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REVIEW 4 major objections 5 minor 1 cited by

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

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This scoping review of 144 papers claims generative AI in medicine is shifting from text-only large language models to multimodal systems that combine imaging, text, and structured data in a single model.

desk verdict Useful scoping review of medical generative AI, but the 'shift to multimodal' claim rests on a hand-curated subset and an off-by-one PRISMA count; worth refereeing after a revision. read the letter →

arxiv 2502.09242 v1 pith:2RQJO6RY submitted 2025-02-13 cs.AI

Lukas Buess , Matthias Keicher , Nassir Navab , Andreas Maier , Soroosh Tayebi Arasteh This is my paper · ORCID
classification cs.AI
keywords generativeAIinmedicinemultimodallargelanguagemodelsscopingreviewclinicalevaluationmetricsradiologyreportgenerationmedicaldatasetsgeneralistcontrastivelearning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This scoping review claims that generative AI in medicine is moving from text-only large language models to multimodal systems that combine images, text, and structured data in one model. The authors argue this shift is already visible in diagnostic support, medical report generation, drug discovery, and conversational AI, and that evaluation practice has not kept up. To ground this claim they systematically screened 4,384 records and included 144 papers published up to the end of 2024. The review matters because it identifies where clinical deployment is likely to succeed and where it will stall: data heterogeneity, interpretability, ethics, and real-world validation remain open problems.

What carries the argument

The central objects are the two multimodal architectures the review uses to organize the field: contrastive language-image pretraining (CLIP) models, which align different modalities in a shared embedding space, and multimodal large language models (MLLMs), which encode images or other non-text data into the language model's embedding space. These carry the argument by defining what multimodal means and by structuring the taxonomy of methods, datasets, and applications. A second mechanism is the pairing of evaluation metrics: lexical metrics such as BLEU and ROUGE versus clinically grounded metrics such as entity-based scoring, grounded factual checks, and error-notation frameworks, which the review uses to show that evaluation lags behind model development.

What would settle it

Count the share of included papers that integrate at least two modalities by publication year; if the share does not rise across 2022 to 2024, the claimed shift is an artifact of curation rather than a property of the literature.

Watch

Extended reading notes

Core claim

The paper's central finding is that the center of gravity of medical generative AI has shifted from unimodal large language models to multimodal models, organized around two architectural families: contrastive models that align images with text in a shared embedding space, and multimodal large language models that project non-text features into the language model's embedding space. This shift is documented across 144 included papers and appears in a growing family of generalist models that handle classification, segmentation, report generation, and visual question answering within one architecture. The authors also find that standard lexical evaluation metrics such as BLEU and ROUGE miss clinical correctness, and that newer clinically grounded metrics, while promising, are not yet standardized across sites and specialties.

Load-bearing premise

The review's trend claims depend on the representativeness of the curated set of 144 papers, since 83 entered through a manual search described as capturing recent and high-impact work and the authors deliberately balanced inclusion across prevalent fields.

Editorial extensions

If this is right

  • Clinical AI development will increasingly center on models that ingest images, text, and structured data together rather than text-only assistants.
  • Evaluation of medical generative models should combine lexical metrics with clinically grounded metrics that check factual correctness and relevance.
  • Dataset construction should move beyond radiology-centric resources toward other specialties and modalities to avoid generalization failures.
  • Generalist models that unify multiple tasks and modalities are becoming the default architectural direction for medical AI.
  • Synthetic image generation will be used to augment scarce data and simulate rare conditions, provided its clinical utility is validated on downstream tasks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: If the field's center of gravity is indeed shifting to multimodal models, text-only clinical language models may become components of larger systems rather than standalone products.
  • Editorial inference: The review's selection method makes the shift claim sensitive to curation, so a formal bibliometric analysis of all published medical AI abstracts would test whether the multimodal trend is a property of the literature or an artifact of inclusion choices.
  • Editorial inference: Clinical adoption may hinge less on model capability than on the availability of evaluation benchmarks that predict real-world diagnostic utility, a gap the review identifies but does not quantify.
  • Editorial inference: The heavy representation of radiology suggests the next bottleneck is data: without analogous multimodal datasets in pathology, genomics, and primary care, the shift to multimodal AI will proceed unevenly across medicine.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This scoping review (arXiv:2502.09242) applies PRISMA-ScR guidance to survey generative AI in medicine, covering text-only LLMs, multimodal CLIP and MLLM architectures, associated datasets, and evaluation metrics. The authors report screening 4,384 database records and 84 additional records from manual searches, ultimately including 144 papers. The review synthesizes these papers in six tables organized by application, dataset, and evaluation approach, and the abstract and discussion make a central claim of a field-level 'shift from unimodal to multimodal approaches' in medical AI. The paper also describes persistent challenges such as data heterogeneity, interpretability, and evaluation gaps.

