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From General to Specific: Tailoring Large Language Models for Personalized Healthcare

T0 review · 5 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read A patient-specific text prompt, built from a predicted diagnosis and similar patients' diagnoses, then refined by deleting words with a reinforcement-learned policy, is enough to make closed proprietary LLMs give more personalized medical…

desk verdict A competent but under-evidenced pipeline for prompt-level medical personalization; the central claim of fine-grained individual adaptation is not yet supported. read the letter →

arxiv 2412.15957 v1 pith:O5EV4FA3 submitted 2024-12-20 cs.CL cs.AIcs.IR

Ruize Shi , Hong Huang , Wei Zhou , Kehan Yin , Kai Zhao , Yun Zhao This is my paper · ORCID
classification cs.CLcs.AIcs.IR
keywords personalizedmedicinelargelanguagemodelspromptoptimizationreinforcementlearninghardpromptscollaborativefilteringhealthcareNLPobstetrics
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 tries to establish that a patient-specific text prompt, built from a predicted diagnosis plus the diagnoses of similar past patients and then refined by deleting words through reinforcement learning, is enough to make closed commercial LLMs give more personalized medical advice. It proposes PMLM, which first uses a fine-tuned LLM to predict each patient's condition and an encoder plus cosine similarity to find similar patients, then composes a coarse personalized prompt and trains a word-level policy to delete tokens so that the final prompt improves the LLM's response as measured by BERTScore against doctor-written replies. On real obstetric and gynecological records, every proprietary LLM tested, including GPT-4, Gemini1.5-pro, and GLM4 variants, improved by more than 10% on BERTScore when driven by the refined prompt, and the GPT-4-based PMLM outperformed fine-tuned open models like Llama3-8B and Qwen2-7B. If this holds, a text-only prompt layer can adapt proprietary LLMs to individual patients without retraining them or sending raw examination tables to the closed service.

What carries the argument

The load-bearing object is the personalized hard prompt, a text prompt that differs for every patient and is passed unchanged to a closed LLM. Its construction is carried by two mechanisms: (1) self-informed personalization, where a LoRA-fine-tuned LLM predictor turns variable-length health check records into a predicted label, and peer-informed personalization, where an MLP encoder projects padded records into a shared space and cosine similarity selects the $k$ most similar past patients whose diagnoses are listed in the prompt; and (2) an RL word-deletion policy that, starting from the coarse prompt, samples one word to delete per iteration based on BERT embeddings concatenated with the mean embedding and the patient vector, using BERTScore gain over the initial prompt's response as reward. Because the final prompt is a subsequence of the coarse prompt and is pure text, it needs no access to the proprietary LLM's parameters.

What would settle it

Take a held-out set of patients, shuffle the predicted labels among patients so that each prompt contains a wrong label, and run the trained PMLM policy to produce refined prompts and responses; if BERTScore against doctor references does not drop markedly relative to the unshuffled case, the method is responding to template-level patterns and not to the patient-specific content.

Watch

Extended reading notes

Core claim

The central claim, stated on the paper's own terms, is that PMLM achieves individual-level personalization for medical LLMs by constructing a coarse-grained hard prompt from self-informed and peer-informed information and refining it with an RL policy that deletes words. The self-informed part is a predicted label from a LoRA-fine-tuned predictor; the peer-informed part is the top-$k$ most similar patients by cosine similarity in an MLP-encoded space, whose ground-truth diagnoses are inserted into the prompt. The RL policy computes deletion probabilities from BERT word embeddings, their mean, and the patient representation, deletes one word per step for $n$ steps, and is trained with a loss that rewards BERTScore gain between the refined and initial prompt responses. The result, the paper claims, is that all evaluated proprietary LLMs exceed a 10% performance improvement and the GPT-4-based PMLM reaches the best BERTScore, exceeding fine-tuned lightweight LLMs while requiring no weight changes to the proprietary model.

