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arxiv: 2310.00795 · v2 · submitted 2023-10-01 · 💻 cs.LG · cs.AI

Recent Advances in Generative AI for Healthcare Applications

Yasin Shokrollahi , Jose Colmenarez , Wenxi Liu , Sahar Yarmohammadtoosky , Matthew M. Nikahd , Pengfei Dong , Xianqi Li , Linxia Gu This is my paper

Pith reviewed 2026-05-24 06:24 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords generative AIhealthcarediffusion modelstransformer architecturesmedical imagingdrug designclinical decision support
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The pith

Generative AI led by diffusion models and transformers has enabled breakthroughs in medical imaging, protein prediction, and clinical tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This review synthesizes recent applications of generative AI in healthcare. It focuses on diffusion models and transformer architectures and their role in medical image tasks, protein structure work, and clinical support functions. The synthesis covers progress in diagnosis assistance, documentation, coding, and drug-related molecular tasks. A reader would care because these applications could change how medical data is processed and decisions are made. The paper also notes current limits and suggests paths for further work.

Core claim

Generative AI, led by diffusion models and transformer architectures, has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. These innovations have enhanced clinical diagnosis, data reconstruction, and drug synthesis.

What carries the argument

Diffusion models and transformer architectures applied across medical imaging, protein prediction, and clinical workflow tasks.

If this is right

  • Medical imaging workflows gain new tools for reconstruction, translation, and classification.
  • Protein structure prediction benefits from generative approaches that improve accuracy.
  • Clinical documentation, coding, and decision support see efficiency gains.
  • Drug design and molecular representation tasks become more automated and targeted.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Wider use in hospitals could shift training requirements for medical staff toward AI oversight skills.
  • Privacy rules around patient data may limit how broadly these models can be trained in practice.
  • The same model families might extend to non-imaging domains such as electronic health record forecasting.
  • Validation studies focused on real-world deployment outcomes would be a natural next measurement step.

Load-bearing premise

The review assumes that the body of cited literature provides a representative and unbiased sample of the field without systematic omission of negative results or over-representation of positive ones.

What would settle it

A meta-analysis that identifies many high-quality studies showing no measurable gains from these models in the listed healthcare areas would falsify the claim of significant breakthroughs.

Figures

Figures reproduced from arXiv: 2310.00795 by Jose Colmenarez, Linxia Gu, Matthew M. Nikahd, Pengfei Dong, Sahar Yarmohammadtoosky, Wenxi Liu, Xianqi Li, Yasin Shokrollahi.

