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REVIEW 3 major objections 5 minor 1 cited by

Best Practices for Large Language Models in Radiology

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This review argues that radiology departments should not pretrain their own large language models, but instead follow a staged escalation from prompting to retrieval-augmented generation to fine-tuning only when needed.

desk verdict A solid, well-organized review of LLM best practices in radiology whose practical escalation ladder is useful and appropriately hedged, but the manuscript is unfinished (citation placeholders, a broken reference) and the ladder's ordering lacks direct radiology evidence. read the letter →

arxiv 2412.01233 v1 pith:4NHTLOTR submitted 2024-12-02 cs.AI

Christian Bluethgen , Dave Van Veen , Cyril Zakka , Katherine Link , Aaron Fanous , Roxana Daneshjou , Thomas Frauenfelder , Curtis Langlotz
show 2 more authors
Sergios Gatidis Akshay Chaudhari
This is my paper · ORCID
classification cs.AI
keywords largelanguagemodelsradiologypromptengineeringretrieval-augmentedgenerationfine-tuningLoRAmodelevaluationprivacy
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 review argues that radiology departments do not need to pretrain their own large language models. The authors recommend an iterative escalation: start with prompt optimization on general-purpose models, add in-context examples and retrieval-augmented generation to supply relevant knowledge, enable tools, and only fine-tune if the simpler steps are insufficient. The payoff is that radiology teams can deploy capable language tools for reporting, summarization, and communication while controlling compute cost, privacy risk, and confabulation. The recommendation rests on a synthesis of recent LLM research and radiology-specific studies rather than a new experiment.

What carries the argument

The organizing mechanism is the iterative optimization ladder (Figure 5). It orders adaptation techniques by increasing complexity and cost: prompting, in-context learning, retrieval-augmented generation, tool access, and fine-tuning. Each higher rung permanently modifies the model or adds persistent infrastructure, so the recommendation is to stop at the lowest rung that meets the task requirement, with prompt engineering remaining relevant even after fine-tuning.

What would settle it

A controlled comparison on a representative radiology task in which a small fine-tuned model clearly beats a well-prompted large model with retrieval augmentation, at equal or lower cost, would undercut the recommended order of escalation.

Watch

Extended reading notes

Core claim

The central claim is that the most effective path to using large language models in radiology follows a cost-ordered adaptation ladder. Its rungs are: iteratively refine the prompt; include a few task examples (in-context learning); ground the model with retrieved external knowledge (retrieval-augmented generation); give the model access to tools; and finally, if needed, fine-tune with curated data, preferably via parameter-efficient methods like LoRA. The paper further claims that pretraining from scratch is impractical for most radiology labs, that open locally hosted models should be favored over closed hosted models for privacy and reproducibility, and that evaluation must combine automated metrics with radiologist review and continue after deployment.

Load-bearing premise

The entire ladder assumes that techniques validated in general-domain LLM research and a small set of early radiology studies will transfer reliably to real radiology workflows across different models, hospitals, and tasks.

Editorial extensions

If this is right

  • Radiology AI projects can skip the multi-million-dollar pretraining step and begin with prompted general-purpose models already available today.
  • Factual reliability can be improved without retraining by grounding outputs in retrieved guidelines and reports, which also makes outputs easier to verify.
  • Privacy-sensitive deployment is feasible with locally hosted open models, since imaging data and notes do not need to be sent to an external API.
  • Fine-tuning, when needed, can be carried out cheaply with LoRA-style methods, making customization attainable for smaller labs.
  • Evaluation that pairs automated scores with radiologist review becomes the gatekeeper for deciding when to escalate to the next rung.