Load-bearing premise

The claim that the RL stage enables fine-grained personalization rests on the assumption that deleting words from the initial prompt is enough to adapt it to each patient, since the paper's Limitations state that addition and replacement are left to future work; if no deletion can express the needed new guidance, the measured gains may reflect selection of a common template that scores well on BERTScore rather than true per-patient adaptation.

Editorial extensions

If this is right

  • A text-only prompt layer can steer closed, proprietary LLMs into producing more individualized responses without fine-tuning or weight access.
  • Patients' raw multi-visit examination tables do not need to be sent to the proprietary model; only the predicted label and selected similar-patient diagnoses appear in the prompt, which the paper argues reduces privacy exposure.
  • The same refined text prompt can in principle be reused across different LLMs, though the policy itself is trained with reward from a specific downstream model.
  • On semantic similarity metrics the method beats fine-tuned 7B-9B open LLMs, but it trails on ROUGE-L, indicating the gains are semantic rather than lexical overlap with doctor-written replies.
  • Since only word deletion is used, the refined prompt cannot introduce new patient-specific content; the authors list addition and replacement as future work.

Reading between the lines

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

  • Editorial inference: because the reward is BERTScore against doctor-written reference responses, the method can only rediscover patterns present in the reference corpus; it would not generate novel advice that no doctor in the training set wrote.
  • Editorial inference: a testable extension is to allow insertion and replacement in the policy; if the current method's gains are primarily from removing generic text, an insertion-capable policy trained on the same reward should yield equal or better personalization on the same obstetric data.
  • Editorial inference: the similarity-based peer-informed component had the smallest ablation impact, suggesting that the predicted label carries most of the personalization signal; a cheaper predictor or a similarity based on raw records rather than zero-padded MLP embeddings might change this balance.
  • Editorial inference: the method transfers naturally to other closed-API personalization tasks, such as email drafting or fitness advice, whenever a reference response is available to define the BERTScore reward; the paper only demonstrates the healthcare setting.
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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

5 major / 5 minor

Summary. The paper proposes PMLM, a method for generating personalized hard prompts for medical LLMs. It first fine-tunes LLaVA1.5-7B as a label predictor, then encodes patient data and retrieves k similar patients via cosine similarity to construct a coarse-grained prompt containing the predicted label and similar patients' diagnoses. A BERT-based policy network refines this prompt by deleting words over n steps, using a REINFORCE-style loss whose reward is the BERTScore improvement of the downstream proprietary LLM response over the initial prompt. The final prompt is fed to a closed LLM such as GPT-4 to produce a response. Experiments on real obstetrics data report BLEU, ROUGE, and BERTScore improvements over the initial prompt and over fine-tuned 7-9B open LLMs.

Significance. If the central claim held, the contribution would be practically significant: it would show that text-only hard prompts can be optimized for individual patients and transferred to proprietary LLMs without weight updates or raw data transfer. The paper has several strengths: real hospital data, evaluation across five proprietary LLMs, an ablation of the three components, a hyperparameter study, and qualitative case studies. However, the current evidence does not establish fine-grained personalization because the evaluation metric is also the training reward, the deletion-only action space limits expressivity, and the state-of-the-art comparison confounds method with base model. The result is a useful proposal with suggestive evidence rather than a validated system.

