This paper is in the following e-collection/theme issue:

Artificial Intelligence (1216) Machine Learning (1401) Clinical Information and Decision Making (1340) Quality Improvement (226) Health Care Quality and Health Services Research (252) Cost-Effectiveness and Economics (234) E-Health Policy and Health Systems Innovation (208) Digital Health Reviews (1191)

Published on in Vol 23, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26391, first published .
A Typology of Existing Machine Learning–Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis

A Typology of Existing Machine Learning–Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis

A Typology of Existing Machine Learning–Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis

Authors of this article:

Ariadne A Nichol1 Author Orcid Image ;   Jason N Batten1 Author Orcid Image ;   Meghan C Halley1 Author Orcid Image ;   Julia K Axelrod1 Author Orcid Image ;   Pamela L Sankar2 Author Orcid Image ;   Mildred K Cho1 Author Orcid Image
Article Authors Cited by (13) Tweetations (15) Metrics

Journals

  1. Cho M. Rising to the challenge of bias in health care AI. Nature Medicine 2021;27(12):2079 View
  2. Prasetyo A, Noviana N, Rosdiana W, Anwar M, Hartiningsih , Hendrixon , Harwijayanti B, Fahlevi M. Stunting Convergence Management Framework through System Integration Based on Regional Service Governance. Sustainability 2023;15(3):1821 View
  3. Lomotey R, Kumi S, Hilton M, Orji R, Deters R. Using Machine Learning to Establish the Concerns of Persons With HIV/AIDS During the COVID-19 Pandemic From Their Tweets. IEEE Access 2023;11:37570 View
  4. Ho A, Bavli I, Mahal R, McKeown M. Multi-Level Ethical Considerations of Artificial Intelligence Health Monitoring for People Living with Parkinson’s Disease. AJOB Empirical Bioethics 2023:1 View
  5. Jiao W, Zhang X, D’Souza F. The Economic Value and Clinical Impact of Artificial Intelligence in Healthcare: A Scoping Literature Review. IEEE Access 2023;11:123445 View
  6. Trotsyuk A, Federico C, Cho M, Altman R, Magnus D. Stronger regulation of AI in biomedicine. Science Translational Medicine 2023;15(713) View
  7. Nichol A, Sankar P, Halley M, Federico C, Cho M. Developer Perspectives on Potential Harms of Machine Learning Predictive Analytics in Health Care: Qualitative Analysis. Journal of Medical Internet Research 2023;25:e47609 View
  8. Bavli I, Ho A, Mahal R, McKeown M. Ethical concerns around privacy and data security in AI health monitoring for Parkinson’s disease: insights from patients, family members, and healthcare professionals. AI & SOCIETY 2024 View
  9. Nichol A, Halley M, Federico C, Cho M, Sankar P. Moral Engagement and Disengagement in Health Care AI Development. AJOB Empirical Bioethics 2024:1 View
  10. Sloss E, McPherson J, Beck A, Guo J, Scheese C, Flake N, Chalkidis G, Staes C. Patient and Caregiver Perceptions of an Interface Design to Communicate Artificial Intelligence–Based Prognosis for Patients With Advanced Solid Tumors. JCO Clinical Cancer Informatics 2024;(8) View
  11. Largent E, Karlawish J, Wexler A. From an idea to the marketplace: identifying and addressing ethical and regulatory considerations across the digital health product-development lifecycle. BMC Digital Health 2024;2(1) View