Episódio 165 – MSX1 com Piter Punk, Tomo II – Parte B - Retrópolis

Bem-vindos ao podcast Retrópolis! Apresentado pela Municipalidade de Retrópolis. Esta é a Parte B do Episódio 165. Sobre o episódio Todo ano a gente traz o Piter Punk pra falar de MSX. Esse ano é sobre MSX 1, não só porque não tínhamos falado de MSX 1 com o Punk, mas também porque este ano,

Lime Tree, La Noraie, Luçay-le-Mâle, France. August 2021. Ref-11606 - Denis Olivier Art Photography

Black-and-white photograph Lime Tree, La Noraie, Luçay-le-Mâle, France. August 2021. Ref-11606 by Denis Olivier. Fine-art print, original signed limited editions, reproductions, optional frame available for purchase at sizes from 10x13 cm to 56x70 cm (4.1x5.1

Murder of a crow

Towards Accurate Differential Diagnosis with Large Language Models

An accurate differential diagnosis (DDx) is a cornerstone of medical care, often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by Large Language Models (LLMs) present new opportunities to both assist and automate aspects of this process. In this study, we introduce an LLM optimized for diagnostic reasoning, and evaluate its ability to generate a DDx alone or as an aid to clinicians. 20 clinicians evaluated 302 challenging, real-world medical cases sourced from the New England Journal of Medicine (NEJM) case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: either assistance from search engines and standard medical resources, or LLM assistance in addition to these tools. All clinicians provided a baseline, unassisted DDx prior to using the respective assistive tools. Our LLM for DDx exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% vs 33.6%, [p = 0.04]). Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51.7%) compared to clinicians without its assistance (36.1%) (McNemar's Test: 45.7, p < 0.01) and clinicians with search (44.4%) (4.75, p = 0.03). Further, clinicians assisted by our LLM arrived at more comprehensive differential lists than those without its assistance. Our study suggests that our LLM for DDx has potential to improve clinicians' diagnostic reasoning and accuracy in challenging cases, meriting further real-world evaluation for its ability to empower physicians and widen patients' access to specialist-level expertise.