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Found 33 papers, 7 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.
LQR based $ω-$stabilization of a heat equation with memory
We consider a heat equation with memory which is defined on a bounded domain in $\mathbb{R}^d$ and is driven by $m$ control inputs acting on the interior of the domain.
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.
Flatness-based motion planning for a non-uniform moving cantilever Euler-Bernoulli beam with a tip-mass
The position of the cantilever joint is the control input to the beam.
Tx-LLM: A Large Language Model for Therapeutics
Developing therapeutics is a lengthy and expensive process that requires the satisfaction of many different criteria, and AI models capable of expediting the process would be invaluable.
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).
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment.
Model reference adaptive control for state and input constrained linear systems
Several modifications of the model reference adaptive control (MRAC) framework have been proposed to address input constraints in uncertain linear systems.
The Capability of Large Language Models to Measure Psychiatric Functioning
The current work investigates the capability of Large language models (LLMs) that are explicitly trained on large corpuses of medical knowledge (Med-PaLM 2) to predict psychiatric functioning from patient interviews and clinical descriptions without being trained to do so.
Towards Generalist Biomedical AI
While considerable work is needed to validate these models in real-world use cases, our results represent a milestone towards the development of generalist biomedical AI systems.
Motion planning for parabolic equations using flatness and finite-difference approximations
Then using the flatness approach we construct an input signal that transfers this ODE between states determined by the initial and final states of the parabolic equation.
Adaptive identification of SISO linear infinite-dimensional systems
The unknown parameter can be reconstructed using the transfer function coefficient estimates obtained with n large and the algebraic expressions relating the transfer function coefficients to the unknown parameter.
Towards Expert-Level Medical Question Answering with Large Language Models
Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67. 2% on the MedQA dataset.
Large Language Models Encode Clinical Knowledge
To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars.
Ranked #4 on
Multiple Choice Question Answering (MCQA)
on MedMCQA
(Dev Set (Acc-%) metric)
Robust and Efficient Medical Imaging with Self-Supervision
These results suggest that REMEDIS can significantly accelerate the life-cycle of medical imaging AI development thereby presenting an important step forward for medical imaging AI to deliver broad impact.
Diagnosing failures of fairness transfer across distribution shift in real-world medical settings
Diagnosing and mitigating changes in model fairness under distribution shift is an important component of the safe deployment of machine learning in healthcare settings.
MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation
Medical AI has tremendous potential to advance healthcare by supporting the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving provider and patient experience.
Transient and Asymptotic Properties of Robust Adaptive Controllers in the Presence of Non-Coercive Lyapunov Functions
Adaptive control architectures often make use of Lyapunov functions to design adaptive laws.
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier.
Supervised Transfer Learning at Scale for Medical Imaging
However, for medical imaging, the value of transfer learning is less clear.
Big Self-Supervised Models Advance Medical Image Classification
Self-supervised pretraining followed by supervised fine-tuning has seen success in image recognition, especially when labeled examples are scarce, but has received limited attention in medical image analysis.
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Predictors returned by underspecified pipelines are often treated as equivalent based on their training domain performance, but we show here that such predictors can behave very differently in deployment domains.
Addressing the Real-world Class Imbalance Problem in Dermatology
Class imbalance is a common problem in medical diagnosis, causing a standard classifier to be biased towards the common classes and perform poorly on the rare classes.
Contrastive Training for Improved Out-of-Distribution Detection
Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems.
Ranked #12 on
Out-of-Distribution Detection
on CIFAR-100 vs CIFAR-10
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
DermGAN: Synthetic Generation of Clinical Skin Images with Pathology
Despite the recent success in applying supervised deep learning to medical imaging tasks, the problem of obtaining large and diverse expert-annotated datasets required for the development of high performant models remains particularly challenging.
A deep learning system for differential diagnosis of skin diseases
In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories).
Towards VQA Models That Can Read
We show that LoRRA outperforms existing state-of-the-art VQA models on our TextVQA dataset.
Ranked #3 on
Visual Question Answering (VQA)
on VizWiz 2018
Pythia v0.1: the Winning Entry to the VQA Challenge 2018
We demonstrate that by making subtle but important changes to the model architecture and the learning rate schedule, fine-tuning image features, and adding data augmentation, we can significantly improve the performance of the up-down model on VQA v2. 0 dataset -- from 65. 67% to 70. 22%.
Ranked #12 on
Visual Question Answering (VQA)
on A-OKVQA
