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Found 7 papers, 0 papers with code
Towards Conversational AI for Disease Management
We advance the previously demonstrated diagnostic capabilities of the Articulate Medical Intelligence Explorer (AMIE) through a new LLM-based agentic system optimised for clinical management and dialogue, incorporating reasoning over the evolution of disease and multiple patient visit encounters, response to therapy, and professional competence in medication prescription.
Towards an AI co-scientist
These results, detailed in separate, co-timed reports, demonstrate the potential to augment biomedical and scientific discovery and usher an era of AI empowered scientists.
Exploring Large Language Models for Specialist-level Oncology Care
Large language models (LLMs) have shown remarkable progress in encoding clinical knowledge and responding to complex medical queries with appropriate clinical reasoning.
Towards Democratization of Subspeciality Medical Expertise
Cardiologists' responses with access to AMIE were superior to cardiologist responses without access to AMIE for all 10 domains.
Capabilities of Gemini Models in Medicine
We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin.
Ranked #1 on
Question Answering
on MedQA
(using extra training data)
Towards Conversational Diagnostic AI
The study included 149 case scenarios from clinical providers in Canada, the UK, and India, 20 PCPs for comparison with AMIE, and evaluations by specialist physicians and patient actors.
Towards Accurate Differential Diagnosis with Large Language Models
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).