Reading between the lines

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

  • The paper does not say how to choose the threshold that triggers a move up the ladder; an operational rule would be to escalate only when the current rung fails an agreed clinical benchmark.
  • The open-vs-closed model recommendation is driven by governance concerns rather than head-to-head accuracy evidence; head-to-head radiology benchmarks could change the balance.
  • As multimodal LLMs improve, the ladder may effectively start at a higher rung, with image-aware prompting already covering tasks like draft report generation without any fine-tuning.
  • The ladder can be tested prospectively: fix an evaluation set, measure each rung's gain, and check whether the gains plateau before fine-tuning.
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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

3 major / 5 minor

Summary. This manuscript is a narrative review of best practices for using and adapting large language models (LLMs) in radiology. It covers technical foundations (transformers, training pipeline, multimodal extensions), possible applications (clinical, operational, research, education), desiderata, limitations (confabulation, privacy, bias, reproducibility), adaptation methods (prompting, in-context learning, retrieval-augmented generation, instruction tuning, alignment, parameter-efficient fine-tuning, quantization, distillation), and evaluation strategies (automatic metrics, radiology-specific benchmarks, human expert assessment). The central recommendation, stated in Section 5, is an iterative escalation ladder: start with optimized prompting of general-purpose models, then enrich context through in-context learning and retrieval-augmented generation, enable access to tools, and finally fine-tune if necessary, while preferring local open models and maintaining ongoing human evaluation.

Significance. If the recommendations are accepted, this review could serve as a practical entry point for radiology teams without dedicated machine-learning resources, helping them decide how to deploy LLMs for language-oriented tasks in radiology. The paper is timely and broad, and it has several concrete strengths: Table 1 and Table 2 are actionable and clearly organized; the authors explicitly hedge that effectiveness depends on the model and may change over time; practical cost estimates for pretraining and fine-tuning are useful; and the review covers a wide range of recent radiology-specific and general-domain sources. As a narrative review rather than a primary study, its value lies in synthesis and guidance, and the authors generally distinguish between established practice, emerging evidence, and suggestions based on practical experience.

major comments (3)
  1. [Section 4.1 (Multimodality paragraph) and Section 4.1.1] The manuscript contains unresolved citation placeholders: 'radiological images.?, 90' in Section 4.1 and 'Model Cards and Versioning.?, 64' in Section 4.1.1. These incomplete citations undermine the evidence base for statements about multimodal LLM performance and model cards, and they are not acceptable in a submitted manuscript. Please replace the '?' placeholders with the intended reference keys or remove the citations.
  2. [Section 4.3 and Section 5 (Central recommendation)] The central recommendation is an ordered escalation ladder: prompting, then in-context learning/RAG, then tools, then fine-tuning. The manuscript states in Section 4.3 that 'Prompting will only moderately influence concrete radiology-specific abilities,' but the only comparative citation for the prompting-versus-fine-tuning choice is ref. 120, a general-domain knowledge-injection study. No radiology-specific head-to-head evidence is provided for the ordering. Because the ladder is the paper's main takeaway, the authors should either cite radiology-specific comparisons (e.g., prompting versus fine-tuning for report summarization or information extraction) or explicitly reframe the ordering as a resource-based heuristic rather than an evidence-based effectiveness ranking, and discuss the evidence gap. The existing hedges about model dependence are helpful but do not directly address the ordinal claim.
  3. [Section 4.3 and Section 5] The proposed ladder is text-centric and does not clearly account for the core radiology task of image interpretation. The paper acknowledges that large multimodal models are needed for image-based tasks and advises readers to 'keep an eye' on them, but the central recommendation does not integrate multimodal model selection or vision bridges into the escalation path. For many real-world radiology tasks, prompt optimization and text-based RAG cannot supply visual competence; the appropriate starting point may be selecting a multimodal model or pairing a text LLM with a vision module. Please clarify how the escalation ladder applies to language-only tasks versus image-interpretation tasks, and incorporate the multimodality discussion into the central recommendation.
minor comments (5)
  1. [Section 2.2] There is a typo: 'particulary' should be 'particularly.'
  2. [Table 1, Few-shot prompting row] The example JSON in the few-shot prompting row has mismatched braces, making the example harder to parse; consider formatting it as a complete, valid JSON object.
  3. [Figure 5 caption] The phrase 'The presented method groups increase in complexity' is awkward; consider rewording to 'The presented groups of methods increase in complexity.'
  4. [Section 4.1 and elsewhere] Several author names contain stray spacing or OCR-like artifacts (e.g., 'LLaV A' for LLaVA, 'V o' in reference author names); a careful proofreading pass is needed.
  5. [Footnote 2 in Section 2.4] The cost calculation is written as '$33/8 GPUs/h', which is ambiguous; clarify whether the $33 is per GPU-hour or for a group of GPUs, and make the arithmetic explicit.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: central recommendations are practical heuristics grounded in external evidence, with self-citations used only as illustrative examples.

