Published on in Vol 22, No 2 (2020): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16866, first published .
The Economic Impact of Artificial Intelligence in Health Care: Systematic Review

The Economic Impact of Artificial Intelligence in Health Care: Systematic Review

The Economic Impact of Artificial Intelligence in Health Care: Systematic Review

Journals

  1. Adly A, Adly A, Adly M. Approaches Based on Artificial Intelligence and the Internet of Intelligent Things to Prevent the Spread of COVID-19: Scoping Review. Journal of Medical Internet Research 2020;22(8):e19104 View
  2. Zhang Y, zhang X, Wu Q, Gu C, Wang Z. Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. Journal of Laparoendoscopic & Advanced Surgical Techniques 2021;31(10):1143 View
  3. von Wedel P, Hagist C. Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review. Journal of Medical Internet Research 2020;22(11):e23315 View
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  5. He Q, Du F, Simonse L. A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth. JMIR Medical Informatics 2021;9(4):e23238 View
  6. Petersen C, Smith J, Freimuth R, Goodman K, Jackson G, Kannry J, Liu H, Madhavan S, Sittig D, Wright A. Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper. Journal of the American Medical Informatics Association 2021;28(4):677 View
  7. Sachar S, Dakessian M, Beitari S, Badrinarayanan S. Artificial Intelligence Alongside Physicians in Canada: Reality and Risks. Journal of Science Policy & Governance 2020;17(01) View
  8. Stanimirović D, Matetić V. Can the COVID-19 pandemic boost the global adoption and usage of eHealth solutions?. Journal of Global Health 2020;10(2) View
  9. Nicol G, Ricchio A, Metts C, Yingling M, Ramsey A, Schweiger J, Miller J, Lenze E. A Smartphone-Based Technique to Detect Dynamic User Preferences for Tailoring Behavioral Interventions: Observational Utility Study of Ecological Daily Needs Assessment. JMIR mHealth and uHealth 2020;8(11):e18609 View
  10. Yin J, Ngiam K, Teo H. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of Medical Internet Research 2021;23(4):e25759 View
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  17. Abbasgholizadeh Rahimi S, Légaré F, Sharma G, Archambault P, Zomahoun H, Chandavong S, Rheault N, T Wong S, Langlois L, Couturier Y, Salmeron J, Gagnon M, Légaré J. Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal. Journal of Medical Internet Research 2021;23(9):e29839 View
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  19. Svedberg P, Reed J, Nilsen P, Barlow J, Macrae C, Nygren J. Toward Successful Implementation of Artificial Intelligence in Health Care Practice: Protocol for a Research Program. JMIR Research Protocols 2022;11(3):e34920 View
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  21. Gomez Rossi J, Feldberg B, Krois J, Schwendicke F. Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis. JMIR Medical Informatics 2022;10(8):e33703 View
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  30. Zipori A, Kerley C, Klein A, Kenney R. Real-World Translation of Artificial Intelligence in Neuro-Ophthalmology: The Challenges of Making an Artificial Intelligence System Applicable to Clinical Practice. Journal of Neuro-Ophthalmology 2022;42(3):287 View
  31. Tachkov K, Zemplenyi A, Kamusheva M, Dimitrova M, Siirtola P, Pontén J, Nemeth B, Kalo Z, Petrova G. Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries. Frontiers in Public Health 2022;10 View
  32. Gurevich E, El Hassan B, El Morr C. Equity within AI systems: What can health leaders expect?. Healthcare Management Forum 2023;36(2):119 View
  33. Wawer Matos P, Reimer R, Rokohl A, Caldeira L, Heindl L, Große Hokamp N. Artificial Intelligence in Ophthalmology – Status Quo and Future Perspectives. Seminars in Ophthalmology 2023;38(3):226 View
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  36. Pietris J, Lam A, Bacchi S, Gupta A, Kovoor J, Chan W. Health Economic Implications of Artificial Intelligence Implementation for Ophthalmology in Australia: A Systematic Review. Asia-Pacific Journal of Ophthalmology 2022;11(6):554 View
  37. Wamala-Andersson S, Richardson M, Landerdahl Stridsberg S, Ryan J, Sukums F, Goh Y. Artificial Intelligence and Precision Health Through Lenses of Ethics and Social Determinants of Health: Protocol for a State-of-the-Art Literature Review. JMIR Research Protocols 2023;12:e40565 View
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  42. Prodan A, Deimel L, Ahlqvist J, Birov S, Thiel R, Toivanen M, Kolitsi Z, Kalra D. Success Factors for Scaling Up the Adoption of Digital Therapeutics Towards the Realization of P5 Medicine. Frontiers in Medicine 2022;9 View
