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ClinicalGPT: Innovations in Clinical LLMs

Updated 7 September 2025
  • ClinicalGPT is a family of domain-specific large language models tailored to optimize clinical workflows by integrating decision support, literature screening, cohort selection, and protocol generation.
  • It leverages fine-tuning, prompt engineering, and multi-agent systems to achieve robust diagnostic and screening outcomes, with evaluations demonstrating competitive accuracy in real-world settings.
  • ClinicalGPT systems deliver scalable, transparent, and interpretable clinical solutions, although reliable performance necessitates ongoing human oversight and domain-specific adaptation.

ClinicalGPT refers to a family of LLM systems and research frameworks explicitly designed to enable or augment a range of clinical and biomedical workflows, typically by leveraging advances in transformer-based natural language processing. As implemented in recent research, ClinicalGPT systems—whether domain-adapted open-source LLMs, prompt-engineered GPT APIs, or multi-agent toolchains—are optimized for medical tasks such as clinical decision support, cohort/participant selection, literature screening, knowledge base construction, and protocol generation. This article synthesizes core technical principles, evaluation outcomes, workflow methodologies, and the current scope and limitations of ClinicalGPT, as established in peer-reviewed and preprint literature.

1. Architectural Modalities and Adaptation Strategies

ClinicalGPT implementations fall into several principal categories, reflecting both the rapidly evolving state of LLM research and the specific requirements of clinical use cases:

  • Domain-specific fine-tuned LLMs: Some ClinicalGPT variants, such as the model described by Wang et al. (Wang et al., 2023), are built atop open-source LLMs (e.g., BLOOM-7B) and subjected to domain-specific supervised instruction tuning and reinforcement learning with human feedback (RLHF). Training incorporates sources such as medical knowledge graphs, real-world clinical records, medical exam question banks, and multi-turn patient-provider conversations. The loss functions and optimization procedures mirror text-to-text transformation frameworks (e.g., T5) and state-of-the-art RL (e.g., PPO with explicit KL penalty and reward models based on human likelihood ranking).
  • Prompt-based clinical task pipelines using general GPT APIs: Alternative implementations, particularly in screening, recruitment, and automated chart review, use robust prompt engineering to tailor generic APIs (e.g., OpenAI GPT-3.5/4) to high-value medical operations (Guo et al., 2023, Peikos et al., 2023, Guan et al., 2023, Rahmanian et al., 24 Apr 2024, Groza et al., 2023, Leng et al., 13 Feb 2025). Prompts may include natural language instructions, task- or feature-specific descriptions, relevant inclusion/exclusion criteria, knowledge graph triplets, and few-shot examples.
  • Multi-agent and modular reasoning systems: Advanced architectures, exemplified in ClinicalAgent (Yue et al., 23 Apr 2024), decompose complex tasks (e.g., trial outcome prediction) into subproblems coordinated by planning, domain, and reasoning agents. Each agent leverages GPT-4, task-specialized prompts, and external tool APIs, utilizing LEAST-TO-MOST and ReAct reasoning methodologies for modular decomposition and iterative resolution.
  • Ontology engineering and knowledge base extraction: LLMs have been used to automate the process of extracting clinical entities, endpoints, and relationships into formal ontologies (e.g., OWL) from large-scale clinical trial corpora (Çakır, 18 Dec 2024), emphasizing cost, speed, and quality metrics in comparison to manual curation.

2. Evaluation Benchmarks and Diagnostic Performance

Performance metrics for ClinicalGPT systems are typically benchmarked against human experts, traditional machine learning baselines, or standard pipelines, using a variety of domain-appropriate evaluation criteria:

  • Paper screening (eligibility/exclusion for reviews): Automated GPT-API screening achieves an overall accuracy of 0.91, with sensitivity for exclusions at 0.91 and inclusions at 0.76. Cohen’s kappa values show moderate inter-rater reliability (0.21–0.26) (Guo et al., 2023).
  • Complex clinical case diagnosis: On 50 challenging published clinical cases drawn from the Massachusetts General Hospital, GPT-4 attains a top-one diagnosis accuracy of 26% (46% in the top three picks) and essential diagnostic test accuracy of 28% on first attempt (44% in top three) (Poterucha et al., 2023). Repeated trials yield higher cumulative recall, but the ability to definitively resolve complex cases remains limited.
  • Clinical trial participant selection: In prompt-based learning frameworks for cohort identification from EHRs, GPT-3.5 Turbo achieves a micro F₁ of 0.9061 and a macro F₁ of 0.8060 on the n2c2 2018 challenge (Rahmanian et al., 24 Apr 2024). CohortGPT (GPT-4 + RL selection of chain-of-thought exemplars) attains F₁ of up to 0.81 in few-shot settings, outperforming fine-tuned BERT/BioGPT models on low-resource datasets (Guan et al., 2023).
  • Disease classification and pathway discovery: In disease prediction from EHR text or structured data, reported F₁ scores range from 74.7% (HSV) to 96% (COPD) for GPT-4, with variable precision/recall trade-offs and failure modes depending on information density and prompt structure (Zhang et al., 2023, Rezk et al., 16 Sep 2024, Castagnari et al., 20 Sep 2024).
  • Cognitive scoring and structured annotation: For identifying cognitive impairment stages from EHR notes, GPT-4o can achieve weighted Cohen's κ of 0.83 (memory clinic) and 0.91–0.96 (Medicare, high-confidence subset) when benchmarked against specialist review (Leng et al., 13 Feb 2025).
  • Clinical trial protocol generation and summarization: Protocol authoring using GPT-4 with engineered prompts achieves cosine similarity of up to 0.81 and BLEU/ROUGE metrics that closely track human-expert references (Maleki et al., 7 Apr 2024). Summarization of trial descriptions is made feasible for large batches via recursive, prompt-based pipelines (White et al., 2023).

