Health AI Developer Foundations (2411.15128v2)

Abstract: Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these challenges, we introduce Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes to accelerate building ML for health applications. The models cover various modalities and domains, including radiology (X-rays and computed tomography), histopathology, dermatological imaging, and audio. These models provide domain specific embeddings that facilitate AI development with less labeled data, shorter training times, and reduced computational costs compared to traditional approaches. In addition, we utilize a common interface and style across these models, and prioritize usability to enable developers to integrate HAI-DEF efficiently. We present model evaluations across various tasks and conclude with a discussion of their application and evaluation, covering the importance of ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting. This technical report will be updated over time as more modalities and features are added.

Health AI Developer Foundations: Enhancing the Development of Machine Learning Models in Healthcare

The paper "Health AI Developer Foundations" introduces the Health AI Developer Foundations (HAI-DEF), a pioneering initiative aimed at mitigating the challenges faced in developing ML models for healthcare applications. The work presented in this paper addresses the prevalent issues of costly, data-intensive, and resource-demanding processes associated with building robust ML models from scratch. HAI-DEF offers a suite of pre-trained, domain-specific foundation models alongside tools and equipment designed to facilitate the swift development of ML models in various healthcare domains.

These foundation models span multiple healthcare modalities, such as radiology, histopathology, dermatology, and audio, providing domain-specific embeddings that afford significant reductions in the need for labeled data, the duration of training times, and the computational cost typically required by traditional approaches. The HAI-DEF initiative utilizes a uniform interface across these models, encouraging seamless integration by developers and researchers alike.

Model Overview

HAI-DEF encompasses several distinct models, each tailored to different healthcare modalities:

1. CXR Foundation: Includes three models based on EfficientNet-L2 that employ supervised contrastive learning and image/text encoding techniques. These models demonstrate superior performance on zero-shot tasks and downstream classification tasks.
2. Path Foundation: Utilizes a Vision Transformer encoder trained with self-supervised learning on histopathology image patches. It incorporates pathology-specific techniques to remain agnostic of stain variations and generalize across different magnifications.
3. Derm Foundation: Employs a BiT ResNet-101x3 encoder fine-tuned on over 16K dermatology images for identifying skin conditions efficiently.
4. HeAR: Trained using a ViT audio encoder, it utilizes a Masked Autoencoder approach to handle health-related auditory data, achieving robust performance across diverse audio tasks.
5. CT Foundation: Provides embeddings ideal for classification tasks, employing VideoCoCa, a video-text model adapted from 2D Contrastive Captioners, trained on large datasets.

Model Evaluations

The empirical evaluations presented in the paper highlight the efficacy of HAI-DEF's foundation models in various data-limited classification tasks, often outperforming generic embeddings, thus underscoring superior data efficiency. Notably, the application of CXR models in tuberculosis detection demonstrated data efficiency gains, as models could reproduce clinician-equivalent results with minimal training data. Furthermore, the foundation models excel in generalization across tasks within their domains, reinforcing their robustness and versatility.

Implications and Future Developments

HAI-DEF significantly lowers the barriers to developing task-specific deep learning models in healthcare by furnishing pre-trained models that require less data and computational power. The impact of this work extends to diverse applications such as distinguishing sarcoma types and identifying neonatal radiology images, as demonstrated by researchers leveraging these resources.

From a theoretical standpoint, the initiative facilitates the exploration of AI’s utility across various healthcare aspects without the prerequisite of intensive computational resources. Practically, the models are made available through research endpoints, open-weight solutions, and containerized deployments, providing flexibility in adoption across different use cases and environments.

As the initiative progresses, future work will involve expanding HAI-DEF's suite to include more modalities and potentially integrate feedback loops for continuous model improvement. Moreover, the research community's input will be critical in identifying novel applications and refining existing models to maximize their efficacy in diverse healthcare settings.

In summary, HAI-DEF represents a critical advancement in healthcare ML by democratizing access and enabling efficient model development. The ongoing expansion and community engagement proposed by the authors will likely enhance the adoption of AI methodologies in healthcare, furthering innovation in clinical practices.