One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence

• URL: http://arxiv.org/abs/2506.10157v2
• Date: Mon, 29 Sep 2025 12:31:15 GMT
• Title: One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence
• Authors: Michelle M. Li, Ben Y. Reis, Adam Rodman, Tianxi Cai, Noa Dagan, Ran D. Balicer, Joseph Loscalzo, Isaac S. Kohane, Marinka Zitnik,
• Abstract summary: Context switching enables medical AI to adapt across specialties, populations, and geographies.<n>It requires advances in data design, model architectures, and evaluation frameworks.
• Score: 16.450764388516244
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Their adaptation to new populations, specialties, or care settings often relies on fine-tuning, prompting, or retrieval from external knowledge bases. These strategies can scale poorly and risk contextual errors: outputs that appear plausible but miss critical patient or situational information. We envision context switching as a solution. Context switching adjusts model reasoning at inference without retraining. Generative models can tailor outputs to patient biology, care setting, or disease. Multimodal models can reason on notes, laboratory results, imaging, and genomics, even when some data are missing or delayed. Agent models can coordinate tools and roles based on tasks and users. In each case, context switching enables medical AI to adapt across specialties, populations, and geographies. It requires advances in data design, model architectures, and evaluation frameworks, and establishes a foundation for medical AI that scales to infinitely many contexts while remaining reliable and suited to real-world care.

Related papers

• Distribution-Based Masked Medical Vision-Language Model Using Structured Reports [9.306835492101413]
  Medical image-text pre-training aims to align medical images with clinically relevant text to improve model performance on various downstream tasks.<n>This work introduces an uncertainty-aware medical image-text pre-training model that enhances generalization capabilities in medical image analysis.
  arXiv  Detail & Related papers  (2025-07-29T13:31:24Z)
• SilVar-Med: A Speech-Driven Visual Language Model for Explainable Abnormality Detection in Medical Imaging [1.220481237642298]
  We introduce an end-to-end speech-driven medical VLM, SilVar-Med, a multimodal medical image assistant.<n>We focus on the interpretation of the reasoning behind each prediction of medical abnormalities with a proposed reasoning dataset.<n>We believe this work will advance the field of medical AI by fostering more transparent, interactive, and clinically viable diagnostic support systems.
  arXiv  Detail & Related papers  (2025-04-14T18:51:37Z)
• From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine [40.23383597339471]
  multimodal AI is capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model.<n>This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings.<n>Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI.
  arXiv  Detail & Related papers  (2025-02-13T11:57:51Z)
• A Survey of Medical Vision-and-Language Applications and Their Techniques [48.268198631277315]
  Medical vision-and-language models (MVLMs) have attracted substantial interest due to their capability to offer a natural language interface for interpreting complex medical data. Here, we provide a comprehensive overview of MVLMs and the various medical tasks to which they have been applied. We also examine the datasets used for these tasks and compare the performance of different models based on standardized evaluation metrics.
  arXiv  Detail & Related papers  (2024-11-19T03:27:05Z)
• Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
  We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
  arXiv  Detail & Related papers  (2024-11-16T18:19:53Z)
• Towards Holistic Disease Risk Prediction using Small Language Models [2.137491464843808]
  We introduce a framework that connects small language models to multiple data sources, aiming to predict the risk of various diseases simultaneously. Our experiments encompass 12 different tasks within a multitask learning setup.
  arXiv  Detail & Related papers  (2024-08-13T15:01:33Z)
• Evaluation of Language Models in the Medical Context Under Resource-Constrained Settings [10.39989311209284]
  We have conducted a comprehensive survey of language models in the medical field. We evaluated a subset of these for medical text classification and conditional text generation. The results reveal remarkable performance across the tasks and evaluated, underscoring the potential of certain models to contain medical knowledge.
  arXiv  Detail & Related papers  (2024-06-24T12:52:02Z)
• Dr-LLaVA: Visual Instruction Tuning with Symbolic Clinical Grounding [53.629132242389716]
  Vision-Language Models (VLM) can support clinicians by analyzing medical images and engaging in natural language interactions. VLMs often exhibit "hallucinogenic" behavior, generating textual outputs not grounded in contextual multimodal information. We propose a new alignment algorithm that uses symbolic representations of clinical reasoning to ground VLMs in medical knowledge.
  arXiv  Detail & Related papers  (2024-05-29T23:19:28Z)
• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models [72.8965643836841]
  We introduce XrayGPT, a novel conversational medical vision-language model.<n>It can analyze and answer open-ended questions about chest radiographs.<n>We generate 217k interactive and high-quality summaries from free-text radiology reports.
  arXiv  Detail & Related papers  (2023-06-13T17:59:59Z)
• Almanac: Retrieval-Augmented Language Models for Clinical Medicine [1.5505279143287174]
  We develop Almanac, a large language model framework augmented with retrieval capabilities for medical guideline and treatment recommendations. Performance on a novel dataset of clinical scenarios evaluated by a panel of 5 board-certified and resident physicians demonstrates significant increases in factuality.
  arXiv  Detail & Related papers  (2023-03-01T02:30:11Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine [5.126042819606137]
  We focus on three key methodological approaches that address some of the particular challenges in AI-driven medical decision making. Domain adaptation and transfer learning enable AI models to be trained and applied across multiple domains. Federated learning enables learning large-scale models without exposing sensitive personal health information.
  arXiv  Detail & Related papers  (2022-11-17T03:32:00Z)
• Self-supervised Multi-modal Training from Uncurated Image and Reports Enables Zero-shot Oversight Artificial Intelligence in Radiology [31.045221580446963]
  We present a model dubbed Medical Cross-attention Vision-Language model (Medical X-VL) Our model enables various zero-shot tasks for oversight AI, ranging from the zero-shot classification to zero-shot error correction. Our method was especially successful in the data-limited setting, suggesting the potential widespread applicability in medical domain.
  arXiv  Detail & Related papers  (2022-08-10T04:35:58Z)
• Towards more patient friendly clinical notes through language models and ontologies [57.51898902864543]
  We present a novel approach to automated medical text based on word simplification and language modelling. We use a new dataset pairs of publicly available medical sentences and a version of them simplified by clinicians. Our method based on a language model trained on medical forum data generates simpler sentences while preserving both grammar and the original meaning.
  arXiv  Detail & Related papers  (2021-12-23T16:11:19Z)
• Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
  We propose a deep co-attention network for multi-view subspace learning. It aims to extract both the common information and the complementary information in an adversarial setting. In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
  arXiv  Detail & Related papers  (2021-02-15T18:46:44Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.