Figure 2
Figure 2. Figure 2: Timeline of Generative AI Family Types. Since diffusion and transformer-based models have outperformed other types of models/architectures and are increasingly used in the healthcare industry ( [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Generative AI in healthcare. 1.1. Rationale and Distinctiveness of Our Review: In healthcare, generative AI has made significant progress over the years. Many experts have written detailed reviews about deep generative AI models designed specifically for healthcare purposes. These reviews include the studies by (Bohr and Memarzadeh 2020), (AlAmir and AlGhamdi 2022), (Ali et al. 2022), (Kazerouni et al. 202… view at source ↗
Figure 4
Figure 4. Figure 4: (left) We observe the representation of self-attention; (right) the illustration delineates the configuration of Multi-Head Attention, which comprises multiple concurrent attention layers (image by (Vaswani et al. 2017)). The process under discussion involves an encoder utilizing a mechanism known as multi-head attention, which extends beyond the singular contextual comprehension found in self-attention. S… view at source ↗
Figure 5
Figure 5. Figure 5: The proposed taxonomy for diffusion-based models in health care in six sub-fields, (I) Image Reconstruction 1. (Özbey et al. 2023), 2. (Xie and Li 2022), (Yang et al. 2024), (II) Image to Image Translation 3. (Lyu and Wang 2022), 4. (Özbey et al. 2023), (III) Image Generation 5. (Müller-Franzes et al. 2023), 6. (Pan et al. 2023), (Sun et al. 2024), (IV) Image Classification 7. (H.-J. Oh and Jeong 2023), 8.… view at source ↗
Figure 6
Figure 6. Figure 6: Results are shown for (a) T1-weighted acquisitions in the IXI dataset and (b) FLAIR-weighted acquisitions in the fastMRI dataset. Reconstructed images are given along with the reference image derived from fully sampled acquisitions, and zoom-in windows and arrows are included to highlight differences among methods. LORAKS and GAN prior show high noise amplification, rGAN shows residual aliasing, and MoDL s… view at source ↗
Figure 7
Figure 7. Figure 7: SynDiff showcased its capability for MRI contrast conversions on the BRATS dataset (Menze et al. 2014). For illustrative purposes, source images, synthesized outputs, and the actual reference images are presented for the following tasks: a) T1 to T2 and b) T2 to FLAIR (Fluid Attenuation Inversion Recovery). The visualization scales utilized are a) [0, 0.75] and b) [0, 0.80]. SynDiff effectively minimizes n… view at source ↗
Figure 8
Figure 8. Figure 8: MT-DDPM Framework's Diffusion Methodology (a): Medical imagery is progressively transformed into pure Gaussian noise through incremental noise addition in the forward diffusion. For the backward process, a system is tasked to filter the Gaussian noise back into a pristine image continuously. (b): MT-DDPM Network Design: This network uses a balanced encoder-decoder design to master the backward process. The… view at source ↗
Figure 9
Figure 9. Figure 9: An overview of the DiffMIC framework includes (a) The forward process during the training phase (b) The reverse process for inference. (c) The DCG Model 𝜏𝐷 directs the diffusion using dual priors from the raw image and ROIs (Y. Yang et al. 2023). 3.1.4. Image Classification [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: The proposed taxonomy for Transformer-based models in health care in seven sub-fields, (I) protein structure prediction 1. (Vig et al. 2020), 2. (Behjati et al. 2022), 3. (Abdine et al. 2023), 4. (Boadu, Cao, and Cheng 2023), 5. (Y. Cao and Shen 2021), 6. (Geffen, Ofran, and Unger 2022), 7. (Castro et al [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Structure of the Deep-Learning Line Classification Model. Textual and layout characteristics of each line are embedded to generate a unique representation for each line, which is subsequently contextualized using a four-layer Transformer that employs self-attention with relative position information. Finally, the representations are classified using a linear layer and a SoftMax function to determine the p… view at source ↗
Figure 12
Figure 12. Figure 12: Structure of the Deep-Learning Line Classification Model. Textual and layout characteristics of each line are embedded to generate a unique representation for each line, which is subsequently contextualized using a four-layer Transformer that employs self-attention with relative position information. Finally, the representations are classified using a linear layer and a SoftMax function to determine the p… view at source ↗
Figure 13
Figure 13. Figure 13: Schematic of the Proposed Methodology. The visual search patterns of radiologists on chest radiographs serve as the initial input for training a global-focal teacher network, denoted as Human Visual Attention Training. Subsequently, this pre-trained teacher network instructs the global-focal student network to acquire visual attention utilizing a newly devised Visual Attention Loss. Implementing the stude… view at source ↗
Figure 15
Figure 15. Figure 15: The proposed architecture of the Prot2Text framework is designed for predicting protein function descriptions in the free-form text (picture by (Abdine et al. 2023)). Recent advancements in protein structure prediction and function annotation have been driven by innovative techniques such as TransFun (Boadu, Cao, and Cheng 2023), TALE (Y. Cao and Shen 2021), DistilProtBert (Geffen, Ofran, and Unger 2022),… view at source ↗
Figure 16
Figure 16. Figure 16: Pipeline for training and generation using the MolGPT model (Bagal et al. 2022). (K. Huang et al. 2021) proposed MolTrans, an approach that combines a knowledge-inspired sub￾structural pattern mining algorithm, an interaction modeling module, and an enhanced transformer encoder for more precise and interpretable drug-target interaction (DTI) predictions. This approach outperformed leading-edge baseline mo… view at source ↗
read the original abstract