3. Workflow Methodologies and Engineering Design

The deployment framework for ClinicalGPT is characterized by well-defined workflows:

  • Automated iterative screening: A Python pipeline iterates over thousands of abstracts, constructing standardized prompts that embed structured inclusion/exclusion criteria, sends requests to the GPT API, records binary decisions, and supports error examination via secondary “reasoning” prompts. Reflection and correction steps enhance transparency and permit retrospective error mitigation (Guo et al., 2023).
  • Cohort selection with medical ontologies: EHR notes are summarized to extract relevant SNOMED CT-annotated sentences; MedCAT annotates concepts, and prompts guide GPT models to output binary eligibility labels (e.g., “met”/“not met”) for each criterion (Rahmanian et al., 24 Apr 2024).
  • Knowledge graph– and rule–augmented prompting: Participant selection and diagnostic classification performance are boosted by injecting knowledge graph “rules” and dynamic, RL-selected exemplars into prompts, allowing context-aware, stepwise reasoning (chain-of-thought) (Guan et al., 2023).
  • Modular, agent-based orchestration: Multi-agent systems decompose complex tasks (e.g., trial outcome prediction), with each subproblem addressed via specialized agents (efficacy, safety, enroLLMent) running custom prompts and validation steps, then synthesized by a central reasoning agent (Yue et al., 23 Apr 2024).
  • Automated ontology extraction/merging: Individual clinical trial CSV records are processed by LLMs tasked with generating OWL code, then aggregated with an efficient synonym-list–driven merging algorithm that maintains concept de-duplication and integrates new ontology concepts in O(n) time (Çakır, 18 Dec 2024).

4. Interpretability, Reasoning, and Error Modes

ClinicalGPT systems incorporate measures for interpretability and reasoning fidelity:

  • Transparent rationales and correction: In screening and diagnosis, ClinicalGPT can be prompted to articulate explicit reasoning (“Explain your reasoning”) or to review and revise its decisions post hoc. Reflection prompts enable the system to recognize and correct misclassifications, providing auditing trails for human review (Guo et al., 2023).
  • Chain-of-thought and rule-based inference: Performance on diagnostic pathway tasks is maximized when LLMs employ chain-of-thought prompting and explicit decision rules (often extracted from clinical guidelines or decision trees), yielding high accuracy (up to 98.4% in anemia diagnosis with CoT prompting and sequential dialog) and interpretable step-by-step justifications (Castagnari et al., 20 Sep 2024).
  • Explainable decision support and user interface: AI clinical decision support systems such as GutGPT (GI bleeding risk) are integrated with dashboards that visualize model outputs (e.g., ICE, PDP, ALE plots) for interpretability and interactive querying. However, standalone LLM explanations can still generate factually incorrect or overconfident rationales in error cases (Chan et al., 2023, Zhang et al., 2023, Rezk et al., 16 Sep 2024).
  • Non-determinism and variability: Phenotype concept recognition and text annotation tasks reveal stochastic output variation (only ~76% reproducibility for identical inputs), cost- and prompt-sensitivity, as well as sensitivity to input ordering—highlighting ongoing challenges in deployment for high-stakes clinical applications (Groza et al., 2023, Rezk et al., 16 Sep 2024).

