David C. Klonoff on Muck Rack

David C. Klonoff

California
As seen in: Color Research & Application, Nature, Wiley Online Library, PLOS, MDPI, Taylor & Francis Online, JAMA (The Journal of the American Medical Association), The Lancet, AHA/ASA Journals, Dove Medical Press and
Mills-Peninsula Health Services, UCSF, Editor-in-Chief, Journal of Diabetes Science and Technology
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Articles

Artificial intelligence for interpreting diabetes data using clinician-curated benchmarks

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By David C. Klonoff, San Mateo, Elizabeth Healey
| The Lancet Verified
With the increased use of wearable devices for self-monitoring, data sources and inputs could grow exponentially. In both adults and children with diabetes, there is a need to interpret time series data from continuous glucose monitors (CGM) to make timely and safe insulin dose adjustments.
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Narrowing the A1c gap: Personalized modeling of HbA1c- continuous glucose monitor discordance in type 1 diabetes

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By Simon Lebech Cichosz, David C. Klonoff, San Mateo
| PLOS
Loading metrics Open Access Peer-reviewed Research Article Citation: Cichosz SL, Thomsen CHN, Klonoff DC, Hirsch IB, Jensen MH (2026) Narrowing the A1c gap: Personalized modeling of HbA1c– continuous glucose monitor discordance in type 1 diabetes. PLOS Digit Health 5(2): e0001229.
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PRIMARY-AI: outcomes-based standards to safeguard primary care in the AI era - Nature Medicine

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By Dian Zeng, Lorainne Tudor Car, Kamlesh Khunti, Yuanli Liu, Till Bärnighausen, Niels H. Chavannes, Pearse A. Keane, Holger Kunz, Lan Xue, Yih Chung Tham, Lorenzo Righetto Verified, Donald J. Boudreau, Qionghai Dai, Weiping Jia, Yang Liu Verified, Dinggang Shen, Jia Liu, Weixing Shen, John S. Ji, Zhong Lin Wang, Zhiyi Wang, Haibo Wang, Shenglan Tang, Chenyang Pei, Zehua Jiang, Zihao Zou, Yiming Qin, Huating Li, Yasha Wang, Dinesh Visva Gunasekeran, Sabrina Wong, Dong Xu, Ryan J. Urbanowicz, Liliana Laranjo, Ana Neves, Nan Liu, Neil M. Bressler, Rifat Atun, David C. Klonoff, Nigam H. Shah, Josip Car, Rupa Sarker, Phuoc Van Le
| Nature Verified
Artificial intelligence (AI) is being deployed at scale in primary care — from diagnostic support and risk stratification to triage and clinical documentation — without any agreed-upon outcomes-based framework for determining whether it strengthens or undermines continuity, coordination, comprehensiveness and people-centered care1. This is dangerous for the billions of people whose first contact with health systems is primary care.
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