# Data-Centric Foundation Models in Computational Healthcare: A Survey

arXiv: https://arxiv.org/abs/2401.02458 · HTML: https://mlsanity.com/paper/2401.02458/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 174 of its 410 references that are in the map.
- First posted on arXiv 4 Jan 2024.

## Builds on (most cited first, 30 of 174)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)

## Built on by (most cited first, 27 of 27)

- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [A Comprehensive Survey of Large Language Models and Multimodal Large Language Models in Medicine](https://mlsanity.com/paper/2405.08603/) (2024)
- [MedBench: A Comprehensive, Standardized, and Reliable Benchmarking System for Evaluating Chinese Medical Large Language Models](https://mlsanity.com/paper/2407.10990/) (2024)
- [Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey](https://mlsanity.com/paper/2408.12880/) (2024)
- [Aligning Human Knowledge with Visual Concepts Towards Explainable Medical Image Classification](https://mlsanity.com/paper/2406.05596/) (2024)
- [Show and Segment: Universal Medical Image Segmentation via In-Context Learning](https://mlsanity.com/paper/2503.19359/) (2025)
- [PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning](https://mlsanity.com/paper/2403.08967/) (2024)
- [Think Twice to See More: Iterative Visual Reasoning in Medical VLMs](https://mlsanity.com/paper/2510.10052/) (2025)
- [Surgical Scene Understanding in the Era of Foundation AI Models: A Comprehensive Review](https://mlsanity.com/paper/2502.14886/) (2025)
- [Cost-effective Instruction Learning for Pathology Vision and Language Analysis](https://mlsanity.com/paper/2407.17734/) (2024)
- [OpenMEDLab: An Open-source Platform for Multi-modality Foundation Models in Medicine](https://mlsanity.com/paper/2402.18028/) (2024)
- [Mediator-Guided Multi-Agent Collaboration among Open-Source Models for Medical Decision-Making](https://mlsanity.com/paper/2508.05996/) (2025)
- [Towards the Autonomous Optimization of Urban Logistics: Training Generative AI with Scientific Tools via Agentic Digital Twins and Model Context Protocol](https://mlsanity.com/paper/2506.13068/) (2025)
- [Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging](https://mlsanity.com/paper/2506.18434/) (2025)
- [Anatomy-VLM: A Fine-grained Vision-Language Model for Medical Interpretation](https://mlsanity.com/paper/2511.08402/) (2025)
- [Clinical Note Bloat Reduction for Efficient LLM Use](https://mlsanity.com/paper/2604.16364/) (2026)
- [Empowering Cognitive Digital Twins with Generative Foundation Models: Developing a Low-Carbon Integrated Freight Transportation System](https://mlsanity.com/paper/2410.18089/) (2024)
- [MedForge: Building Medical Foundation Models Like Open Source Software Development](https://mlsanity.com/paper/2502.16055/) (2025)
- [Aligning Large Language Models with Healthcare Stakeholders: A Pathway to Trustworthy AI Integration](https://mlsanity.com/paper/2505.02848/) (2025)
- [Language-Enhanced Generative Modeling for Amyloid PET Synthesis from MRI and Blood Biomarkers](https://mlsanity.com/paper/2511.02206/) (2025)
- [DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging](https://mlsanity.com/paper/2601.13954/) (2026)
- [Brain Imaging Foundation Models, Are We There Yet? A Systematic Review of Foundation Models for Brain Imaging and Biomedical Research](https://mlsanity.com/paper/2506.13306/) (2025)
- [ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain](https://mlsanity.com/paper/2509.09130/) (2025)
- [Does medical specialization of VLMs enhance discriminative power?: A comprehensive investigation through feature distribution analysis](https://mlsanity.com/paper/2601.14774/) (2026)
- [Toward Clinically Ready Foundation Models in Medical Image Analysis: Adaptation Mechanisms and Deployment Trade-offs](https://mlsanity.com/paper/2603.14271/) (2026)
- [Enhancing Brain MRI Anomaly Detection and Reasoning with ROI Rethink and Synthetic Data](https://mlsanity.com/paper/2606.25894/) (2026)
- [Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning](https://mlsanity.com/paper/2606.31599/) (2026)

## Abstract (arXiv)

The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics. In this survey, we investigate a wide range of data-centric approaches in the FM era (from model pre-training to inference) towards improving the healthcare workflow. We discuss key perspectives in AI security, assessment, and alignment with human values. Finally, we offer a promising outlook on FM-based analytics to enhance patient outcomes and clinical workflows in the evolving landscape of healthcare and medicine. We provide an up-to-date list of healthcare-related foundation models and datasets at https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare.