Skip to content
2000
image of The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review

Abstract

Introduction

Healthcare organizations are complicated and demanding for all stakeholders, but artificial intelligence (AI) has revolutionized several sectors, especially healthcare, with the potential to enhance patient outcomes and standard of life. Quick advancements in AI can transform healthcare by implementing it into clinical procedures. Reporting AI's involvement in clinical settings is vital for its successful adoption by providing medical professionals with the necessary information and tools.

Background

This paper offers a thorough and up-to-date summary of the present condition of AI in medical settings, including its possible uses in patient interaction, treatment suggestions, and disease diagnosis. It also addresses the challenges and limitations, including the necessity for human expertise along with future directions. In doing so, it improves the understanding of AI's relevance in healthcare and supports medical institutions in successfully implementing AI technologies.

Methods

The structured literature review, with its dependable and reproducible research process, allowed the authors to acquire 337 peer-reviewed publications from indexing databases, such as Scopus and EMBASE, without any time restrictions. The researchers utilized both qualitative and quantitative factors to assess authors, publications, keywords, and collaboration networks.

Results

AI implementation in healthcare holds enormous potential for enhancing patient outcomes, treatment recommendations, and disease diagnosis. AI technologies can use massive datasets and recognize patterns to beat human performance in various healthcare domains. AI provides improved accuracy, reduced expenses, and time savings. It can transform customized medicine, optimize drug dosages, improve management of population health, set guidelines, offer digital medical assistants, promote mental health services, boost patient knowledge, and maintain patient-clinician trust.

Conclusion

AI can be utilized to detect diseases, develop customized therapy plans, and support medical professionals with their clinical decision-making. Instead of just automating jobs, AI focuses on creating technologies that can improve patient care in several healthcare settings. However, challenges such as biasness, data confidentiality, and data quality must be resolved for the appropriate and successful integration of AI in healthcare.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ctmc/10.2174/0115680266339394250225112747
2025-03-03
2025-09-13