Significance. If the reported trend claim were reliable, the review would offer a valuable, up-to-date map of a rapidly moving field, with useful taxonomies of models, datasets, and clinical evaluation metrics. Its strengths include the reproducible database queries in Supplementary Table S.2, the explicit PRISMA-ScR checklist, and a structured categorization of 144 primary papers into coherent categories (Tables 1-6). The qualitative discussion of evaluation metrics (Section 6) is informative and reasonably current, and the identification of radiology-heavy dataset bias is a credible observation. However, the headline 'shift' claim depends on a literature-selection process that includes an unspecified manual search and a deliberate balancing step, which the reported methodology does not adequately support as a population-level trend. The review's descriptive synthesis remains useful regardless, but the central claim needs substantial clarification and additional transparency about how the included set was curated.

major comments (4)
  1. [Section 3 and Figure 2] The reported screening numbers are internally inconsistent. The text says 2,656 articles were excluded during title/abstract screening, while Figure 2 reports 2,657 excluded records. The text also states that 83 papers came from manual searches and that the total is 144, but 60 database papers plus 83 manual papers gives 143; Figure 2 shows 84 records from 'other methods' (1 website + 83 citations), which would give 144. These discrepancies affect the credibility of the PRISMA-ScR flow and must be reconciled before the review can be considered reproducible.
  2. [Sections 2.3-2.4 and Abstract] The central claim of a 'shift from unimodal to multimodal approaches' is not supported by the described selection design. The database search was constructed as two targeted subsearches, one for text-only LLMs and one for multimodal models, so the retrieved set is a union of two intentionally defined categories rather than a sample from which prevalence can be inferred. Moreover, 83 of the 144 included papers (58%) came from a manual search described only as capturing 'recent and high-impact publications' (Section 2.3), and Section 2.4 states that inclusion was deliberately balanced 'to ensure proportional inclusion from prevalent fields.' Under this design, the observed preponderance of multimodal papers could be an artifact of the inclusion rules. To keep the claim, the authors should either restrict all trend statements to the curated set with an explicit caveat that the set is not a random sample, or provide a sensitivity analysis based only on database-derived papers and a prespecified manual search protocol.
  3. [Section 2.4] The balancing criterion 'proportional inclusion from prevalent fields, such as X-ray report generation' is too vague to be a reproducible selection rule. The authors should specify how 'prevalent' was operationalized, who made the balancing decisions, what data were used to determine prevalence, and how many candidate papers were included or excluded because of this rule. Without this detail, a reader cannot determine whether the resulting 144-paper corpus is representative of the literature or of the authors' interests.
  4. [Sections 5 and 7] The 'shift' claim is presented as a temporal trend, but no time-based analysis is shown. The review spans 2020-2024, yet no table or figure reports the distribution of unimodal versus multimodal papers by publication year, nor is there an analysis of whether the share of multimodal papers changed over time within the included set. If the authors intend to claim a shift, they need to provide direct evidence of changing proportions over time, or explicitly reframe the claim as a comparison between two categories present in the included set.
minor comments (5)
  1. [Figure 3 caption] The caption contains a typo: 'ebbedding space' should be 'embedding space.'
  2. [Section 2.1] The eligibility criteria state that 'manually selected preprints with high relevance and potential impact' were included, but the criteria do not define 'relevance' or 'impact.' Consider providing an operational definition.
  3. [Section 6, Table 6] The column header 'Application' is so broad that entries like 'Report evaluation' and 'Conversation evaluation' are not fully descriptive; consider subdividing by evaluation target (e.g., text generation, image generation, calibration).
  4. [Section 5.4, Table 5] Some dataset sizes are given as raw counts without a clear unit (e.g., '224K triplets' vs. '25M pairs'); standardizing the unit label (e.g., 'image-text pairs,' 'patients') would improve clarity.
  5. [References] Several references are to arXiv preprints without a note of peer-reviewed status, which conflicts with the stated inclusion criterion of 'peer-reviewed conference and journal publications, alongside manually selected preprints.' Clarify which included papers are preprints and how they were evaluated for 'relevance and potential impact.'