  43. Petersson L, Larsson I, Nygren J, Nilsen P, Neher M, Reed J, Tyskbo D, Svedberg P. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Services Research 2022;22(1) View
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  45. Monteith S, Glenn T, Geddes J, Whybrow P, Achtyes E, Bauer M. Expectations for Artificial Intelligence (AI) in Psychiatry. Current Psychiatry Reports 2022;24(11):709 View
  46. Khamisy-Farah R, Gilbey P, Furstenau L, Sott M, Farah R, Viviani M, Bisogni M, Kong J, Ciliberti R, Bragazzi N. Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature. International Journal of Environmental Research and Public Health 2021;18(17):8989 View
  47. Bakker L, Aarts J, Uyl-de Groot C, Redekop K. How can we discover the most valuable types of big data and artificial intelligence-based solutions? A methodology for the efficient development of the underlying analytics that improve care. BMC Medical Informatics and Decision Making 2021;21(1) View
  48. Ruamviboonsuk P, Chantra S, Seresirikachorn K, Ruamviboonsuk V, Sangroongruangsri S. Economic Evaluations of Artificial Intelligence in Ophthalmology. Asia-Pacific Journal of Ophthalmology 2021;10(3):307 View
  49. Wolff J, Matschinske J, Baumgart D, Pytlik A, Keck A, Natarajan A, von Schacky C, Pauling J, Baumbach J. Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare – a proof of concept study for the prediction of coronary artery calcification scores. Journal of Integrative Bioinformatics 2022;19(4) View
  50. Schwendicke F, Cejudo Grano de Oro J, Garcia Cantu A, Meyer-Lueckel H, Chaurasia A, Krois J. Artificial Intelligence for Caries Detection: Value of Data and Information. Journal of Dental Research 2022;101(11):1350 View
  51. Morris M, Song E, Rajesh A, Asaad M, Phillips B. Ethical, Legal, and Financial Considerations of Artificial Intelligence in Surgery. The American Surgeon 2023;89(1):55 View
  52. Hashiguchi T, Oderkirk J, Slawomirski L. Fulfilling the Promise of Artificial Intelligence in the Health Sector: Let’s Get Real. Value in Health 2022;25(3):368 View
  53. Ramessur R, Raja L, Kilduff C, Kang S, Li J, Thomas P, Sim D. Impact and Challenges of Integrating Artificial Intelligence and Telemedicine into Clinical Ophthalmology. Asia-Pacific Journal of Ophthalmology 2021;10(3):317 View
  54. Gomez Rossi J, Rojas-Perilla N, Krois J, Schwendicke F. Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy. JAMA Network Open 2022;5(3):e220269 View
  55. Day S, Shah V, Kaganoff S, Powelson S, Mathews S. Assessing the Clinical Robustness of Digital Health Startups: Cross-sectional Observational Analysis. Journal of Medical Internet Research 2022;24(6):e37677 View
  56. Khan W, Seto E. A “Do No Harm” Novel Safety Checklist and Research Approach to Determine Whether to Launch an Artificial Intelligence–Based Medical Technology: Introducing the Biological-Psychological, Economic, and Social (BPES) Framework. Journal of Medical Internet Research 2023;25:e43386 View
  57. Hickok M, Dorsey C, O'Brien T, Baur D, Ingram K, Chauhan C, Gamundani A. Case Study: The Distilling of a Biased Algorithmic Decision System through a Business Lens. SSRN Electronic Journal 2022 View
  58. Channa R, Wolf R, Abràmoff M, Lehmann H. Effectiveness of artificial intelligence screening in preventing vision loss from diabetes: a policy model. npj Digital Medicine 2023;6(1) View
  59. Brauner P, Hick A, Philipsen R, Ziefle M. What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI. Frontiers in Computer Science 2023;5 View
  60. Stonko D, Morrison J, Hicks C. A review of mature machine learning- and artificial intelligence-enabled applications in aortic surgery. JVS-Vascular Insights 2023;1:100016 View
  61. Kelly L, Coote L, Dinnes J, Fleming C, Holmes H, Matin R. Key issues when considering adopting a skin cancer diagnostic tool that uses artificial intelligence. British Journal of Dermatology 2023;189(2):219 View
  62. Langius-Wiffen E, de Jong P, Hoesein F, Dekker L, van den Hoven A, Nijholt I, Boomsma M, Veldhuis W. Retrospective batch analysis to evaluate the diagnostic accuracy of a clinically deployed AI algorithm for the detection of acute pulmonary embolism on CTPA. Insights into Imaging 2023;14(1) View
  63. Rizvi A, Rizvi F, Lalakia P, Hyman L, Frasso R, Sztandera L, Das A. Is Artificial Intelligence the Cost-Saving Lens to Diabetic Retinopathy Screening in Low- and Middle-Income Countries?. Cureus 2023 View
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  65. Chinnasamy V, Sakulsaengprapha V, Mathews S. The Benefit of Artificial Intelligence-Based Diagnosis in Gastroenterology and Hepatology Is Highly Variable: A Diagnostic Need and Burden Analysis. Gastroenterology 2023;165(3):788 View
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