Accessibility settings

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

Tutorial (174) Organizational Issues (278) Theoretical Frameworks and Concepts (604) Machine Learning (3402) Data Science (735) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (183) AI Language Models in Health Care (874)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/66100, first published .
Doctor typing on laptop, reviewing medical history form with stethoscope

AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations

AI and Machine Learning Terminology in Medicine, Psychology, and Social Sciences: Tutorial and Practical Recommendations

Authors of this article:

Bo Cao1, 2, 3 Author Orcid Image ;   Russell Greiner1, 2, 4 Author Orcid Image ;   Andrew Greenshaw1 Author Orcid Image ;   Jie Sui5 Author Orcid Image
Article Authors Cited by (11) Tweetations (3) Metrics

Journals

  1. Gangwal A, Lavecchia A. IMPACT Framework: Establishing Global Standards for Artificial Intelligence Implementation, Methodology, and Translation in Drug Discovery. WIREs Computational Molecular Science 2026;16(2) View
  2. Thongprayoon C, Pesce F, Cheungpasitporn W. Clinical Artificial Intelligence Agents in Nephrology: From Prediction to Action Through Workflow-Native Intelligence—A Roadmap for Workflow-Integrated Care. Journal of Clinical Medicine 2026;15(7):2576 View
  3. Almarhabi M, Hawkes C, Grosan C, Sonesson L. Virtual simulation in postgraduate healthcare education: Definitions, models, and applications in emergency medicine: A scoping review. Artificial Intelligence in Emergency Medicine 2026;2:100024 View
  4. Ben Dor B, Sonis S. Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer. Head and Neck Pathology 2026;20(1) View
  5. Neale M, Wong C, Kreuter D, Taylor J, Sivapalaratnam S. Understanding the Barriers to Translating Artificial Intelligence into the Clinical Laboratory. European Journal of Internal Medicine 2026:107029 View
  6. Chen Y, Zhang Y, Zhang Z. Machine learning prediction for menopause women with low bone mass: a multicenter and retrospective study. Scientific Reports 2026;16(1) View
  7. Alradwan I, Allabban S, Aleissa A, Alqahtani N, Alghamdi H, Al Fayez N, Alshabibi M, Tawfik E, Almughem F, Alshehri A. Pulmonary Drug Delivery in the Era of Nanomedicine: From Biological Barriers to Artificial Intelligence-Driven Optimization. Pharmaceuticals 2026;19(7):1095 View
  8. Gangwal A, Lavecchia A. From artificial intelligence promise to translational practice in drug discovery: Reproducibility, validation and evidence-based implementation. Drug Discovery Today 2026;31(6):104835 View
  9. Bogdanova N, Serebrova S, Klimov V, Esakova E, Zhuravleva M. Prospects for integrating artificial intelligence into the practice of gastroenterologists and hepatologists. Meditsinskiy sovet = Medical Council 2026;(15):182 View
  10. Karim S, Somers V. Reading between the lines—machine learning, cardiovascular risk, and the overnight electrocardiogram. SLEEP 2026 View

Books/Policy Documents

  1. Peñalosa J, Díez E. Advances in Production Management Systems: Shaping the Future of Industry Through Sustainable, Data-Driven, and Human-Centric Production Systems. View