Train Clinical AI to Reason Like a Team of Doctors
Overview
Authors
Authors
Affiliations
Affiliations
Soon will be listed here.
References
1.
Ghassemi M, Oakden-Rayner L, Beam A
. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021; 3(11):e745-e750.
DOI: 10.1016/S2589-7500(21)00208-9.
View
2.
Narla A, Kuprel B, Sarin K, Novoa R, Ko J
. Automated Classification of Skin Lesions: From Pixels to Practice. J Invest Dermatol. 2018; 138(10):2108-2110.
DOI: 10.1016/j.jid.2018.06.175.
View
3.
Chanda T, Hauser K, Hobelsberger S, Bucher T, Nogueira Garcia C, Wies C
. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma. Nat Commun. 2024; 15(1):524.
PMC: 10789736.
DOI: 10.1038/s41467-023-43095-4.
View
4.
Rosell L, Alexandersson N, Hagberg O, Nilbert M
. Benefits, barriers and opinions on multidisciplinary team meetings: a survey in Swedish cancer care. BMC Health Serv Res. 2018; 18(1):249.
PMC: 5887214.
DOI: 10.1186/s12913-018-2990-4.
View
5.
Soukup T, Gandamihardja T, McInerney S, Green J, Sevdalis N
. Do multidisciplinary cancer care teams suffer decision-making fatigue: an observational, longitudinal team improvement study. BMJ Open. 2019; 9(5):e027303.
PMC: 6549703.
DOI: 10.1136/bmjopen-2018-027303.
View