AI-CHAT PRIME PRACTICE
Mem45-005	Diagnosis, triage and response quality of five large language models on emergency medicine vignettes: a blinded comparison
	Quote:	Conclusion In this selected set of synthetic vignettes, all five models correctly named the reference diagnosis, yet undertriage and potentially harmful omissions occurred and response quality differed between models. These findings do not establish any models clinical safety.... These findings do not establish that any model is safe for clinical use.
	Article:	https://www.medrxiv.org/content/10.64898/2026.10.21.26366076v1
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	Search Authors:	Gurinder (Sunny) Johal Li (Danny) Liang,
PRACTICE
Mmx44-008	Moving from Bench to Bedside: A Social History of Actionability in Biomedicine
	Quote:	Conclusions The adoption of actionability in these fields suggests that actionability as a conceptual framework may be most valuable to areas of translational medicine seeking to make sense of increasing amounts of data and technological innovation with differing levels of scientific validity and clinical utility. Recognizing this value, we also caution that actionability drives our attention primarily towards whether a test or piece of information can lead to action, not whether that action has proven benefits. As clinicians and researchers face difficult questions about how to sort through growing amounts of data to generate knowledge that can have a real impact on patient health, empirical bioethics should play a key role in analyzing the trade-offs and impacts of different approaches.
	Article:	https://www.medrxiv.org/content/10.64898/2026.08.03.26359586v1
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	Search Authors:	Kellie Owens Ari Z. Klein Graciela Gonzalez-Hernandez,
PRACTICE
Mpc44-005	Evaluating a Clinical Decision Support System for Chronic Pain Management: Secondary Per-Protocol Analysis of Pragmatic Randomized Controlled Trial Data
	Article:	https://www.medrxiv.org/content/10.64898/2026.10.07.26364970v1
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	Search Authors:	Emma McCord Justin Blackburn Ann M. Holmes Nate C. Apathy Randall W. Grout Christopher A. Harle Olena Mazurenko,
AI-CHAT PRIME PRACTICE
Mpt44-010	Errors, Hallucinations, and Clinical Impact of General-Purpose Multimodal Large Language Models in Histopathology
	Quote:	Conclusion Diagnostic accuracy alone substantially underestimates the safety limitations of general-purpose LLMs in histopathology. Errors and hallucinations were common even when the final diagnosis was correct, and their burden was independently associated with clinical impact. Evaluation frameworks should therefore assess both diagnostic correctness and the reliability and potential consequences of accompanying generated content.
	Article:	https://www.medrxiv.org/content/10.64898/2026.09.18.26363369v1
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	Search Authors:	Kris Lami Shipra Agarwal Aleksandra Asaturova Serdar Balci Agnes Harahap Haeyoun Kang Jennifer Kim Mina Komuta Thiyaphat Laohawetwanit Santosh Menon Jijgee Munkhdelger Hoa Hoang Ngoc Pham Daniel Gomes Pinto Ayushi Sahay Swati Satturwar Kurumi Seki Maher Sughayer Yuri Tachibana Ilknur Turkmen Qingqing Wu Ehsan Ullah Andrey Bychkov Junya Fukuoka,

Javilk's Research Links Newsletter for Week 45
Updated 11/14/2026-03:15:39 AM
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Mem45-005

Diagnosis, triage and response quality of five large language models on emergency medicine vignettes: a blinded comparison

Mmx44-008

Moving from Bench to Bedside: A Social History of Actionability in Biomedicine

Mpc44-005

Evaluating a Clinical Decision Support System for Chronic Pain Management: Secondary Per-Protocol Analysis of Pragmatic Randomized Controlled Trial Data

Mpt44-010

Errors, Hallucinations, and Clinical Impact of General-Purpose Multimodal Large Language Models in Histopathology