major comments (5)
  1. [Section 4.2, Eq. (9) vs. Section 5.2, Tables 1-3] The RL reward is BERTScore against doctor reference responses, and BERTScore is also the headline evaluation metric in Tables 1-3. The authors explicitly argue (Section 5.2) that greater emphasis should be placed on BERTScore. Hence the reported BERTScore gains are, to an unknown extent, a consequence of optimizing the same score. The held-out year split reduces overfitting but does not remove the objective-alignment problem. The paper needs an independent evaluation (e.g., clinician ratings, or a metric not used in training, or a check that the refinement changes responses in ways correlated with patient-specific data beyond the predicted label).
  2. [Section 5.2, Tables 1-2] The claim that PMLM 'achieves state-of-the-art performance' is confounded: the PMLM row in Table 2 is identical to the GPT-4 'after' row in Table 1. The comparison is thus between GPT-4 with a refined prompt and fine-tuned 7-9B open models with an unrefined prompt. This does not show that the PMLM method, as opposed to the base LLM scale, is responsible for the improvement. The authors should include fine-tuned lightweight baselines with the same personalized prompt (or at least the coarse prompt) and a GPT-4 baseline without PMLM in the same table.
  3. [Section 5.3, Eqs. 5-11, Limitations] The personalization study only plots counts of deleted word indices; it does not show that different patients receive semantically different refinements. Since the action space is deletion-only (Eq. 7) and the Limitations section explicitly states that addition and replacement are left to future work, the final prompt is a subsequence of the coarse prompt and cannot introduce patient-specific instructions absent from the initial prompt. The BERTScore gains could therefore come from selecting a common deletion pattern that emphasizes the predicted label and similar-patient block, rather than from adapting the advice to the individual. A per-patient analysis or a clinician-blinded evaluation is needed to support the claim of individual-level personalization.
  4. [Section 5.2, Table 1] The sentence 'all evaluated LLMs exhibit performance improvements exceeding 10%' is contradicted by Table 1 on the BERTScore F1 metric: Gemini1.5-pro improves from 63.94 to 70.13 (about 9.7%), GPT3.5-turbo from 65.19 to 68.89 (about 5.7%), and GLM4-plus from 61.26 to 67.31 (about 9.9%). The claim is only true for BLEU-4 or ROUGE-N for some models. Since the paper highlights BERTScore as the most important metric, this overstatement should be corrected and the claim restated per metric.
  5. [Section 5.1, Experimental settings] No variance, confidence intervals, or significance tests are reported despite the statement that averages over five runs are used. Given the small test set (the second half of 2022 is a small slice of the 2,373 patients) and the modest differences in the ablation study (e.g., Variant 2 vs. Variant 3 differ by about 2.4 BERTScore F1), the improvements could be within run-to-run noise. The authors should report standard deviations and significance tests, or at least the minimum and maximum over runs.
minor comments (5)
  1. [Section 1, Contributions] There is a typo in the first contribution bullet: 'healthcare filed' should be 'healthcare field'.
  2. [Section 4.2, Eqs. 5-11] The state indexing is inconsistent: the paper defines S = {s0, ..., s_{n-1}} but says the process is repeated n times and the final state is s_{n-1}. After n deletions the final state should be s_n; please correct the off-by-one and ensure the loss in Eq. (11) aligns with the number of steps.
  3. [Section 5.2, Table 2] The caption of Table 2 does not state that the PMLM row uses GPT-4 as the base LLM; since PMLM is model-agnostic, please specify the base model for each row in the caption or table notes.
  4. [Appendix A.2, Dataset details] The text says the final dataset contains '38,817 records for 2,373 pregnant patients', but the subsequent sentence 'It contains 56 entries from 2020, 1,638 from 2021, and 679 from 2022' sums to 2,373 and appears to refer to patients, not records; please clarify the units.
  5. [Appendix A.1 and Ethical statement] The paper states that code will be released upon formal publication. For reproducibility, please provide a link to the code or at least a synthetic data generator and detailed preprocessing scripts, since the real data cannot be shared for privacy reasons.

Circularity Check

2 steps flagged · score 4.0 of 10

BERTScore is both the RL reward and the headline evaluation metric, and the personalization study verifies mostly the definitional property that prompts differ across patients.

  1. fitted input called prediction [Section 4.2, Eq. (9) and Eq. (11); Section 5.2, Table 1]
    "Reward = BS (ˆr, r) − BS (ˆr0, r) ... L = − n−1X i=0 log(π(ej|si)) · Reward ... Finally, we compare the outputs from each LLM with the reference responses, utilizing BLEU (Papineni et al., 2002), ROUGE (Lin, 2004) and BERTScore (Zhang et al., 2020) as metrics."