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the review synthesizes external literature, and its trend claims, while methodologically vulnerable, are not equivalent to the search design by construction.

full rationale

This is a scoping review, not a derivation with fitted parameters or equations, so the core circularity patterns do not apply. The central claim of a 'shift from unimodal to multimodal approaches' is a narrative synthesis of 144 selected papers, and the selection process did not force that conclusion: the two database subsearches were designed to capture both text-only LLMs and multimodal models, and the manual search aimed at 'recent and high-impact publications' with 'proportional inclusion from prevalent fields.' Those choices may create selection bias, and the PRISMA flow has a minor numerical inconsistency, but bias and reporting inconsistency are not circularity in the sense of a result being equivalent to its inputs by construction. The paper's self-citations (e.g., [2], [10], [16], [34], [73], [91], [104], [133], [142]) appear as illustrative examples in tables and narrative descriptions; none is load-bearing for the review's overall conclusions, and none imports a uniqueness theorem or forbids alternatives. The methodological limitations are acknowledged in Section 7, including 'overrepresentation of radiology-focused datasets and models,' which further indicates the authors did not present the selection as a derivation. Therefore no circular step is identifiable, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities: the paper introduces no model, method, or quantity. The load-bearing premises are methodological: representative literature selection and effective search. These are not backed by external evidence beyond the paper's own curation.

assumptions (3)
  • domain assumption The included set of 144 papers, including manually selected preprints, is representative of the research landscape in generative AI for medicine.
    Section 2.4 states that inclusion aimed for proportional representation across prevalent fields, and Section 3 reports that 83 of 144 papers came from non-database manual searches; the trend claims depend on this representativeness.
  • domain assumption The search strings with NOT 'ChatGPT' and NOT 'Review' filters identify the intended literature without systematic bias.
    Supplementary Table S.2 defines the queries; the review's conclusions about the field presuppose these queries effectively sample the literature.
  • domain assumption The evaluation metrics discussed are representative of current practices in medical generative AI.
    Section 6 generalizes about evaluation practices based on the included subset, which is heavily radiology-oriented, a point the authors acknowledge in the Discussion.

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Cite this review

Pith. "Pith review of From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine." pith.science (2026). https://pith.science/paper/2RQJO6RY

@misc{pith2026250209242,
  author       = {Pith},
  title        = {Pith review of: From large language models to multimodal AI: A scoping review on the potential of generative AI in medicine},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2RQJO6RY}},
  note         = {Machine review of arXiv:2502.09242}
}
read the original abstract

Generative artificial intelligence (AI) models, such as diffusion models and OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy and automating clinical workflows. The field has advanced rapidly, evolving from text-only large language models for tasks such as clinical documentation and decision support to multimodal AI systems capable of integrating diverse data modalities, including imaging, text, and structured data, within a single model. The diverse landscape of these technologies, along with rising interest, highlights the need for a comprehensive review of their applications and potential. This scoping review explores the evolution of multimodal AI, highlighting its methods, applications, datasets, and evaluation in clinical settings. Adhering to PRISMA-ScR guidelines, we systematically queried PubMed, IEEE Xplore, and Web of Science, prioritizing recent studies published up to the end of 2024. After rigorous screening, 144 papers were included, revealing key trends and challenges in this dynamic field. Our findings underscore a shift from unimodal to multimodal approaches, driving innovations in diagnostic support, medical report generation, drug discovery, and conversational AI. However, critical challenges remain, including the integration of heterogeneous data types, improving model interpretability, addressing ethical concerns, and validating AI systems in real-world clinical settings. This review summarizes the current state of the art, identifies critical gaps, and provides insights to guide the development of scalable, trustworthy, and clinically impactful multimodal AI solutions in healthcare.

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.