TY - JOUR
T1 - The transformation of patient-clinician relationships with AI-based medical advice
AU - Nov, Oded
AU - Aphinyanaphongs, Yindalon
AU - Lui, Yvonne W.
AU - Mann, Devin
AU - Porfiri, Maurizio
AU - Riedl, Mark
AU - Rizzo, John Ross
AU - Wiesenfeld, Batia
N1 - Funding Information:
This work was supported by a U.S. National Science Foundation grants #1928614, #1928586.
PY - 2021/3
Y1 - 2021/3
N2 - The transformation of patient-clinician relationships with AI-based medical advice is discussed. many new tools are based on entirely new ‘black-box’ AI-based technologies, whose inner workings are likely not fully understood by patients or clinicians. Most patients with Type 1 diabetes now use continuous glucose monitors and insulin pumps to tightly manage their disease. Their clinicians carefully review the data streams from both devices to recommend dosage adjustments. Recently new automated recommender systems to monitor and analyze food intake, insulin doses, physical activity, and other factors influencing glucose levels, and provide data-intensive, AI-based recommendations on how to titrate the regimen, are in different stages of FDA approval using ‘black box’ technology, which is an alluring proposition for a clinical scenario that requires identification of meaningful patterns in complex and voluminous data.
AB - The transformation of patient-clinician relationships with AI-based medical advice is discussed. many new tools are based on entirely new ‘black-box’ AI-based technologies, whose inner workings are likely not fully understood by patients or clinicians. Most patients with Type 1 diabetes now use continuous glucose monitors and insulin pumps to tightly manage their disease. Their clinicians carefully review the data streams from both devices to recommend dosage adjustments. Recently new automated recommender systems to monitor and analyze food intake, insulin doses, physical activity, and other factors influencing glucose levels, and provide data-intensive, AI-based recommendations on how to titrate the regimen, are in different stages of FDA approval using ‘black box’ technology, which is an alluring proposition for a clinical scenario that requires identification of meaningful patterns in complex and voluminous data.
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U2 - 10.1145/3417518
DO - 10.1145/3417518
M3 - Review article
AN - SCOPUS:85101579091
SN - 0001-0782
VL - 64
SP - 46
EP - 48
JO - Communications of the ACM
JF - Communications of the ACM
IS - 3
ER -