Specialty-Oriented Generalist Medical AI for Chest CT Screening
- URL: http://arxiv.org/abs/2304.02649v4
- Date: Wed, 24 Apr 2024 17:10:34 GMT
- Title: Specialty-Oriented Generalist Medical AI for Chest CT Screening
- Authors: Chuang Niu, Qing Lyu, Christopher D. Carothers, Parisa Kaviani, Josh Tan, Pingkun Yan, Mannudeep K. Kalra, Christopher T. Whitlow, Ge Wang,
- Abstract summary: We propose the first-of-its-kind medical multimodal-multitask foundation model (M3FM) with application in lung cancer screening and related tasks.
M3FM consistently outperforms the state-of-the-art single-modal task-specific models.
As a specialty-oriented generalist medical AI model, M3FM paves the way for similar breakthroughs in other areas of medicine.
- Score: 14.31187762890342
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology. Fully mining such big data requires multitasking; otherwise, occult but important aspects may be overlooked, adversely affecting clinical management and population healthcare. Despite remarkable successes of AI in individual tasks with single-modal data, the progress in developing generalist medical AI remains relatively slow to combine multimodal data for multitasks because of the dual challenges of data curation and model architecture. The data challenge involves querying and curating multimodal structured and unstructured text, alphanumeric, and especially 3D tomographic scans on an individual patient level for real-time decisions and on a scale to estimate population health statistics. The model challenge demands a scalable and adaptable network architecture to integrate multimodal datasets for diverse clinical tasks. Here we propose the first-of-its-kind medical multimodal-multitask foundation model (M3FM) with application in lung cancer screening and related tasks. After we curated a comprehensive multimodal multitask dataset consisting of 49 clinical data types including 163,725 chest CT series and 17 medical tasks involved in LCS, we develop a multimodal question-answering framework as a unified training and inference strategy to synergize multimodal information and perform multiple tasks via free-text prompting. M3FM consistently outperforms the state-of-the-art single-modal task-specific models, identifies multimodal data elements informative for clinical tasks and flexibly adapts to new tasks with a small out-of-distribution dataset. As a specialty-oriented generalist medical AI model, M3FM paves the way for similar breakthroughs in other areas of medicine, closing the gap between specialists and the generalist.
Related papers
- MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation [40.9095393430871]
We introduce MedViLaM, a unified vision-language model towards a generalist model for medical data.
MedViLaM can flexibly encode and interpret various forms of medical data, including clinical language and imaging.
We present instances of zero-shot generalization to new medical concepts and tasks, effective transfer learning across different tasks, and the emergence of zero-shot medical reasoning.
arXiv Detail & Related papers (2024-09-29T12:23:10Z) - MultiMed: Massively Multimodal and Multitask Medical Understanding [41.160488390597905]
MultiMed is a benchmark designed to evaluate and enable large-scale learning across a wide spectrum of medical modalities and tasks.
It consists of 2.56 million samples across ten medical modalities such as medical reports, pathology, genomics, and protein data.
Using MultiMed, we conduct comprehensive experiments benchmarking state-of-the-art unimodal, multimodal, and multitask models.
arXiv Detail & Related papers (2024-08-22T18:41:36Z) - FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction [34.732561455987145]
We propose a unified healthcare prediction model, also named by textbfFlexCare, to flexibly accommodate incomplete multimodal inputs.
A task-agnostic multimodal information extraction module is presented to capture decorrelated representations of diverse intra- and inter-modality patterns.
Experimental results on multiple tasks from MIMIC-IV/MIMIC-CXR/MIMIC-NOTE datasets demonstrate the effectiveness of the proposed method.
arXiv Detail & Related papers (2024-06-17T12:03:10Z) - 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) - Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models [17.643421997037514]
We propose a novel framework that tackles both discriminative and generative multimodal medical tasks.
The learning of Med-MoE consists of three steps: multimodal medical alignment, instruction tuning and routing, and domain-specific MoE tuning.
Our model can achieve performance superior to or on par with state-of-the-art baselines.
arXiv Detail & Related papers (2024-04-16T02:35:17Z) - Towards Generalist Foundation Model for Radiology by Leveraging
Web-scale 2D&3D Medical Data [66.9359934608229]
This study aims to initiate the development of Radiology Foundation Model, termed as RadFM.
To the best of our knowledge, this is the first large-scale, high-quality, medical visual-language dataset, with both 2D and 3D scans.
We propose a new evaluation benchmark, RadBench, that comprises five tasks, including modality recognition, disease diagnosis, visual question answering, report generation and rationale diagnosis.
arXiv Detail & Related papers (2023-08-04T17:00:38Z) - LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical
Imaging via Second-order Graph Matching [59.01894976615714]
We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets.
We have collected approximately 1.3 million medical images from 55 publicly available datasets.
LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
arXiv Detail & Related papers (2023-06-20T22:21:34Z) - Towards Medical Artificial General Intelligence via Knowledge-Enhanced
Multimodal Pretraining [121.89793208683625]
Medical artificial general intelligence (MAGI) enables one foundation model to solve different medical tasks.
We propose a new paradigm called Medical-knedge-enhanced mulTimOdal pretRaining (MOTOR)
arXiv Detail & Related papers (2023-04-26T01:26:19Z) - Competence-based Multimodal Curriculum Learning for Medical Report
Generation [98.10763792453925]
We propose a Competence-based Multimodal Curriculum Learning framework ( CMCL) to alleviate the data bias and make best use of available data.
Specifically, CMCL simulates the learning process of radiologists and optimize the model in a step by step manner.
Experiments on the public IU-Xray and MIMIC-CXR datasets show that CMCL can be incorporated into existing models to improve their performance.
arXiv Detail & Related papers (2022-06-24T08:16:01Z) - Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
In hospitals, data are siloed to specific information systems that make the same information available under different modalities.
This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time.
We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
arXiv Detail & Related papers (2020-10-20T20:05:35Z)
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.