  • From This Site

    /content/journals/ctmc/10.2174/0115680266339394250225112747
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Suleimenov I.E. Artificial Intelligence: What is it? Proceedings of the 2020 6th International Conference on Computer and Technology Applications Antalya, Turkey Association for Computing Machinery 2020 22 25 10.1145/3397125.3397141
    [Google Scholar]
  2. Davenport T. Kalakota R. The potential for artificial intelligence in healthcare. Futur. Healthc. J. 2019 6 2 94 98 10.7861/futurehosp.6‑2‑94 31363513
    [Google Scholar]
  3. Russell S.J. Norvig P. Artificial intelligence: A modern approach. New York Pearson Education, Inc. 2016
    [Google Scholar]
  4. McCorduck P. Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence. Natick, Massachusetts AK Peters 1979
    [Google Scholar]
  5. Alowais S.A. Alghamdi S.S. Alsuhebany N. Alqahtani T. Alshaya A.I. Almohareb S.N. Aldairem A. Alrashed M. Saleh B.K. Badreldin H.A. Yami A.M.S. Harbi A.S. Albekairy A.M. Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med. Educ. 2023 23 1 689 10.1186/s12909‑023‑04698‑z 37740191
    [Google Scholar]
  6. Jordan M.I. Mitchell T.M. Machine learning: Trends, perspectives, and prospects. Science 2015 349 6245 255 260 10.1126/science.aaa8415 26185243
    [Google Scholar]
  7. VanLEHN K.U.R.T. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 2011 46 4 197 221 10.1080/00461520.2011.611369
    [Google Scholar]
  8. Topol E.J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 2019 25 1 44 56 10.1038/s41591‑018‑0300‑7 30617339
    [Google Scholar]
  9. Esteva A. Kuprel B. Novoa R.A. Ko J. Swetter S.M. Blau H.M. Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017 542 7639 115 118 10.1038/nature21056 28117445
    [Google Scholar]
  10. Liberati A. Altman D.G. Tetzlaff J. Mulrow C. Gøtzsche P.C. Ioannidis J.P.A. Clarke M. Devereaux P.J. Kleijnen J. Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 2009 339 jul21 1 b2700 10.1136/bmj.b2700 19622552
    [Google Scholar]
  11. Secinaro S. Calandra D. Secinaro A. Muthurangu V. Biancone P. The role of artificial intelligence in healthcare: A structured literature review. BMC Med. Inform. Decis. Mak. 2021 21 1 125 10.1186/s12911‑021‑01488‑9 33836752
    [Google Scholar]
  12. Elango B. Rajendran P. Authorship trends and collaboration pattern in the marine sciences literature: A scientometric study. Int. J. Inf. Dissem. Technol. 2012 2 1 166 169
    [Google Scholar]
  13. 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]
  14. Xu J. Yang P. Xue S. Sharma B. Sanchez-Martin M. Wang F. Beaty K.A. Dehan E. Parikh B. Translating cancer genomics into precision medicine with artificial intelligence: Applications, challenges and future perspectives. Hum. Genet. 2019 138 2 109 124 10.1007/s00439‑019‑01970‑5 30671672
    [Google Scholar]
  15. Chen M. Zhang B. Cai Z. Seery S. Gonzalez M.J. Ali N.M. Ren R. Qiao Y. Xue P. Jiang Y. Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey. Front. Med. 2022 9 990604 10.3389/fmed.2022.990604 36117979
    [Google Scholar]
  16. Rui A. Garabedian P.M. Marceau M. Syrowatka A. Volk L.A. Edrees H.H. Seger D.L. Amato M.G. Cambre J. Dulgarian S. Newmark L.P. Nanji K.C. Schultz P. Jackson G.P. Rozenblum R. Bates D.W. Performance of a web-based reference database with natural language searching capabilities: Usability evaluation of dynamed and micromedex with watson. JMIR Human Factors 2023 10 3 e43960 10.2196/43960 37067858
    [Google Scholar]
  17. Lim J.I. Rachitskaya A.V. Hallak J.A. Gholami S. Alam M.N. Artificial intelligence for retinal diseases. Asia Pac. J. Ophthalmol. 2024 13 4 100096 10.1016/j.apjo.2024.100096 39209215
    [Google Scholar]
  18. Ye J. Woods D. Jordan N. Starren J. The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support. AMIA Jt. Summits Transl. Sci. Proc. 2024 2024 459 467 38827061
    [Google Scholar]
  19. Baweja D. A comparative analysis of automation anywhere, uipath, and blueprism. 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) IEEE, 2023 pp. 1715-1718. 10.1109/ICACITE57410.2023.10182777
    [Google Scholar]
  20. Watanabe M. Kim B. Jeng A. St john sepsis alert correlation with management of infections and antimicrobial selection: A pilot study. Open Forum Infect. Dis. 2017 4 Suppl. 1 S513 S513 10.1093/ofid/ofx163.1333
    [Google Scholar]
  21. Lee L.I.T. Kanthasamy S. Ayyalaraju R.S. Ganatra R. The current state of artificial intelligence in medical imaging and nuclear medicine. BJR|Open 2019 1 1 20190037 10.1259/bjro.20190037 33178956
    [Google Scholar]
  22. Devlin T. Gao L. Collins O. Heath G.W. Figurelle M. Avila A. Boyd C. Ayub H. Sevilis T. VALIDATE—Utilization of the Viz.ai mobile stroke care coordination platform to limit delays in LVO stroke diagnosis and endovascular treatment. Frontiers in Stroke 2024 3 1381930 10.3389/fstro.2024.1381930
    [Google Scholar]
  23. Buchem v.M.M. Boosman H. Bauer M.P. Kant I.M.J. Cammel S.A. Steyerberg E.W. The digital scribe in clinical practice: A scoping review and research agenda. NPJ Digit. Med. 2021 4 1 57 10.1038/s41746‑021‑00432‑5 33772070
    [Google Scholar]
