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Abstract
A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that combines clinical history, physical examination, investigations and procedures. Interactive interfaces powered by large language models present new opportunities to assist and automate aspects of this process
1
. Here we introduce the Articulate Medical Intelligence Explorer (AMIE), a large language model that is optimized for diagnostic reasoning, and evaluate its ability to generate a differential diagnosis alone or as an aid to clinicians. Twenty clinicians evaluated 302 challenging, real-world medical cases sourced from published case reports. Each case report was read by two clinicians, who were randomized to one of two assistive conditions: assistance from search engines and standard medical resources; or assistance from AMIE in addition to these tools. All clinicians provided a baseline, unassisted differential diagnosis prior to using the respective assistive tools. AMIE exhibited standalone performance that exceeded that of unassisted clinicians (top-10 accuracy 59.1% versus 33.6%,
P
= 0.04). Comparing the two assisted study arms, the differential diagnosis quality score was higher for clinicians assisted by AMIE (top-10 accuracy 51.7%) compared with clinicians without its assistance (36.1%; McNemar’s test: 45.7,
P
Academic Publication Towards accurate differential diagnosis with large language models
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Towards accurate differential diagnosis with large language models
Abstract A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that com...
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Abstract Clinical decision-making is one of the most impactful parts of a physician’s responsibilities and stands to benefit greatly from artificial intelligence solutions and lar...
Large Language Models in Healthcare and Medical Applications: A Review
This paper provides a systematic and in-depth examination of large language models (LLMs) in the healthcare domain, addressing their significant potential to transform medical practice through adva...
Current applications and challenges in large language models for patient care: a systematic review
Abstract Background The introduction of large language models (LLMs) into clinical practice promises to improve patient education and empo...
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What is the core focus of the research titled 'Towards accurate differential diagnosis with large language models'?
This literature focuses on: Abstract A comprehensive differential diagnosis is a cornerstone of medical care that is often reached through an iterative process of interpretation that combines clinical history, physical examination, inve...
Are there open-source GitHub repositories related to Towards accurate differential diagnosis with large language models?
Yes, open-source projects like nidhinjs/prompt-master (A Claude skill that writes the accurate prompts for any AI tool. Zero tokens or credits wasted. Full context and memory retention) are actively building upon these concepts.
Which startups are commercializing the technology behind Towards accurate differential diagnosis with large language models?
Products like X-Pilot are bringing this to market. Their focus is: Explain anything accurately, from document to video course.
What other academic literature is closely related to 'Towards accurate differential diagnosis with large language models'?
Yes, highly correlated activity was mapped. An entry titled 'Towards accurate differential diagnosis with large language models' discusses this: Abstract A comprehensive differential diagnosis is a cornerstone of medical care that is often reached throu...
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Commercial Realization
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GitHubnidhinjs/prompt-master
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GitHubpixel-point/aval
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Product HuntX-Pilot
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Product HuntGrok Voice API