5. Limitations, Risks, and Human Oversight

Despite robust metrics in many domains, several critical limitations have been established for ClinicalGPT systems:

  • False negatives and recall trade-offs: In literature screening and disease detection, high overall accuracy masks moderate sensitivity for included/relevant items (e.g., 0.76 for inclusion in reviews, 62% recall for delirium risk) (Guo et al., 2023, Rezk et al., 16 Sep 2024). This suggests persistent risk of overlooking essential items or at-risk patients.
  • Factual hallucinations and over-prescription: LLMs sometimes provide incorrect rationales, hallucinate recommendations, or propose unnecessary tests/interventions, especially when prompted for management strategies or risk estimates outside their calibrated domain (Zhang et al., 2023, Rezk et al., 16 Sep 2024).
  • Privacy and regulatory concerns: Use of commercial LLMs introduces challenges related to patient data privacy (transmission outside secure environments) and regulatory approval, motivating modular deployments or transition to locally hosted models (Zhang et al., 2023).
  • Transparency and calibration issues: LLMs are generally unable to output calibrated probability estimates for risk prediction, may overemphasize recent/unstructured data, and show vulnerability to input order and context window truncation (Rezk et al., 16 Sep 2024).
  • Human-in-the-loop requirement: Multiple studies emphasize that LLM-based ClinicalGPT systems should serve as decision aids rather than autonomous agents; ultimate oversight and validation must remain with clinical professionals, particularly in safety-critical or legally regulated tasks (Guo et al., 2023, Zhang et al., 2023, Rezk et al., 16 Sep 2024).
  • Domain-specific fine-tuning and resource demands: The most effective ClinicalGPTs are fine-tuned on large, diverse, domain-specific datasets and augmented by RL (as in ClinicalGPT-R1), which requires significant computational and data resources and careful reward function design (Lan et al., 13 Apr 2025).

6. Applications, Scalability, and Future Directions

ClinicalGPT architectures are being applied and refined across the clinical data pipeline:

  • Automated screening and review: Large-scale manuscript screening—across tens of thousands of records—becomes feasible at a fraction of traditional effort/cost, e.g., 10 minutes/\$25 for thousands of abstracts (Guo et al., 2023).
  • Cohort selection and recruitment: Prompt-engineered LLMs with SNOMED CT integration, medical annotation, and optimized sentence selection now achieve state-of-the-art F₁s on EHR-based eligibility classification (Rahmanian et al., 24 Apr 2024); dedicated frameworks (CohortGPT) enable robust recruitment with minimal labeled data and improved efficiency (Guan et al., 2023).
  • Risk prediction and clinical scoring: LLM-derived features from unstructured notes (e.g., GPT-rate “risk of death”) significantly enhance EMR-based models for mortality and readmission, improving AUC and PPV among high-risk cohorts (Anderson et al., 14 Apr 2025). GPT-4o automates chart review for dementia staging, achieving near-perfect agreement in high-confidence cases (Leng et al., 13 Feb 2025).
  • Protocol and ontology generation: Prompt-based GPT-4 architectures enable end-to-end protocol authoring, with competitive BLEU/ROUGE metrics, and scalable ontology engineering suitable for updating research knowledge bases in near real-time (Maleki et al., 7 Apr 2024, Çakır, 18 Dec 2024).
  • Multi-agent and ensemble reasoning: ClinicalAgent and similar designs enable decomposed, explainable predictions for trial outcome forecasting, supported by expert codebases for community use (Yue et al., 23 Apr 2024).

Ongoing areas of research and refinement include (1) further integration of external structured knowledge and biomedical ontologies into LLM workflows, (2) standardized evaluation frameworks for clinical reasoning and decision-making tasks, (3) approaches to reproducibility, calibration, and error correction under non-determinism, and (4) domain adaptation/fine-tuning pipelines for specialty-specific applications.

7. Comparative Summary of Key Technical Results

Task/Domain ClinicalGPT Framework Top Metric(s) Notable Context/Strength/Limitation
Manuscript Screening Prompt-based GPT API (Guo et al., 2023) Accuracy 0.91, inclusion sensitivity 0.76 High efficiency and transparency, modest kappa
Diagnosis – Hard Cases GPT-4, GPT-3.5 (Poterucha et al., 2023) Top-1 26%, Top-3 46% Difficult to unify complex differential diagnoses
Trial EnroLLMent (Cohort) GPT-3.5 Turbo (Rahmanian et al., 24 Apr 2024) Micro F₁ 0.91, Macro F₁ 0.81 Integration of SNOMED CT, robust on short text
Reasoning LLM (diagnosis) ClinicalGPT-R1 (Lan et al., 13 Apr 2025) > GPT-4o (Chinese), ≈ GPT-4 (English) Explicit CoT, SFT+RL, MedBench-Hard
Clinical Trial Prediction ClinicalAgent (Yue et al., 23 Apr 2024) PR-AUC 0.7908 Multi-agent, ReAct, LEAST-TO-MOST
Clinical Risk (Delirium) GPT-4, clinalytix (Rezk et al., 16 Sep 2024) Precision 98%, Recall 62%, F₁ 76% High false negative risk, lacks calibrated probs
Cognitive Chart Review GPT-4o (Leng et al., 13 Feb 2025) Kappa 0.79–0.96 (task-dependent) Near-perfect for high-confidence splits
Protocol/KB Generation GPT-4 (Maleki et al., 7 Apr 2024, Çakır, 18 Dec 2024) Cosine sim up to 0.81, inclusion 86% Scalable, cost/time efficient, needs relation tuning

References

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