#MedicalAI

yourimpactlab.com

Can an AI language model write care-planning summaries that rated close to clinicians' in a blinded test?

Making research accessible so anyone can benefit and make a difference

buff.ly

Sequential diagnosis with language models

We explored the capabilities of frontier language models in sequential diagnosis, a diagnostic paradigm that captures the iterative nature of clinical reasoning. Given a brief clinical presentation,…

dlvr.it

Large Language Model Framework for Structured Biomarker Extraction and Triple-Negative Breast #Cancer Identification From Pathology Reports: Development and Evaluation Study

Background: Triple-negative breast #Cancer (TNBC) is an aggressive molecular subtype of breast #Cancer that needs to be identified accurately from pathology reports to inform decision-making. Large language models (LLMs) have been reported to show great potential in the automatic extraction of important oncology information from unstructured medical text. Objective: We aimed to create and test an LLM-based approach for automatic biomarker extraction and TNBC subtype classification based on breast #Cancer pathology reports using prompt-engineering techniques. Methods: In total, 532 breast #Cancer pathology reports from The #Cancer Genome Atlas (TCGA) were used. Our approach extracted structured oncology features, such as estrogen receptor status, progesterone receptor status, HER2 status, tumor size, tumor grade, and tumor histology, which were used to identify TNBC. We tested 4 frontier models: GPT-4o mini (released July 18, 2024), GPT-4.1 nano (released April 14, 2025), GPT-5 nano (released August 7, 2025), and Gemini 2.5 Flash-Lite. Results: The designed framework was able to extract relevant biomarkers and clinicopathological attributes from unstructured pathology reports and allowed for the automated identification of patients with TNBC. Gemini 2.5 Flash-Lite showed superior performance in the extraction of clinicopathological attributes. For all models investigated, chain-of-thought (CoT) prompting was shown to outperform other strategies, such as zero-shot and few-shot prompting, for both biomarker extraction and TNBC classification. GPT-4o mini using CoT prompts reported the highest performance for TNBC classification. Conclusions: Using prompt-engineering techniques based on LLMs proved to be an efficient method of translating unstructured breast #Cancer pathology reports into structured clinical information. Moreover, it can be used to accurately identify the TNBC subtype.

www.youtube.com

AI in Healthcare: The Security Risks Hospitals Must Fix #shorts

AI in medicine offers massive potential for diagnostics, but basic security gaps remain the real crisis. Before integrating advanced models into radiology and pathology, healthcare systems must priori

blog.hangadac.com

MS AI 진단, 의사보다 2배 정확한 이유 - 천의무봉

마이크로소프트 AI(MS AI)의 진단 시스템 MAI-DxO가 어려운 의료 사례 시뮬레이션에서 일반의보다 두 배 이상 정확했다는 연구 결과가 나왔습니다. 2026년 10월 22일 Science에 발표된 이 연구는 AI가 어디서 강하고 어디서 한계가 있는지 보여줍니다. 핵심은 모델