2017-000101.
Google Scholar
Gandhi SO, Sabik L. Emergency department visit classification using the NYU algorithm. Am J Manag Care. 2014;20(4):315–20.
Hautz WE, Kämmer 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.
Google Scholar
Haug CJ, Drazen JM. Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. N Engl J Med. 2023;388(13):1201–8. https://doi.org/10.1056/NEJMra2302038.
Google Scholar
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):289–96. https://doi.org/10.1016/j.jiph.2022.01.011.
Google Scholar
Pudjihartono N, Fadason T, Kempa-Liehr AW, O’Sullivan JM. A review of feature selection methods for machine learning-based Disease Risk Prediction. Front Bioinform. 2022;2:927312. Published 2022 Jun 27.
Google Scholar
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. Published 2021 Jun 29.
Google Scholar
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.
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;98:10869–74. https://doi.org/10.1073/pnas.191367098.
Google Scholar
Yersal O. Biological subtypes of breast cancer: prognostic and therapeutic implications. World J Clin Oncol. 2014;5(3):412–24. https://doi.org/10.5306/wjco.v5.i3.412.
Google Scholar
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;11:733–9. https://doi.org/10.1038/nrg2825.
Google Scholar
Blanco-González A, Cabezón A, Seco-González A, Conde-Torres D, Antelo-Riveiro P, Piñeiro Á, et al. The role of AI in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals. 2023;16(6):891. https://doi.org/10.3390/ph16060891.
Google Scholar
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):2628–43. https://doi.org/10.1021/acs.jcim.3c00200.
Google Scholar
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.
Google Scholar
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):815–24. https://doi.org/10.1080/17460441.2022.2095368.
Google Scholar
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.
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):175–94. https://doi.org/10.1111/cbdd.14164.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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):86–93. https://doi.org/10.1111/cts.12884.
Google Scholar
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):87–95. https://doi.org/10.1038/clpt.2011.371.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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):535–48. https://doi.org/10.1007/s40264-022-01153-8.
Google Scholar
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.
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):1574–4. https://doi.org/10.1200/JCO.2022.40.16_suppl.1574.
Google Scholar
Sjövall F, Lanckohr C, Bracht H. What’s new in therapeutic drug monitoring of antimicrobials? Intensive care Med. 2023 May 3. https://doi.org/10.1007/s00134-023-07060-5.
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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):1379–80. https://doi.org/10.1007/s11606-019-04961-4.
Google Scholar
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):1148–54. https://doi.org/10.1377/hlthaff.2014.0352.
Google Scholar
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.
Donzé 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;173:632–8.
Google Scholar
Predictive Analytics in Healthcare | Reveal. Accessed 6.20.2023.
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Beam YuK, Kohane AL. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2(10):719–31. https://doi.org/10.1038/s41551-018-0305-z.
Google Scholar
Vollmer S, Mateen BA, Bohner G, Király 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.
Google Scholar
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.
Google Scholar
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):1806–16. https://doi.org/10.1001/jama.2019.16489.2754798.
Google Scholar
Artificial Intelligence and Machine Learning in Software as a Medical Device. US Food and Drug Administration. Released 2021. FDA website: Accessed 6.20.2023.
White Paper on Artificial Intelligence, European Commission. A European approach to excellence and trust. 2020. Feb 19, Accessed 6.20.2023.
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):11–5. https://doi.org/10.1007/s12553-022-00722-2.
Google Scholar
Regulatory Science Strategy to 2025. European Medicines Agency. Released 2020. Accessed 6.20.2023.
Dasta J. Application of artificial intelligence to pharmacy and medicine. Hosp Pharm. 1992;27(4):312–5.
Pharma News Intelligence. Available from: Accessed 6.20.2023.
Chatbots. Medicine Delivery.; Available from: Accessed 6.20.2023.
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.
Google Scholar
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.
