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REVIEW 4 major objections 6 minor 66 references

Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A systematic review of 19 studies claims LLMs can assist rare-disease diagnosis from text, while true multimodal integration remains the open frontier.

desk verdict A useful rare-disease survey undermined by a phantom experimental claim and PRISMA arithmetic that doesn't add up. read the letter →

arxiv 2505.17065 v1 pith:K5LETQPT submitted 2025-05-18 cs.CL cs.LG

Valentina Carbonari , Pierangelo Veltri , Pietro Hiram Guzzi This is my paper · ORCID
classification cs.CLcs.LG
keywords largelanguagemodelsrarediseasesdiagnosissystematicreviewmultimodaldataquestionnairesclinicalnaturalprocessing
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

The paper is a systematic review that sets out to establish what large language models (LLMs) currently can and cannot do for the diagnosis of rare diseases. Following a systematic-review protocol, it selects 19 studies and sorts them into two methodological camps: studies that test LLMs with structured questionnaires and studies that use LLMs to extract or synthesize knowledge from unstructured text. Across the selected literature, it finds that the models are always used standalone, closed-source, and on a single input modality, never combining genetic, imaging, or electronic health record data. The conclusion asserts that the authors' own experiments with multiple LLMs and structured questionnaires showed promising diagnostic results, but the body of the paper does not report those experiments. A sympathetic reader would take the paper's thesis to be that LLMs are already plausible assistive tools for rare-disease diagnosis from text, and that multimodal integration is the necessary next step.

What carries the argument

The machinery that carries the argument is a systematic literature selection plus a five-dimension classification framework. The selection process is what lets the authors speak about 'the selected literature' as a single corpus; the classification framework (disease focus, study objective, input data modality, LLM type and access, and pipeline role) is what turns that corpus into a diagnosis of the field. The last two dimensions do the heaviest lifting: because every selected study uses a closed-source model in a standalone, monomodal setting, the framework yields the paper's central generalization that LLM-based rare-disease diagnosis is not yet integrated with genomic, imaging, or electronic health record data.

What would settle it

Ask the authors to release the list of the 19 included studies and the prompts, questionnaires, and outputs behind their claimed experiments; if the counts cannot be reconstructed (30 assessed by full-text review minus 21 excluded is 9, not 19) or the experimental results cannot be reproduced, the review's empirical generalizations lose their basis. A simpler check is to rerun the stated literature search with the stated keywords and date range and see whether the same 19 studies emerge.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that current LLM applications in rare-disease diagnosis are promising but structurally narrow. The detailed cases the review examines—glaucoma, sarcoidosis, Kienböck's disease, and amyloidosis—all use closed-source models in standalone mode with a single input type, either a structured questionnaire or raw patient-generated text. The review's classification framework organizes the field along five dimensions (disease focus, objective, input modality, model type and access, and pipeline role), and every selected study lands on the same side of the last two dimensions, which is what supports the conclusion that no study yet realizes a multimodal pipeline. The paper also asserts, in its conclusion, that the authors' own experiments with multiple LLMs and structured questionnaires produced promising results for diagnostic assistance, with genomic, imaging, laboratory, and longitudinal patient data named as the field's open frontier.

Load-bearing premise

The load-bearing assumption is that the 19 studies the selection process nominally included are the relevant, representative literature on LLMs and rare-disease diagnosis; the text cannot substantiate that because its own counts (30 assessed by full-text review, 21 excluded, 19 included) do not reconcile, and the claimed promising experiments are asserted in the conclusion without a reported method or results.

Editorial extensions

If this is right

  • If the review is right, clinicians considering an LLM for rare-disease diagnosis today should expect a text-only, closed-source assistant validated mainly through questionnaires, not a tool embedded in clinical workflow.
  • The dominance of monomodal studies implies that the next measurable gains will come from datasets and pipelines that pair clinical text with genetic variants, imaging, and structured laboratory results.
  • The proposed classification framework gives future evaluations a shared vocabulary, allowing new LLM studies to be compared on disease focus, input modality, model access, and pipeline role rather than as isolated accuracy figures.
  • Because every reviewed study is standalone and closed-source, reproducibility and auditability become the first governance issues for clinical deployment.
  • Taken at face value, the authors' own 'promising results' point to near-term use in triage and patient-facing explanation rather than autonomous diagnosis.