The rapid advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across various sectors, notably in healthcare. In particular, generative AI-led by diffusion models and transformer architectures-has enabled significant breakthroughs in medical imaging (including image reconstruction, image-to-image translation, generation, and classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding, and billing, as well as drug design and molecular representation. These innovations have enhanced clinical diagnosis, data reconstruction, and drug synthesis. This review paper aims to offer a comprehensive synthesis of recent advances in healthcare applications of generative AI, with an emphasis on diffusion and transformer models. Moreover, we discuss current capabilities, highlight existing limitations, and outline promising research directions to address emerging challenges. Serving as both a reference for researchers and a guide for practitioners, this work offers an integrated view of the state of the art, its impact on healthcare, and its future potential.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. This review paper claims that generative AI, led by diffusion models and transformer architectures, has enabled significant breakthroughs across nine healthcare domains: medical imaging (reconstruction, translation, generation, classification), protein structure prediction, clinical documentation, diagnostic assistance, radiology interpretation, clinical decision support, medical coding and billing, and drug design/molecular representation. It positions itself as a comprehensive synthesis of recent advances, with discussion of current capabilities, limitations, and future research directions, serving as a reference for researchers and guide for practitioners.

Significance. A well-executed survey with transparent selection criteria could provide a useful integrated view of the state of the art in an active area. However, the headline narrative of widespread 'significant breakthroughs' rests entirely on the representativeness and balance of the cited literature; without that, the synthesis does not add substantial new insight beyond existing individual papers.

major comments (1)
  1. [Abstract / Introduction] Abstract and Introduction: the central claim that diffusion and transformer models have produced 'significant breakthroughs' in the nine listed domains is supported solely by selection and interpretation of prior publications. No explicit literature-search protocol, inclusion/exclusion criteria, date range, or handling of negative/null results is described, making it impossible to evaluate whether the cited works constitute a representative sample or over-represent positive demonstrations.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment below and will revise the manuscript to improve transparency on literature selection.

read point-by-point responses

  1. Referee: [Abstract / Introduction] Abstract and Introduction: the central claim that diffusion and transformer models have produced 'significant breakthroughs' in the nine listed domains is supported solely by selection and interpretation of prior publications. No explicit literature-search protocol, inclusion/exclusion criteria, date range, or handling of negative/null results is described, making it impossible to evaluate whether the cited works constitute a representative sample or over-represent positive demonstrations.

    Authors: We agree that an explicit description of the literature selection process is needed for transparency. Although the paper is framed as a narrative synthesis of recent advances rather than a formal systematic review, we will add a dedicated subsection in the Introduction (or a new 'Methods' section) detailing the approach taken. This will include: search databases (PubMed, arXiv, Google Scholar), date range (primarily 2020–2023), keywords (combinations of 'diffusion model', 'transformer', 'generative AI' with each of the nine healthcare domains), inclusion criteria (peer-reviewed or preprint works reporting empirical applications or benchmarks), and exclusion criteria (purely theoretical works or non-generative methods). We will also expand the existing limitations discussion to note potential publication bias and reference studies highlighting challenges or null results where relevant. These changes will allow readers to better evaluate the synthesis. revision: yes

Circularity Check

0 steps flagged

No circularity: survey of external literature only

full rationale

This is a review paper whose central claims consist of summaries of cited external publications on diffusion models, transformers, and their healthcare applications. No equations, fitted parameters, derivations, or internal predictions appear in the abstract or described structure. No self-citation is invoked as a load-bearing uniqueness theorem or ansatz. The representativeness concern raised by the skeptic is a question of selection bias, not a reduction of any claimed result to the paper's own inputs by construction. Therefore the derivation chain is empty and the circularity score is 0.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a literature review with no new mathematical content, free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5718 in / 997 out tokens · 18645 ms · 2026-05-24T06:24:52.048689+00:00 · methodology

discussion (0)

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Reference graph

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