  24. Voter A.F. Larson M.E. Garrett J.W. Yu J.P.J. Diagnostic accuracy and failure mode analysis of a deep learning algorithm for the detection of cervical spine fractures. AJNR Am. J. Neuroradiol. 2021 42 8 1550 1556 10.3174/ajnr.A7179 34117018
    [Google Scholar]
  25. Shaver J. The state of telehealth before and after the covid-19 pandemic. Prim. Care 2022 49 4 517 530 10.1016/j.pop.2022.04.002 36357058
    [Google Scholar]
  26. Kinnings S.L. Liu N. Tonge P.J. Jackson R.M. Xie L. Bourne P.E. A machine learning-based method to improve docking scoring functions and its application to drug repurposing. J. Chem. Inf. Model. 2011 51 2 408 419 10.1021/ci100369f 21291174
    [Google Scholar]
  27. Varnek A. Baskin I. Machine learning methods for property prediction in chemoinformatics: Quo Vadis? J. Chem. Inf. Model. 2012 52 6 1413 1437 10.1021/ci200409x 22582859
    [Google Scholar]
  28. Ain Q.U. Aleksandrova A. Roessler F.D. Ballester P.J. Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2015 5 6 405 424 10.1002/wcms.1225 27110292
    [Google Scholar]
  29. Erickson B.J. Korfiatis P. Akkus Z. Kline T.L. Machine learning for medical imaging. Radiographics 2017 37 2 505 515 10.1148/rg.2017160130 28212054
    [Google Scholar]
  30. Zeng X. Luo G. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection. Health Inf. Sci. Syst. 2017 5 1 2 10.1007/s13755‑017‑0023‑z 29038732
    [Google Scholar]
  31. Li D. Madden A. Liu C. Ding Y. Qian L. Zhou E. Modelling online user behavior for medical knowledge learning. Ind. Manage. Data Syst. 2018 118 4 889 911 10.1108/IMDS‑07‑2017‑0309
    [Google Scholar]
  32. Wesolowski M. Suchacz B. Artificial neural networks: Theoretical background and pharmaceutical applications: A review. J. AOAC Int. 2012 95 3 652 668 10.5740/jaoacint.SGE_Wesolowski_ANN 22816255
    [Google Scholar]
  33. Saravanan K. Sasithra S. Review on classification based on artificial neural nteworks. J. Ambient Syst. Appl. 2014 2 4 11 18
    [Google Scholar]
  34. Pastur-Romay L. Cedrón F. Pazos A. Porto-Pazos A. Deep artificial neural networks and neuromorphic chips for big data analysis: Pharmaceutical and bioinformatics applications. Int. J. Mol. Sci. 2016 17 8 1313 10.3390/ijms17081313 27529225
    [Google Scholar]
  35. Li H. Zhang Z. Liu Z. Application of artificial neural networks for catalysis: A review. Catalysts 2017 7 10 306 10.3390/catal7100306
    [Google Scholar]
  36. Abiodun O.I. Jantan A. Omolara A.E. Dada K.V. Mohamed N.A. Arshad H. State-of-the-art in artificial neural network applications: A survey. Heliyon 2018 4 11 e00938 10.1016/j.heliyon.2018.e00938 30519653
    [Google Scholar]
  37. Shahid N. Rappon T. Berta W. Applications of artificial neural networks in health care organizational decision-making: A scoping review. PLoS One 2019 14 2 e0212356 10.1371/journal.pone.0212356 30779785
    [Google Scholar]
  38. Dutta S. Long W.J. Brown D.F.M. Reisner A.T. Automated detection using natural language processing of radiologists recommendations for additional imaging of incidental findings. Ann. Emerg. Med. 2013 62 2 162 169 10.1016/j.annemergmed.2013.02.001 23548405
    [Google Scholar]
  39. Heintzelman N.H. Taylor R.J. Simonsen L. Lustig R. Anderko D. Haythornthwaite J.A. Childs L.C. Bova G.S. Longitudinal analysis of pain in patients with metastatic prostate cancer using natural language processing of medical record text. J. Am. Med. Inform. Assoc. 2013 20 5 898 905 10.1136/amiajnl‑2012‑001076 23144336
    [Google Scholar]
  40. Cai T. Giannopoulos A.A. Yu S. Kelil T. Ripley B. Kumamaru K.K. Rybicki F.J. Mitsouras D. Natural language processing technologies in radiology research and clinical applications. Radiographics 2016 36 1 176 191 10.1148/rg.2016150080 26761536
    [Google Scholar]
  41. Savova G.K. Tseytlin E. Finan S. Castine M. Miller T. Medvedeva O. Harris D. Hochheiser H. Lin C. Chavan G. Jacobson R.S. DeepPhe: A natural language processing system for extracting cancer phenotypes from clinical records. Cancer Res. 2017 77 21 e115 e118 10.1158/0008‑5472.CAN‑17‑0615 29092954
    [Google Scholar]
  42. Verma R. Melcher U. A support vector machine based method to distinguish proteobacterial proteins from eukaryotic plant proteins. BMC Bioinformatics 2012 13 S15 Suppl. 15 S9 10.1186/1471‑2105‑13‑S15‑S9 23046503
    [Google Scholar]
  43. Zhu B. Chen H. Chen B. Xu Y. Zhang K. Support vector machine model for diagnosing pneumoconiosis based on wavelet texture features of digital chest radiographs. J. Digit. Imaging 2014 27 1 90 97 10.1007/s10278‑013‑9620‑9 23836078
    [Google Scholar]
  44. Gu X. Ni T. Wang H. New fuzzy support vector machine for the class imbalance problem in medical datasets classification. Scient. World J. 2014 2014 1 e536434 10.1155/2014/536434
    [Google Scholar]
  45. Retico A. Bosco P. Cerello P. Fiorina E. Chincarini A. Fantacci M.E. Predictive models based on support vector machines: Whole‐brain versus regional analysis of structural mri in the alzheimer’s disease. J. Neuroimag. 2015 25 4 552 563 10.1111/jon.12163 25291354
    [Google Scholar]
  46. Wang Z.L. Zhou Z.G. Chen Y. Li X.T. Sun Y.S. Support vector machines model of computed tomography for assessing lymph node metastasis in esophageal cancer with neoadjuvant chemotherapy. J. Comput. Assist. Tomogr. 2017 41 3 455 460 10.1097/RCT.0000000000000555 27879527