Reading between the lines

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

  • The authors leave implicit that their 'diagnostic odyssey' framing suggests a concrete outcome measure, time-to-diagnosis; a natural extension would compare time-to-diagnosis in cohorts where an LLM triaged the initial patient text against standard care.
  • A cheap falsifiable benchmark would rerun the four questionnaire studies (glaucoma, sarcoidosis, Kienböck's disease, amyloidosis) with one genetic or imaging feature added per case and measure whether diagnostic accuracy changes.
  • The review's focus on closed-source models implies a reproducibility hazard: later API versions may not reproduce published accuracy numbers, so an open-weight replication of the same questionnaires would give more durable evidence.
  • Because the claimed experiments appear only in the conclusion and lack a methods or results section, the fair reading is that the authors intend them as a pointer for future work rather than as established evidence.
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Signed reviews

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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 / 6 minor

Summary. This manuscript is presented as a PRISMA systematic review of large language models (LLMs) applied to the diagnosis of rare diseases, with an additional advertised original experimentation section. The paper describes the PRISMA selection procedure, sketches a classification framework for the literature, surveys datasets and ontologies, lists challenges, and offers future perspectives. The abstract promises a section on experimentation that 'utilizes multiple LLMs alongside structured questionnaires' for diagnosis, and the conclusion states that 'Our experimentation with different LLMs... showed promising results regarding their potential to assist in diagnosis.' However, the full text contains no such experimental section, no protocol, no data, and no results. In addition, the PRISMA accounting in Section 2 is arithmetically inconsistent, the list of the 19 supposedly included studies is not provided, and several citation-supported claims rest on references that do not address the cited topics. The manuscript therefore does not currently support its stated central contributions.

Significance. If the advertised systematic review and experimentation were actually present and correct, the paper would be a useful contribution to a growing area: it would map the current text-only focus of LLM work in rare diseases, identify multimodal integration as the frontier, and assemble a helpful inventory of datasets, ontologies, and limitations. The paper has some genuinely informative components, notably Table 1's summaries of four disease-specific studies and Table 5's structured list of limitations. The difficulty is that these components do not compensate for the absence of the promised experiments and the irreproducibility of the review's selection. As submitted, the central claims about 'promising results' and about what 'the selected literature' shows are unsupported, so the paper's significance cannot be assessed beyond its descriptive parts.

major comments (4)
  1. [Abstract and Section 7] The abstract promises 'a section on experimentation that utilizes multiple LLMs alongside structured questionnaires, specifically designed for diagnostic purposes,' but no such section exists anywhere in the manuscript. Section 7 then asserts, 'Our experimentation with different LLMs, however, showed promising results regarding their potential to assist in diagnosis,' without providing any protocol, model names, questionnaire design, data, evaluation metrics, or numerical results. This is an internal inconsistency between the declared contributions and the actual content, and the diagnostic promise claim is therefore unverifiable and unreproducible.
  2. [Section 2 (PRISMA flow)] The PRISMA accounting cannot be reconstructed. Section 2 states that 30 studies met the initial inclusion criteria and were assessed by full-text review, that 21 articles were excluded during this phase, and that 19 studies were ultimately included; however, 30 - 21 = 9, not 19. The manuscript also does not provide the list of the 19 included studies; Table 1 summarizes only four disease-specific studies, and Section 3's classification framework is not applied to any enumerated set of 19 papers. In addition, the stated exclusion criterion 'lack of peer review' is contradicted by the inclusion of reference [28], a medRxiv preprint, in Table 1. These problems make it impossible to verify which studies the survey's generalizations are based on.
  3. [Section 4.1 and Introduction (References [17], [21], [34])] Several load-bearing citation claims are unsupported by the cited references. The claim that MIMIC-III has been 'extensively adopted for patient phenotyping, disease classification, and predictive modelling' cites [17], which is a book on biological network analysis, not a MIMIC-III adoption study. The corresponding claim about MIMIC-IV cites [21], a survey on disease spreading modeling, which likewise does not discuss MIMIC-IV. In the Introduction, the need for a systematic survey is supported in part by [34], a paper on age and gender differences in SARS-CoV-2 outcomes, which is not relevant to that point. Because these citations do not support the assertions they accompany, the survey's factual grounding is in need of systematic verification.
  4. [Section 3 (classification framework)] The proposed classification framework is described in general terms but never actually applied to the included studies. Section 3 repeats the framework description almost verbatim twice, includes an unresolved 'Table ??' cross-reference, and presents no completed classification of the reviewed papers. Statements such as 'In all the articles analyzed, the models were tested in closed environments and independently' are based on only the four studies in Table 1, not on the 19 studies claimed to be included. The survey's synthesis is therefore not supported by the evidence actually presented.
minor comments (6)
  1. [Section 1 (first paragraph)] The sentence 'affecting an estimated 3.5–5.9' is missing the unit and the citation; it should read '3.5%–5.9% of the global population,' as correctly stated later in Section 1.1 with reference [37].
  2. [Section 3] A paragraph beginning 'Research into the use of Large Language Models...' is duplicated almost verbatim, and the cross-reference to the framework table remains as 'Table ??'. Please remove the duplication and fix the cross-reference.
  3. [Section 3.0.1] The phrase 'lunar avascular necrosis' should be 'lunate avascular necrosis.'
  4. [Section 4.4] The sentence 'It is [43] a social network structured around various topics-focused forums' is ungrammatical; consider revising to 'Reddit [43] is a social network structured around topic-focused forums.'
  5. [Section 7] The sentence 'According to Hasani et al., this kind of cooperation is essential...' cites no reference number; if reference [19] is intended, it should be cited explicitly at that point.
  6. [Section 4] Tables 2, 3, and 4 are presented without in-text callouts in the running text; please add explicit references to each table in the relevant subsection.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper is a literature survey with no derivational chain; its missing experimentation section is an unsupported assertion, not a circular reduction.