full rationale

This paper is a narrative review of best practices rather than a derivation, so the usual circularity patterns (self-definitional fits, fitted inputs called predictions, imported uniqueness theorems, ansatz smuggled via citation) do not apply. The central escalation recommendation in Section 5 and Figure 5 — start with prompting, then in-context learning/RAG, then tools, then fine-tuning if necessary — is explicitly hedged: Table 1 notes that 'the effectiveness of some recommendations depends on the model, and best practices may change over time,' and the Conclusions state that 'the effectiveness of these techniques may vary across different LLM types and versions.' The comparative claim about prompting versus fine-tuning is supported by external general-domain work (ref. 120), while the RAG recommendation cites the original external RAG paper (ref. 118) and the few-shot/in-context claim cites the external GPT-3 paper (ref. 10). Self-citations (RadAdapt ref. 12, CheXagent ref. 26, Clinical Text Summarization ref. 52, RaLEs ref. 106, Almanac ref. 107) appear as examples of existing systems, benchmarks, or studies rather than as the load-bearing warrant for the paper's recommendations. No equation is fitted and then renamed as a prediction, and no result is forced by a self-citation chain. The main scientific limitation is evidentiary rather than circular: the ordinal structure of the escalation ladder is not backed by a head-to-head radiology benchmark across all rungs, but the authors acknowledge uncertainty and present the ladder as iterative guidance. Under the hard rule that circularity requires a quotable reduction of a claim to its own inputs, no such step is present; the score reflects only the presence of non-load-bearing self-citations in a review context.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

Free parameters: none, the paper fits no data and introduces no fitted quantities. Axioms: two domain assumptions about transferability of general LLM findings to radiology and about the accuracy of the cited literature. Invented entities: none.

assumptions (2)
  • domain assumption Recommendations validated on general-domain LLMs and a few radiology studies transfer to real radiology deployments.
    The paper builds its advice on general LLM references and early radiology pilots (refs 10, 52, 57, 78, 118); the authors themselves qualify this in Table 1's note and the Conclusions, but the transfer is still assumed for the central ladder.
  • domain assumption The cited empirical results (capabilities, costs, failure modes, evaluation metrics) are accurate.
    Claims such as the cost estimates in the footnotes, LoRA and QLoRA effectiveness, and confabulation statistics rest on sources the paper does not reproduce; two in-text citations are unresolved placeholders.

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Cite this review

Pith. "Pith review of Best Practices for Large Language Models in Radiology." pith.science (2026). https://pith.science/paper/4NHTLOTR

@misc{pith2026241201233,
  author       = {Pith},
  title        = {Pith review of: Best Practices for Large Language Models in Radiology},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4NHTLOTR}},
  note         = {Machine review of arXiv:2412.01233}
}
read the original abstract

At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies.

Figures

Figures reproduced from arXiv: 2412.01233 by the authors.

Figure 1
Figure 1. Process of training a large language model (LLM). (1) First, an LLM is pretrained in a self-supervised way to predict [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Simplified example of enhancing model responses by incorporating additional information from external sources with [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Large language models (LLMs) in agentic workflows. In this example, the user asks for lung nodules in a CT scan [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Simplified overview of training and domain adaptation methods for large language models (LLMs). Initially, any LLM [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Iterative LLM Optimization for Radiology Tasks. When applying a large language model (LLM) to a new problem, [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]

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Forward citations

Cited by 1 Pith paper

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.