    [Google Scholar]
  47. Davies N. Manthorpe J. Sampson E.L. Iliffe S. After the Liverpool Care Pathway—development of heuristics to guide end of life care for people with dementia: Protocol of the ALCP study. BMJ Open. 2015 5 9 e008832 10.1136/bmjopen‑2015‑008832 26338688
    [Google Scholar]
  48. Davies N. Mathew R. Wilcock J. Manthorpe J. Sampson E.L. Lamahewa K. Iliffe S. A co-design process developing heuristics for practitioners providing end of life care for people with dementia. BMC Palliat. Care 2016 15 1 68 10.1186/s12904‑016‑0146‑z 27484683
    [Google Scholar]
  49. Mohan D. Rosengart M.R. Fischhoff B. Angus D.C. Farris C. Yealy D.M. Wallace D.J. Barnato A.E. Testing a videogame intervention to recalibrate physician heuristics in trauma triage: Study protocol for a randomized controlled trial. BMC Emerg. Med. 2016 16 1 44 10.1186/s12873‑016‑0108‑z 27835981
    [Google Scholar]
  50. Davies N. Manthorpe J. Sampson E.L. Lamahewa K. Wilcock J. Mathew R. Iliffe S. Guiding practitioners through end of life care for people with dementia: The use of heuristics. PLoS One 2018 13 11 e0206422 10.1371/journal.pone.0206422 30427873
    [Google Scholar]
  51. Patel V.L. Shortliffe E.H. Stefanelli M. Szolovits P. Berthold M.R. Bellazzi R. Abu-Hanna A. The coming of age of artificial intelligence in medicine. Artif. Intell. Med. 2009 46 1 5 17 10.1016/j.artmed.2008.07.017 18790621
    [Google Scholar]
  52. Wahl B. Cossy-Gantner A. Germann S. Schwalbe N.R. Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Glob. Health 2018 3 4 e000798 10.1136/bmjgh‑2018‑000798 30233828
    [Google Scholar]
  53. Basu K. Sinha R. Ong A. Basu T. Artificial intelligence: How is it changing medical sciences and its future? Indian J. Dermatol. 2020 65 5 365 370 10.4103/ijd.IJD_421_20 33165420
    [Google Scholar]
  54. Myszczynska M.A. Ojamies P.N. Lacoste A.M.B. Neil D. Saffari A. Mead R. Hautbergue G.M. Holbrook J.D. Ferraiuolo L. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat. Rev. Neurol. 2020 16 8 440 456 10.1038/s41582‑020‑0377‑8 32669685
    [Google Scholar]
  55. Ahsan M.M. Luna S.A. Siddique Z. Machine-learning-based disease diagnosis: A comprehensive review. Healthcare 2022 10 3 541 10.3390/healthcare10030541 35327018
    [Google Scholar]
  56. McKinney S.M. Sieniek M. Godbole V. Godwin J. Antropova N. Ashrafian H. Back T. Chesus M. Corrado G.S. Darzi A. Etemadi M. Garcia-Vicente F. Gilbert F.J. Halling-Brown M. Hassabis D. Jansen S. Karthikesalingam A. Kelly C.J. King D. Ledsam J.R. Melnick D. Mostofi H. Peng L. Reicher J.J. Romera-Paredes B. Sidebottom R. Suleyman M. Tse D. Young K.C. Fauw D.J. Shetty S. International evaluation of an AI system for breast cancer screening. Nature 2020 577 7788 89 94 10.1038/s41586‑019‑1799‑6 31894144
    [Google Scholar]
  57. Kim H.E. Kim H.H. Han B.K. Kim K.H. Han K. Nam H. Lee E.H. Kim E.K. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study. Lancet Digit. Health 2020 2 3 e138 e148 10.1016/S2589‑7500(20)30003‑0 33334578
    [Google Scholar]
  58. Haenssle H.A. Fink C. Schneiderbauer R. Toberer F. Buhl T. Blum A. Kalloo A. Hassen A.B.H. Thomas L. Enk A. Uhlmann L. Alt C. Arenbergerova M. Bakos R. Baltzer A. Bertlich I. Blum A. Bokor-Billmann T. Bowling J. Braghiroli N. Braun R. Buder-Bakhaya K. Buhl T. Cabo H. Cabrijan L. Cevic N. Classen A. Deltgen D. Fink C. Georgieva I. Hakim-Meibodi L.E. Hanner S. Hartmann F. Hartmann J. Haus G. Hoxha E. Karls R. Koga H. Kreusch J. Lallas A. Majenka P. Marghoob A. Massone C. Mekokishvili L. Mestel D. Meyer V. Neuberger A. Nielsen K. Oliviero M. Pampena R. Paoli J. Pawlik E. Rao B. Rendon A. Russo T. Sadek A. Samhaber K. Schneiderbauer R. Schweizer A. Toberer F. Trennheuser L. Vlahova L. Wald A. Winkler J. Wölbing P. Zalaudek I. 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 1842 10.1093/annonc/mdy166 29846502
    [Google Scholar]
  59. Li S. Zhao R. Zou H. Artificial intelligence for diabetic retinopathy. Chin. Med. J. 2022 135 3 253 260 10.1097/CM9.0000000000001816 34995039
    [Google Scholar]
  60. Alfaras M. Soriano M.C. Ortín S. A fast machine learning model for ecg-based heartbeat classification and arrhythmia detection. Front. Phys. 2019 7 2 103 10.3389/fphy.2019.00103
    [Google Scholar]
  61. Raghunath S. Pfeifer J.M. Ulloa-Cerna A.E. Nemani A. Carbonati T. Jing L. vanMaanen D.P. Hartzel D.N. Ruhl J.A. Lagerman B.F. Rocha D.B. Stoudt N.J. Schneider G. Johnson K.W. Zimmerman N. Leader J.B. Kirchner H.L. Griessenauer C.J. Hafez A. Good C.W. Fornwalt B.K. Haggerty C.M. 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 1298 10.1161/CIRCULATIONAHA.120.047829 33588584
    [Google Scholar]
  62. Becker J. Decker J.A. Römmele C. Kahn M. Messmann H. Wehler M. Schwarz F. Kroencke T. Scheurig-Muenkler C. Artificial intelligence-based detection of pneumonia in chest radiographs. Diagnostics 2022 12 6 1465 10.3390/diagnostics12061465 35741276
    [Google Scholar]
  63. Santamato V. Tricase C. Faccilongo N. Iacoviello M. Marengo A. Exploring the impact of artificial intelligence on healthcare management: A combined systematic review and machine-learning approach. Appl. Sci. 2024 14 22 10144 10.3390/app142210144