full rationale

This manuscript is a PRISMA-style systematic review and does not present a quantitative derivation, fitted model, or prediction whose output could be equivalent to its inputs by construction. The central defect is that the abstract and conclusion advertise an original experimentation section — 'we present a section on experimentation that utilizes multiple LLMs alongside structured questionnaires' and 'Our experimentation with different LLMs, however, showed promising results regarding their potential to assist in diagnosis' — but no such protocol, data, or results appear in Sections 1-7. That is an internal consistency and completeness problem, not circularity: there is no equation, fitted parameter, or derivation chain to compare with a claimed output. The self-citations in the bibliography, including refs [17], [21], and [34] involving co-authors, are used only as general background support for statements about MIMIC adoption and the value of surveys; they are not invoked as uniqueness theorems, nor do they carry the paper's conclusions. The PRISMA accounting is also internally inconsistent (30 studies assessed by full-text review, 21 excluded, yet 19 reported as included), but arithmetic inconsistency is a reporting defect rather than input-output circularity. No load-bearing step in the survey reduces to its own inputs, so the circularity score is 0.

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

The paper is a review: it introduces no fitted parameters, no new model, and no new entities. Its load-bearing assumptions are methodological: that the reported PRISMA accounting is correct, though Section 2 reports 30 assessed, 21 excluded, and 19 included, which does not sum; that the 19 cited studies are representative and their results reliable, since no independent verification is performed; and that the promised original experimentation exists, since the conclusion cites its results but no experiment section appears.

assumptions (3)
  • domain assumption The 19 studies reported as included in the PRISMA review constitute a valid and representative sample of LLM research in rare diseases.
    Every general statement in the survey about 'the selected literature' inherits this assumption. Section 2's counts are internally inconsistent, with 30 assessed, 21 excluded, and 19 included, which cannot be reconciled, so the assumption that the review process was executed as reported is weakened.
  • domain assumption The accuracy and capability claims of the 19 cited studies are taken at face value.
    The survey performs no independent evaluation and re-runs no experiments; its conclusions about LLM diagnostic accuracy inherit the validity and error bars of the cited papers, such as the four disease-specific evaluations in Table 1.
  • domain assumption The premise that LLMs can meaningfully assist rare disease diagnosis is testable and true.
    This is the field-level premise the survey organizes evidence around. The paper's own intended test, the promised experimentation section, is absent from the full text, so the premise rests entirely on cited work rather than on anything demonstrated by this manuscript.

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Cite this review

Pith. "Pith review of Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases." pith.science (2026). https://pith.science/paper/K5LETQPT

@misc{pith2026250517065,
  author       = {Pith},
  title        = {Pith review of: Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K5LETQPT}},
  note         = {Machine review of arXiv:2505.17065}
}
read the original abstract

Recent advances in artificial intelligence, particularly large language models LLMs, have shown promising capabilities in transforming rare disease research. This survey paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies that leverage textual data to uncover insights and patterns critical for diagnosis, treatment, and patient care. While current research predominantly employs textual data, the potential for multimodal data integration combining genetic, imaging, and electronic health records stands as a promising frontier. We review foundational papers that demonstrate the application of LLMs in identifying and extracting relevant medical information, simulating intelligent conversational agents for patient interaction, and enabling the formulation of accurate and timely diagnoses. Furthermore, this paper discusses the challenges and ethical considerations inherent in deploying LLMs, including data privacy, model transparency, and the need for robust, inclusive data sets. As part of this exploration, we present a section on experimentation that utilizes multiple LLMs alongside structured questionnaires, specifically designed for diagnostic purposes in the context of different diseases. We conclude with future perspectives on the evolution of LLMs towards truly multimodal platforms, which would integrate diverse data types to provide a more comprehensive understanding of rare diseases, ultimately fostering better outcomes in clinical settings.

Figures

Figures reproduced from arXiv: 2505.17065 by the authors.

Figure 1
Figure 1. Flowchart of the diagnostic journey for rare disease patients. The path [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. PRISMA flow diagram illustrating the systematic review process. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.