    The policy is optimized to increase BERTScore of the generated response against the doctor reference, and the paper's main evidence that PMLM improves personalization is the same BERTScore reported in Table 1. Section 5.2 explicitly privileges BERTScore for validation, saying greater emphasis should be placed on BERTScore. Thus the evaluation metric is the training objective, so the reported BERTScore gain is partly an artifact of optimizing that score rather than an independent confirmation of personalized response quality. The temporal train/test split and the concurrent BLEU/ROUGE improvements give partial independence, so this is not a full tautology, but the central claim lacks an evaluation metric that was not used as reward.

  2. self definitional [Section 3, definition of personalized prompt; Section 5.3, Personalization Study]
    "Furthermore, Pi ̸= Pj, i̸= j, which means that the personalized prompts differ for each patient to guide LLMs in offering tailored responses. ... As shown in Figure 6, the frequency of word modifications varies significantly across different indices. This result demonstrates that the policy network ... effectively performs personalized refinements, constructing prompts tailored to individual needs."

    The paper defines a personalized prompt solely as one that differs across patients (Pi ≠ Pj), and the personalization study shows that deletion counts vary across patients. Because the deletion probabilities already contain the patient representation x̂ by construction (Eq. 7), patient-dependent word deletion follows from the architecture and does not independently demonstrate that the final responses are semantically or clinically personalized. The conclusion that PMLM achieves individual-level personalization therefore re-uses its own definitional criterion as evidence.

full rationale

PMLM's derivation chain is not circular in the strongest sense of a fitted parameter being renamed as a prediction: the RL policy is trained on a temporal training split and evaluated on held-out 2022 data, and BLEU, ROUGE, and BERTScore all improve, so the main quantitative result has independent empirical content. The circularity burden is instead concentrated in two places. First, the reward function (Eq. 9) and the preferred evaluation metric (Table 1, BERTScore) are identical, so the headline improvement on BERTScore is partially an optimization artifact; the paper does not report a clinician-blinded or metric-free check to break that loop. Second, the personalization study (Section 5.3) treats varying word-deletion counts as evidence of individual-level personalization, but that variation follows from the definition of personalized prompts (Pi ≠ Pj) and from injecting x̂ into the policy, so it does not measure semantic personalization. The deletion-only refinement, acknowledged in Limitations as primarily exploring deletion, further limits the operation space, though this is a construct-validity limitation rather than a circular step. Overall, the central claim still has independent content, so the score is moderate rather than high.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on domain assumptions about the validity of doctor-reference similarity, the clinical meaning of pairwise patient similarity, predictor accuracy, and the sufficiency of deletion-only prompt refinement. No new physical or mathematical entities are introduced.

free parameters (3)
  • k (number of similar patients) = 10
    Tuned on validation in Section 5.5 across {5, 10, 15, 20, 25}; affects the amount of peer-informed information in the prompt.
  • n (number of prompt modification steps) = 10
    Tuned on validation in Section 5.5 across {5, 10, 15, 20, 25}; controls how many words are deleted from the initial prompt.
  • learning rate = 0.005
    Adam learning rate for the policy network, reported in Section 5.1 without sensitivity analysis.
assumptions (4)
  • domain assumption Doctor reference responses r accurately represent the desired personalized output, and BERTScore against them measures personalization quality.
    Used to define the reward in Eq.9 and the evaluation in Tables 1-3; no clinical expert validation is provided.
  • domain assumption Cosine similarity over MLP-encoded, zero-padded concatenated checkup vectors identifies clinically similar patients.
    Invoked in Eq.4 to select top-k similar patients; there is no clinical validation that this similarity corresponds to comparable medical conditions.
  • domain assumption The LoRA-fine-tuned LLaVA1.5-7B predictor produces labels accurate enough to build the personalized prompt.
    Section 4.1 and Appendix A.3 report a Micro-F1 around 82.6 on the reduced 12-label set, so prediction errors propagate into the prompt.
  • ad hoc to paper Deleting words from a prompt is sufficient to refine personalized prompts and improve responses.
    Section 4.2 uses only deletion as the modification operation; the paper's Limitations acknowledges that addition and replacement are left to future work.