    [Google Scholar]
  64. Botha N.N. Segbedzi C.E. Dumahasi V.K. Maneen S. Kodom R.V. Tsedze I.S. Akoto L.A. Atsu F.S. Lasim O.U. Ansah E.W. Artificial intelligence in healthcare: A scoping review of perceived threats to patient rights and safety. Arch. Public Health 2024 82 1 188 10.1186/s13690‑024‑01414‑1 39444019
    [Google Scholar]
  65. Hirani R. Noruzi K. Khuram H. Hussaini A.S. Aifuwa E.I. Ely K.E. Lewis J.M. Gabr A.E. Smiley A. Tiwari R.K. Etienne M. Artificial intelligence and healthcare: A journey through history, present innovations, and future possibilities. Life 2024 14 5 557 10.3390/life14050557 38792579
    [Google Scholar]
  66. Kelly C.J. Karthikesalingam A. Suleyman M. Corrado G. King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019 17 1 195 10.1186/s12916‑019‑1426‑2 31665002
    [Google Scholar]
  67. Donins U. Behmane D. Challenges and Solutions for Artificial Intelligence Adoption in Healthcare – A Literature Review. Innovation in Medicine and Healthcare. Singapore Springer Nature Singapore 2023 10.1007/978‑981‑99‑3311‑2_6
    [Google Scholar]
  68. Rezaeikhonakdar D. AI chatbots and challenges of hipaa compliance for ai developers and vendors. J. Law Med. Ethics 2023 51 4 988 995 10.1017/jme.2024.15 38477276
    [Google Scholar]
  69. Williamson S.M. Prybutok V. Balancing privacy and progress: A review of privacy challenges, systemic oversight, and patient perceptions in ai-driven healthcare. Appl. Sci. 2024 14 2 675 10.3390/app14020675
    [Google Scholar]
  70. Cross J.L. Choma M.A. Onofrey J.A. Bias in medical ai: Implications for clinical decision-making. PLOS Dig. Health. 2024 3 11 e0000651 10.1371/journal.pdig.0000651 39509461
    [Google Scholar]
  71. Ratwani R.M. Sutton K. Galarraga J.E. Addressing ai algorithmic bias in health care. JAMA 2024 332 13 1051 1052 10.1001/jama.2024.13486 39230911
    [Google Scholar]
  72. Norori N. Hu Q. Aellen F.M. Faraci F.D. Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns 2021 2 10 100347 10.1016/j.patter.2021.100347 34693373
    [Google Scholar]
  73. Draghi B. Wang Z. Myles P. Tucker A. Identifying and handling data bias within primary healthcare data using synthetic data generators. Heliyon 2024 10 2 e24164 10.1016/j.heliyon.2024.e24164 38288010
    [Google Scholar]
  74. Bajwa J. Munir U. Nori A. Williams B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc. J. 2021 8 2 e188 e194 10.7861/fhj.2021‑0095 34286183
    [Google Scholar]
  75. Ross P. Spates K. Considering the safety and quality of artificial intelligence in health care. Jt. Comm. J. Qual. Patient Saf. 2020 46 10 596 599 10.1016/j.jcjq.2020.08.002 32878718
    [Google Scholar]
  76. Lämmermann L. Hofmann P. Urbach N. Managing artificial intelligence applications in healthcare: Promoting information processing among stakeholders. Int. J. Inf. Manage. 2024 75 4 102728 10.1016/j.ijinfomgt.2023.102728
    [Google Scholar]
  77. Olawade D.B. David-Olawade A.C. Wada O.Z. Asaolu A.J. Adereni T. Ling J. Artificial intelligence in healthcare delivery: Prospects and pitfalls. J. Med. Surg. Pub. Health 2024 3 100108 10.1016/j.glmedi.2024.100108
    [Google Scholar]
  78. Frasca M. Torre L.D. Pravettoni G. Cutica I. Explainable and interpretable artificial intelligence in medicine: A systematic bibliometric review. Disc. Artif. Intel. 2024 4 1 15 10.1007/s44163‑024‑00114‑7
    [Google Scholar]
  79. Amann J. Blasimme A. Vayena E. Frey D. Madai V.I. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med. Inform. Decis. Mak. 2020 20 1 310 10.1186/s12911‑020‑01332‑6 33256715
    [Google Scholar]
  80. Gerdes A. The role of explainability in AI-supported medical decision-making. Disc. Artif. Intel. 2024 4 1 29 10.1007/s44163‑024‑00119‑2
    [Google Scholar]
  81. Marshall D.T. Naff D.B. The ethics of using artificial intelligence in qualitative research. J. Empir. Res. Hum. Res. Ethics 2024 19 3 92 102 10.1177/15562646241262659 38881315
    [Google Scholar]
  82. Ossa A.L. Lorenzini G. Milford S.R. Shaw D. Elger B.S. Rost M. Integrating ethics in AI development: A qualitative study. BMC Med. Ethics 2024 25 1 10 10.1186/s12910‑023‑01000‑0 38262986
    [Google Scholar]
  83. Chudyk A.M. Waldman C. Horrill T. Demczuk L. Shimmin C. Stoddard R. Hickes S. Schultz A.S.H. Models and frameworks of patient engagement in health services research: A scoping review protocol. Res. Involv. Engagem. 2018 4 1 28 10.1186/s40900‑018‑0111‑5 30214822
    [Google Scholar]
  84. Dey N. Rautray P. Soni M. Patient-Centered Design in a Connected Healthcare World: A Case Study. Research into Design for a Connected World. Singapore Springer Singapore 2019 10.1007/978‑981‑13‑5974‑3_83
    [Google Scholar]
  85. Edgman-Levitan S. Schoenbaum S.C. Patient-centered care: Achieving higher quality by designing care through the patient’s eyes. Isr. J. Health Policy Res. 2021 10 1 21 10.1186/s13584‑021‑00459‑9 33673875
    [Google Scholar]
  86. Haug C.J. Drazen J.M. Artificial intelligence and machine learning in clinical medicine. N. Engl. J. Med. 2023 388 13 1201 1208 10.1056/NEJMra2302038 36988595
    [Google Scholar]
  87. Bagabir A.S. Ibrahim N.K. Bagabir A.H. Ateeq H.R. Covid-19 and artificial intelligence: Genome sequencing, drug development and vaccine discovery. J. Infect. Public Health 2022 15 2 289 296 10.1016/j.jiph.2022.01.011 35078755
