Pith. sign in

REVIEW 4 cited by

Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.03264 v1 pith:RS3GQ55S submitted 2024-04-04 cs.CY cs.AI

Yuting He , Fuxiang Huang , Xinrui Jiang , Yuxiang Nie , Minghao Wang , Jiguang Wang , Hao Chen This is my paper · ORCID
classification cs.CYcs.AI
keywords healthcarefuturechallengesdevelopmentfoundationsurveycurrentdata
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Foundation model, which is pre-trained on broad data and is able to adapt to a wide range of tasks, is advancing healthcare. It promotes the development of healthcare artificial intelligence (AI) models, breaking the contradiction between limited AI models and diverse healthcare practices. Much more widespread healthcare scenarios will benefit from the development of a healthcare foundation model (HFM), improving their advanced intelligent healthcare services. Despite the impending widespread deployment of HFMs, there is currently a lack of clear understanding about how they work in the healthcare field, their current challenges, and where they are headed in the future. To answer these questions, a comprehensive and deep survey of the challenges, opportunities, and future directions of HFMs is presented in this survey. It first conducted a comprehensive overview of the HFM including the methods, data, and applications for a quick grasp of the current progress. Then, it made an in-depth exploration of the challenges present in data, algorithms, and computing infrastructures for constructing and widespread application of foundation models in healthcare. This survey also identifies emerging and promising directions in this field for future development. We believe that this survey will enhance the community's comprehension of the current progress of HFM and serve as a valuable source of guidance for future development in this field. The latest HFM papers and related resources are maintained on our website: https://github.com/YutingHe-list/Awesome-Foundation-Models-for-Advancing-Healthcare.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Feedback-Driven Vision-Language Alignment with Minimal Human Supervision

    cs.CV 2025-01 conditional novelty 6.0 of 10

    SVP uses self-captioning with grounding feedback and a scoring filter to adapt vision-language models with about 1,000 images, improving captioning, referring, object recall, and hallucination control.

  2. Vector Contrastive Learning For Pixel-Wise Pretraining In Medical Vision

    cs.CV 2025-06 conditional novelty 5.0 of 10

    COVER reformulates contrastive learning as vector regression of geometric displacement fields and reports state-of-the-art self-supervised pretraining results across 2D and 3D medical imaging tasks.

  3. Anatomy-Guided Radiology Report Generation with Pathology-Aware Regional Prompts

    cs.CV 2024-11 conditional novelty 5.0 of 10

    A report generation pipeline that uses detected pathologies mapped to anatomical regions as prompt tokens improves several NLG and clinical efficacy metrics on MIMIC-CXR.

  4. From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine

    cs.AI 2025-02 conditional novelty 3.0 of 10

    A PRISMA-ScR scoping review of 144 studies finds the field shifting from text-only LLMs to multimodal AI in medicine, with evaluation and data diversity still the main bottlenecks.

Pith tools