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Cite this review

Pith. "Pith review of From General to Specific: Tailoring Large Language Models for Personalized Healthcare." pith.science (2026). https://pith.science/paper/O5EV4FA3

@misc{pith2026241215957,
  author       = {Pith},
  title        = {Pith review of: From General to Specific: Tailoring Large Language Models for Personalized Healthcare},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O5EV4FA3}},
  note         = {Machine review of arXiv:2412.15957}
}
read the original abstract

The rapid development of large language models (LLMs) has transformed many industries, including healthcare. However, previous medical LLMs have largely focused on leveraging general medical knowledge to provide responses, without accounting for patient variability and lacking true personalization at the individual level. To address this, we propose a novel method called personalized medical language model (PMLM), which explores and optimizes personalized LLMs through recommendation systems and reinforcement learning (RL). Specifically, by utilizing self-informed and peer-informed personalization, PMLM captures changes in behaviors and preferences to design initial personalized prompts tailored to individual needs. We further refine these initial personalized prompts through RL, ultimately enhancing the precision of LLM guidance. Notably, the personalized prompt are hard prompt, which grants PMLM high adaptability and reusability, allowing it to directly leverage high-quality proprietary LLMs. We evaluate PMLM using real-world obstetrics and gynecology data, and the experimental results demonstrate that PMLM achieves personalized responses, and it provides more refined and individualized services, offering a potential way for personalized medical LLMs.

Figures

Figures reproduced from arXiv: 2412.15957 by the authors.

Figure 1
Figure 1. Previous so-called personalized medical LLMs only contain knowledge of a single disease, with personalization limited to variations in the input data. Today, LLMs have been successfully applied in nu￾merous fields (Fang et al., 2024; Shah et al., 2023), with healthcare emerging as a particularly promis￾ing domain where LLMs have made notable strides in supporting treatment (Wang et al., 2024; Zheng et al., 2024a). M… view at source ↗
Figure 2
Figure 2. Architecture of the proposed PMLM. step by using retrieval strategy to determine the most effective prompt. Rewriter (Li et al., 2024a) combines supervised learning with RL to rewrite prompts. However, the methods discussed above primarily focus on identifying optimal prompts for specific tasks, rather than designing personalized prompts tailored to individual users. 3 Method Preparation Our work focuses on personal… view at source ↗
Figure 3
Figure 3. Example prompt for the predictor. network evaluates the modification probability of each word, guided by a reward function to optimize the refinement process. Importantly, the techniques employed in PMLM do not alter the internal param￾eters or structure of the underlying LLM, enabling seamless and efficient application to proprietary LLMs enriched with extensive disease knowledge. 4.1 Extraction of Personalized Inf… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Example of the coarse-grained personalized [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Example prompt for evaluation, where the [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 7
Figure 7. Figure 7: Hyper-parameter study. The X-axis represents [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MedReadCtrl: Personalizing medical text generation with readability-controlled instruction learning

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    MedReadCtrl instruction-tunes LLaMA3 to control readability at 12 grade levels, reporting lower readability errors than GPT-4 and higher content scores on unseen clinical simplification.

  2. Prompt Mechanisms in Medical Imaging: A Comprehensive Survey

    eess.IV 2025-06 conditional novelty 4.0 of 10

    A broad survey that organizes prompt mechanisms for medical image generation, segmentation, and classification into a two-dimensional taxonomy of core technologies and clinical applications.

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