    [Google Scholar]
  88. Pudjihartono N. Fadason T. Kempa-Liehr A.W. O’Sullivan J.M. A review of feature selection methods for machine learning-based disease risk prediction. Front. Bioinfor. 2022 2 927312 10.3389/fbinf.2022.927312 36304293
    [Google Scholar]
  89. Widen E. Raben T.G. Lello L. Hsu S.D.H. Machine learning prediction of biomarkers from snps and of disease risk from biomarkers in the uk biobank. Genes 2021 12 7 991 10.3390/genes12070991 34209487
    [Google Scholar]
  90. Wang H. Avillach P. Diagnostic classification and prognostic prediction using common genetic variants in autism spectrum disorder: Genotype-based deep learning. JMIR Med. Inform. 2021 9 4 e24754 10.2196/24754 33714937
    [Google Scholar]
  91. Sørlie T. Perou C.M. Tibshirani R. Aas T. Geisler S. Johnsen H. Hastie T. Eisen M.B. van de Rijn M. Jeffrey S.S. Thorsen T. Quist H. Matese J.C. Brown P.O. Botstein D. Lønning P.E. Børresen-Dale A.L. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. USA 2001 98 19 10869 10874 10.1073/pnas.191367098 11553815
    [Google Scholar]
  92. Yersal O. Barutca S. Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 2014 5 3 412 424 10.5306/wjco.v5.i3.412 25114856
    [Google Scholar]
  93. Leek J.T. Scharpf R.B. Bravo H.C. Simcha D. Langmead B. Johnson W.E. Geman D. Baggerly K. Irizarry R.A. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 2010 11 10 733 739 10.1038/nrg2825 20838408
    [Google Scholar]
  94. Quazi S. Artificial intelligence and machine learning in precision and genomic medicine. Med. Oncol. 2022 39 8 120 10.1007/s12032‑022‑01711‑1 35704152
    [Google Scholar]
  95. Subramanian M. Wojtusciszyn A. Favre L. Boughorbel S. Shan J. Letaief K.B. Pitteloud N. Chouchane L. Precision medicine in the era of artificial intelligence: Implications in chronic disease management. J. Transl. Med. 2020 18 1 472 10.1186/s12967‑020‑02658‑5 33298113
    [Google Scholar]
  96. Johnson K.B. Wei W.Q. Weeraratne D. Frisse M.E. Misulis K. Rhee K. Zhao J. Snowdon J.L. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 2021 14 1 86 93 10.1111/cts.12884 32961010
    [Google Scholar]
  97. Pulley J.M. Denny J.C. Peterson J.F. Bernard G.R. Vnencak-Jones C.L. Ramirez A.H. Delaney J.T. Bowton E. Brothers K. Johnson K. Crawford D.C. Schildcrout J. Masys D.R. Dilks H.H. Wilke R.A. Clayton E.W. Shultz E. Laposata M. McPherson J. Jirjis J.N. Roden D.M. Operational implementation of prospective genotyping for personalized medicine: The design of the Vanderbilt PREDICT project. Clin. Pharmacol. Ther. 2012 92 1 87 95 10.1038/clpt.2011.371 22588608
    [Google Scholar]
  98. Huang C. Clayton E.A. Matyunina L.V. McDonald L.D. Benigno B.B. Vannberg F. McDonald J.F. Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Sci. Rep. 2018 8 1 16444 10.1038/s41598‑018‑34753‑5 30401894
    [Google Scholar]
  99. Sheu Y. Magdamo C. Miller M. Das S. Blacker D. Smoller J.W. AI-assisted prediction of differential response to antidepressant classes using electronic health records. NPJ Digit. Med. 2023 6 1 73 10.1038/s41746‑023‑00817‑8 37100858
    [Google Scholar]
  100. Martin G.L. Jouganous J. Savidan R. Bellec A. Goehrs C. Benkebil M. Miremont G. Micallef J. Salvo F. Pariente A. Létinier L. Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data. Drug Saf. 2022 45 5 535 548 10.1007/s40264‑022‑01153‑8 35579816
    [Google Scholar]
  101. Lee H. Kim H.J. Chang H.W. Kim D.J. Mo J. Kim J.E. Development of a system to support warfarin dose decisions using deep neural networks. Sci. Rep. 2021 11 1 14745 10.1038/s41598‑021‑94305‑2 34285309
    [Google Scholar]
  102. Blasiak A. Truong A. Tan W.J.L. Kumar K.S. Tan S.B. Teo C.B. Tan B.K.J. Tadeo X. Tan H.L. Chee C.E. Yong W-P. Ho D. Sundar R. PRECISE CURATE.AI: A prospective feasibility trial to dynamically modulate personalized chemotherapy dose with artificial intelligence. J. Clin. Oncol. 2022 40 16_suppl Suppl. 1574 1574 10.1200/JCO.2022.40.16_suppl.1574
    [Google Scholar]
  103. Sjövall F. Lanckohr C. Bracht H. What’s new in therapeutic drug monitoring of antimicrobials? Intensive Care Med. 2023 49 7 857 859 10.1007/s00134‑023‑07060‑5 37133741
    [Google Scholar]
  104. Partin A. Brettin T.S. Zhu Y. Narykov O. Clyde A. Overbeek J. Stevens R.L. Deep learning methods for drug response prediction in cancer: Predominant and emerging trends. Front. Med. 2023 10 1086097 10.3389/fmed.2023.1086097 36873878
    [Google Scholar]
  105. Zhang H. Chen Y. Li F. Predicting anticancer drug response with deep learning constrained by signaling pathways. Front. Bioinformat. 2021 1 639349 10.3389/fbinf.2021.639349 36303766
    [Google Scholar]
  106. 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 10.3389/fphar.2021.814858 35153767
    [Google Scholar]
  107. Liu J.Y.H. Rudd J.A. Predicting drug adverse effects using a new gastro-intestinal pacemaker activity drug database (gipadd). Sci. Rep. 2023 13 1 6935 10.1038/s41598‑023‑33655‑5 37117211
    [Google Scholar]
  108. Blanco-González A. Cabezón A. Seco-González A. Conde-Torres D. Antelo-Riveiro P. Piñeiro Á. Garcia-Fandino R. The role of AI in drug discovery: Challenges, opportunities, and strategies. Pharmaceuticals 2023 16 6 891 10.3390/ph16060891 37375838
