MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants (2412.12661v1)

Abstract: Recent advancements in mixed-modal generative models have enabled flexible integration of information across image-text content. These models have opened new avenues for developing unified biomedical assistants capable of analyzing biomedical images, answering complex questions about them, and predicting the impact of medical procedures on a patient's health. However, existing resources face challenges such as limited data availability, narrow domain coverage, and restricted sources (e.g., medical papers). To address these gaps, we present MedMax, the first large-scale multimodal biomedical instruction-tuning dataset for mixed-modal foundation models. With 1.47 million instances, MedMax encompasses a diverse range of tasks, including multimodal content generation (interleaved image-text data), biomedical image captioning and generation, visual chatting, and report understanding. These tasks span diverse medical domains such as radiology and histopathology. Subsequently, we fine-tune a mixed-modal foundation model on the MedMax dataset, achieving significant performance improvements: a 26% gain over the Chameleon model and an 18.3% improvement over GPT-4o across 12 downstream biomedical visual question-answering tasks. Additionally, we introduce a unified evaluation suite for biomedical tasks, providing a robust framework to guide the development of next-generation mixed-modal biomedical AI assistants.

MedMax: Mixed-Modal Instruction Tuning for Training Biomedical Assistants

The paper presents a comprehensive paper on the development of MedMax, which is touted as the pioneering large-scale multimodal biomedical instruction-tuning dataset. The dataset is designed to enhance the capability of mixed-modal foundation models in the biomedical domain. MedMax consists of 1.47 million instances, encompassing a diverse array of tasks including multimodal content generation with interleaved image-text data, biomedical image captioning and generation, visual chatting, and report understanding. These tasks primarily span medical domains such as radiology and histopathology.

The authors outline the current limitations in existing resources for developing biomedical assistants, such as limited data availability, narrow domain coverage, and the dependence on restricted sources like medical papers. To mitigate these limitations, MedMax is introduced, with the dataset supporting tasks beyond conventional datasets like VQA-RAD, SLAKE, and PathVQA. Unlike its predecessors, MedMax allows for a wide array of complex biomedical tasks due to its instruction-tuning methodology, which finetunes a mixed-modal foundation model, thereby significantly enhancing the model's performance.

This paper achieved substantial performance improvements when finetuning a mixed-modal foundation model with the MedMax dataset. The authors report a 26% gain over the Chameleon model and an 18.3% improvement over GPT-4o across twelve downstream biomedical visual question-answering tasks. Such significant numerical results underscore the efficacy of the MedMax dataset in advancing the domain of mixed-modal biomedical AI.

Further contributing to the utility of this work, the paper introduces a unified evaluation suite for biomedical tasks. This suite provides a versatile framework to guide the development of the next generation of mixed-modal biomedical AI. The evaluation tasks include visual question answering, image captioning and generation, visual chat, and report understanding, offering a comprehensive measure of the models' performance post-tuning with MedMax.

The implications of this research are noteworthy, both practically and theoretically. Practically, MedMax serves as a high-quality dataset that bridges the gap in mixed-modal biomedical model training, equipping AI models to better interpret, interact with, and generate multimedia biomedical content. Theoretically, this work paves the way for future developments in AI with multimodal interaction capabilities, emphasizing the importance of diverse and comprehensive datasets for training.

The paper closes by indicating potential future directions, noting the possibility of integrating more varied biomedical tasks and exploring mixed-modal interactions involving multiple images and modalities. This research thus establishes a foundational step towards developing more capable and versatile biomedical AI systems. The results indicate promising advancements in multimodal AI performance, suggesting the potential for enhanced medical diagnosis, prognosis, and treatment planning through improved AI-assisted methods.