The balance between artificial and human intelligence in clinical practice
Balanced adoption requires diversified datasets, privacy-preserving strategies (pseudonymization, differential privacy, federated learning), transparent reporting, AI literacy and ethics in medical education and interfaces that expose uncertainty and employ cognitive forcing functions. Post-deployment surveillance should track data drift, out-of-distribution inputs and performance using automated alerts and multidisciplinary review. Artificial intelligence should augment, never replace, clinical judgment, with explicit role delineation and continuous monitoring to safeguard equity and patient-centred outcomes.
Authors
- Jorma Ryhänen (ORCID: https://orcid.org/0000-0002-4676-2831)
- Turkka Anttila (ORCID: https://orcid.org/0000-0003-1563-1940)
- Robert F. Miller (ORCID: https://orcid.org/0000-0003-2067-4291)
- Domenico Marrella
- Bo Liu
- Philippe Liverneaux
Institutions
- Centre National de la Recherche Scientifique (FR)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- Peking University (CN)
- Beijing Jishuitan Hospital (CN)
- Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (FR)
- Institute of Musculoskeletal Science & Education (United States) (US)
- Université de Strasbourg (FR)
Publication Details
- Journal
- Journal of Hand Surgery (European Volume)
- Published
- 2026-01-15
- DOI
- https://doi.org/10.1177/17531934251401382
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 17.70