Buch VH, Ahmed I, Maruthappu M. Artificial intelligence in medicine: current trends and future possibilities. Br J Gen Pract. 2018;68(668):143–4. https://doi.org/10.3399/bjgp18X695213.
Google Scholar
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.
Google Scholar
Ghosh PK, Jain P, Wankhede S, Preethi M, Kannan MK. Virtual nursing Assistant. J Geog Sci. 2021;8:279–85. 20.18001.GSJ.2021.V8I3.21.36690.
Burgess M. The NHS is trialling an AI chatbot to answer your medical questions. Wired. 2017. Jan 5, Accessed 20 June 2023.
Pavel Jiřík. Inspiring Applications of Digital Virtual Assistants in Healthcare. July 22., 2022. Accessed 20 June 2023.
Kim JW, Jones KL, D’Angelo E. How to prepare prospective psychiatrists in the era of Artificial Intelligence. Acad Psychiatry. 2019;43(3):337–9. https://doi.org/10.1007/s40596-019-01025-x.
Google Scholar
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.
Google Scholar
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.
Google Scholar
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.
Google Scholar
Luxton DD. Artificial intelligence in psychological practice: current and future applications and implications. Prof Psychol Res Pract. 2014;45(5):332–9. https://doi.org/10.1037/a0034559.
Google Scholar
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.
Google Scholar
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):856–64. https://doi.org/10.1016/j.bpsc.2021.02.001.
Google Scholar
Artificial Intelligence in Healthcare. 39 Examples Improving the Future of Medicine. Emerj. Published September 21, 2021. Accessed June 19, 2023.
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.
Google Scholar
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.
Google Scholar
Wang H, Zhang Z, Ip M, Lau J. T.F. Social media–based conversational agents for health management and interventions. J Med Internet Res. 2018;20(8):e261. https://doi.org/10.2196/jmir.9275.
Google Scholar
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.
Google Scholar
Wong CK, Yeung DY, Ho HC, Tse KP, Lam CY. Chinese older adults’ internet use for health information. J Appl Gerontol. 2014;33(3):316–35. https://doi.org/10.1177/0733464812463430.
Google Scholar
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.
Google Scholar
Görtz M, Baumgärtner 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.
Google Scholar
Nakhleh A, Spitzer S, Shehadeh N. ChatGPT’s response to the diabetes knowledge questionnaire: implications for Diabetes Education. Diabetes Technol Ther. 2023 Apr;16. https://doi.org/10.1089/dia.2023.0134.
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.
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.
Google Scholar
Kaptchuk TJ, Miller FG. Placebo Effects in Medicine. N Engl J Med. 2015;373(1):8–9. https://doi.org/10.1056/NEJMp1504023.
Google Scholar
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.
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):443–5. https://doi.org/10.1017/S1930297500007221.
How Americans View Use of AI in Health Care and Medical Research. Accessed 19 June 2023.
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.
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. Published 2022 May 2.
Google Scholar
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):491–501. https://doi.org/10.1016/j.jval.2018.12.007.
Google Scholar
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):e599–e611. https://doi.org/10.1016/S2589-7500(21)00132-1.
Google Scholar
West SM, Whittaker M, Crawford K. Discriminating Systems: gender, race and power in AI. AI Now Institute; 2019.
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.
deBurca S. The learning health care organization. Int J Qual Health Care. 2000;12(6):457–8. https://doi.org/10.1093/intqhc/12.6.457.
Google Scholar
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.
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.
Google Scholar
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):1640–6. https://doi.org/10.1007/s00330-018-5601-1.
Google Scholar
Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. Artif Intell Healthc. 2020;295–336. https://doi.org/10.1016/b978-0-12-818438-7.00012-5.
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.
Google Scholar
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.
Google Scholar
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, 15–20.
Radanliev P, De Roure D. Epistemological and bibliometric analysis of Ethics and Shared responsibility—health policy and IoT Systems. Sustainability. 2021;13(15):8355.
Google Scholar
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):1083–91.
Google Scholar
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