Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency

Author:

Cutillo Christine M., ,Sharma Karlie R.ORCID,Foschini LucaORCID,Kundu Shinjini,Mackintosh Maxine,Mandl Kenneth D.

Publisher

Springer Science and Business Media LLC

Subject

Health Information Management,Health Informatics,Computer Science Applications,Medicine (miscellaneous)

Reference33 articles.

1. Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

2. Wiens, J. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat. Med. 25, 1337–1340 (2019).

3. McDermott, M. B. A. et al. Reproducibility in machine learning for health. Preprint at https://www.profillic.com/paper/arxiv:1907.01463 (2019).

4. Finlayson, S. G. et al. Adversarial attacks on medical machine learning. Science 363, 1287–1289 (2019).

5. Schulam, P. & Saria, S. Can you trust this prediction? Auditing pointwise reliability after learning. PMLR. 89, 1022–1031 (2019).

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