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Found 12 papers, 2 papers with code
Advancing Conversational Diagnostic AI with Multimodal Reasoning
Large Language Models (LLMs) have demonstrated great potential for conducting diagnostic conversations but evaluation has been largely limited to language-only interactions, deviating from the real-world requirements of remote care delivery.
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).
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
In this work, we explore how conformal prediction can be used to provide uncertainty quantification in language models for the specific task of multiple-choice question-answering.
TIER: Text-Image Entropy Regularization for CLIP-style models
We formalize this observation using a novel regularization scheme that penalizes the entropy of the text-token to image-patch similarity scores.
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction
Self-supervised models trained with a contrastive loss such as CLIP have shown to be very powerful in zero-shot classification settings.
Self-Supervision on Images and Text Reduces Reliance on Visual Shortcut Features
Deep learning models trained in a fully supervised manner have been shown to rely on so-called "shortcut" features.