    [Google Scholar]
  109. Yang X. Wang Y. Byrne R. Schneider G. Yang S. Concepts of artificial intelligence for computer-assisted drug discovery. Chem. Rev. 2019 119 18 10520 10594 10.1021/acs.chemrev.8b00728 31294972
    [Google Scholar]
  110. Gangwal A. Ansari A. Ahmad I. Azad A.K. Kumarasamy V. Subramaniyan V. Wong L.S. Generative artificial intelligence in drug discovery: Basic framework, recent advances, challenges, and opportunities. Front. Pharmacol. 2024 15 2 1331062 10.3389/fphar.2024.1331062 38384298
    [Google Scholar]
  111. Jing W. Drug Repositioning in the AI-Driven Era: Data, Approaches, and Challenges IntechOpen: Rijeka. Repurposed Drugs - Current State and Future Perspectives, M.D.S. Engin 2024
    [Google Scholar]
  112. Nelson K.M. Chang E.T. Zulman D.M. Rubenstein L.V. Kirkland F.D. Fihn S.D. Using predictive analytics to guide patient care and research in a national health system. J. Gen. Intern. Med. 2019 34 8 1379 1380 10.1007/s11606‑019‑04961‑4 31011959
    [Google Scholar]
  113. Amarasingham R. Patzer R.E. Huesch M. Nguyen N.Q. Xie B. Implementing electronic health care predictive analytics: Considerations and challenges. Health Aff. 2014 33 7 1148 1154 10.1377/hlthaff.2014.0352 25006140
    [Google Scholar]
  114. Ansari M.S. Alok A.K. Jain D. Rana S. Gupta S. Salwan R. Venkatesh S. Predictive Model Based on Health Data Analysis for Risk of Readmission in Disease-Specific Cohorts. Perspect. Health Inf. Manag. 2021 18 Spring 1j 34035791
    [Google Scholar]
  115. Donzé J. Aujesky D. Williams D. Schnipper J.L. Potentially avoidable 30-day hospital readmissions in medical patients: Derivation and validation of a prediction model. JAMA Intern. Med. 2013 173 8 632 638 10.1001/jamainternmed.2013.3023 23529115
    [Google Scholar]
  116. Lopes J. Guimarães T. Santos M.F. Predictive and prescriptive analytics in healthcare: A survey. Procedia Comput. Sci. 2020 170 2 1029 1034 10.1016/j.procs.2020.03.078
    [Google Scholar]
  117. 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 4 1398 10.3390/app10041398
    [Google Scholar]
  118. Crossnohere N.L. Elsaid M. Paskett J. Bose-Brill S. Bridges J.F.P. Guidelines for artificial intelligence in medicine: Literature review and content analysis of frameworks. J. Med. Inter. Res. 2022 24 8 e36823 10.2196/36823 36006692
    [Google Scholar]
  119. Dasta J.F. Application of artificial intelligence to pharmacy and medicine. Hosp. Pharm. 1992 27 4 312 315, 319-322 10183640
    [Google Scholar]
  120. Vora L.K. Gholap A.D. Jetha K. Thakur R.R.S. Solanki H.K. Chavda V.P. Artificial intelligence in pharmaceutical technology and drug delivery design. Pharmaceutics 2023 15 7 1916 10.3390/pharmaceutics15071916 37514102
    [Google Scholar]
  121. Buch V.H. Ahmed I. Maruthappu M. Artificial intelligence in medicine: Current trends and future possibilities. Br. J. Gen. Pract. 2018 68 668 143 144 10.3399/bjgp18X695213 29472224
    [Google Scholar]
  122. Li L.R. Du B. Liu H.Q. Chen C. Artificial intelligence for personalized medicine in thyroid cancer: Current status and future perspectives. Front. Oncol. 2021 10 2 604051 10.3389/fonc.2020.604051 33634025
    [Google Scholar]
  123. Davoudi A. The intelligent ICU pilot study: Using artificial intelligence technology for autonomous patient monitoring. Sci. Rep. 2018 9 8020
    [Google Scholar]
  124. Curtis R.G. Bartel B. Ferguson T. Blake H.T. Northcott C. Virgara R. Maher C.A. Improving user experience of virtual health assistants: Scoping review. J. Med. Internet Res. 2021 23 12 e31737 10.2196/31737 34931997
    [Google Scholar]
  125. Kannan M.K.J. Virtual nursing assistant. J. Geogr. Sci. 2021 8 279 285
    [Google Scholar]
  126. Omarov B. Narynov S. Zhumanov Z. Artificial intelligence-enabled chatbots in mental health: A systematic review. Comput. Mater. Continua 2023 74 3 5105 5122 10.32604/cmc.2023.034655
    [Google Scholar]
  127. Sun G. Zhou Y.H. AI in healthcare: Navigating opportunities and challenges in digital communication. Front. Dig. Health 2023 5 1291132 10.3389/fdgth.2023.1291132 38173911
    [Google Scholar]
  128. Kim J.W. Jones K.L. D’Angelo E. How to prepare prospective psychiatrists in the era of artificial intelligence. Acad. Psychiatry 2019 43 3 337 339 10.1007/s40596‑019‑01025‑x 30659443
    [Google Scholar]
  129. Graham S. Depp C. Lee E.E. Nebeker C. Tu X. Kim H.C. Jeste D.V. Artificial intelligence for mental health and mental illnesses: An overview. Curr. Psychiatry Rep. 2019 21 11 116 10.1007/s11920‑019‑1094‑0 31701320
    [Google Scholar]
  130. Fitzpatrick K.K. 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 Ment. Health 2017 4 2 e19 10.2196/mental.7785 28588005
    [Google Scholar]
  131. Williams A.D. 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 10.1371/journal.pone.0057447 23451231
    [Google Scholar]
  132. Luxton D.D. Artificial intelligence in psychological practice: Current and future applications and implications. Prof. Psychol. Res. Pr. 2014 45 5 332 339 10.1037/a0034559
    [Google Scholar]
  133. Prochaska J.J. Vogel E.A. 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 10.2196/24850 33755028
    [Google Scholar]
  134. Lee E.E. Torous J. Choudhury D.M. Depp C.A. Graham S.A. Kim H.C. Paulus M.P. Krystal J.H. Jeste D.V. Artificial intelligence for mental health care: Clinical applications, barriers, facilitators, and artificial wisdom. Biol. Psychiatry Cogn. Neurosci. Neuroimaging 2021 6 9 856 864 10.1016/j.bpsc.2021.02.001 33571718
    [Google Scholar]
  135. Chew H.S.J. The use of artificial intelligence–based conversational agents (Chatbots) for weight loss: Scoping review and practical recommendations. JMIR Med. Inform. 2022 10 4 e32578 10.2196/32578 35416791
    [Google Scholar]
  136. Zhang J. Oh Y.J. 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 10.2196/22845 32996892
    [Google Scholar]
  137. Bombard Y. Baker G.R. Orlando E. Fancott C. Bhatia P. Casalino S. Onate K. Denis J.L. Pomey M.P. Engaging patients to improve quality of care: A systematic review. Implement. Sci. 2018 13 1 98 10.1186/s13012‑018‑0784‑z 30045735
    [Google Scholar]
  138. Wong C.K.M. Yeung D.Y. Ho H.C.Y. Tse K.P. Lam C.Y. Chinese older adults’ Internet use for health information. J. Appl. Gerontol. 2014 33 3 316 335 10.1177/0733464812463430 24717738
    [Google Scholar]
  139. Aggarwal A. Tam C.C. Wu D. Li X. Qiao S. Artificial intelligence–based chatbots for promoting health behavioral changes: Systematic review. J. Med. Internet Res. 2023 25 e40789 10.2196/40789 36826990
    [Google Scholar]
  140. Görtz M. An artificial intelligence-based chatbot for prostate cancer education: Design and patient evaluation study. Dig. Health. 2023 9 20552076231173304
    [Google Scholar]
  141. Nakhleh A. Spitzer S. Shehadeh N. ChatGPT’s response to the diabetes knowledge questionnaire: Implications for diabetes education. Diabetes Technol. Ther. 2023 25 8 571 573 10.1089/dia.2023.0134 37062754
    [Google Scholar]
  142. Kirchner G.J. Kim R.Y. Weddle J.B. Bible J.E. Can artificial intelligence improve the readability of patient education materials? Clin. Orthop. Relat. Res. 2023 481 11 2260 2267 10.1097/CORR.0000000000002668 37116006
    [Google Scholar]
  143. Lee D. Yoon S.N. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. Int. J. Environ. Res. Public Health 2021 18 1 271 10.3390/ijerph18010271 33401373
    [Google Scholar]
  144. Kaptchuk T.J. Miller F.G. Placebo effects in medicine. N. Engl. J. Med. 2015 373 1 8 9 10.1056/NEJMp1504023 26132938
    [Google Scholar]
  145. Lupton M. Some ethical and legal consequences of the application of artificial intelligence in the field of medicine. Trends. Med. 2018 18 4 100147 10.15761/TiM.1000147
    [Google Scholar]
  146. Pezzo M.V. Beckstead J.W. Patients prefer artificial intelligence to a human provider, provided the AI is better than the human: A commentary on Longoni, Bonezzi and Morewedge (2019). Judgm. Decis. Mak. 2020 15 3 443 445 10.1017/S1930297500007221
    [Google Scholar]
  147. Rojahn J. Palu A. Skiena S. Jones J.J. American public opinion on artificial intelligence in healthcare. PLoS One 2023 18 11 e0294028 10.1371/journal.pone.0294028 37943752
    [Google Scholar]
  148. 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 10.1186/s12911‑020‑01191‑1 32698869
    [Google Scholar]
  149. Khullar D. Casalino L.P. Qian Y. Lu Y. Krumholz H.M. Aneja S. Perspectives of patients about artificial intelligence in health care. JAMA Netw. Open 2022 5 5 e2210309 e2210309 10.1001/jamanetworkopen.2022.10309 35507346
    [Google Scholar]
  150. Russo S. Jongerius C. Faccio F. Pizzoli S.F.M. Pinto C.A. Veldwijk J. Janssens R. Simons G. Falahee M. Bekker-Grob d.E. Huys I. Postmus D. Kihlbom U. Pravettoni G. Understanding patients’ preferences: A systematic review of psychological instruments used in patients’ preference and decision studies. Value Health 2019 22 4 491 501 10.1016/j.jval.2018.12.007 30975401
    [Google Scholar]
  151. Young A.T. Amara D. Bhattacharya A. Wei M.L. Patient and general public attitudes towards clinical artificial intelligence: A mixed methods systematic review. Lancet Digit. Health 2021 3 9 e599 e611 10.1016/S2589‑7500(21)00132‑1 34446266
    [Google Scholar]
  152. Picek O. Spillover effects from next generation EU. Inter Econ. 2020 55 5 325 331 10.1007/s10272‑020‑0923‑z 33132414
    [Google Scholar]
  153. Sousa J.M. Mas D.F. Pesqueira A. Lemos C. Verde M.J. Cobianchi L. The potential of ai in health higher education to increase the students’ learning outcomes. TEM J. 2021 10 488 497 10.18421/TEM102‑02
    [Google Scholar]
/content/journals/ctmc/10.2174/0115680266339394250225112747
Loading
The Impact and Role of Artificial Intelligence (AI) in Healthcare: Systematic Review
Bentham Science Publishers ; https://doi.org/10.2174/0115680266339394250225112747
/content/journals/ctmc/10.2174/0115680266339394250225112747
/content/journals/ctmc/10.2174/0115680266339394250225112747
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher's website along with the published article.

file format download Download

  • Article Type:     Review Article
Keywords: Artificial intelligence (AI) ; AI applications ; Clinical decision-making ; Healthcare ; Patient care ; AI technologies

Most Cited Most Cited RSS feed

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