LLM-Based Medication Safety Review System

Updated 31 December 2025

LLM-Based Medication Safety Review System is a technology that integrates large language models and clinical domain knowledge to automate the detection, triage, and assessment of medication-related risks.
It leverages hybrid pipelines like retrieval augmentation and multi-agent configurations to achieve high accuracy (up to 91%) while reducing computational cost by approximately 60%.
Key challenges include contextual reasoning gaps, alert fatigue, and generalizability issues, underscoring the need for continuous learning and improved clinical workflow integration.

An LLM-Based Medication Safety Review System applies LLMs to automate the detection, triage, and contextual assessment of medication-related risks in clinical, digital health, and patient-generated data streams. These systems are architected for rigorous scalability, integrating deep learning with clinical domain knowledge—frequently through retrieval augmentation, graph-based reasoning, or hybrid pipelines—to identify misuse, adverse events, contraindications, and complex interaction hazards. Evaluation on open-domain queries, structured EHRs, synthetic and real patient cohorts shows that high-performing frameworks can approach or reach clinical expert-level sensitivity, but contextual reasoning, alert fatigue, and generalizability remain dominant sources of error. The emergence of agentic and multi-agent configurations, continuous benchmarking, and dynamic, risk-weighted scoring underpins the current state of the art.

1. Dataset Foundations and Annotation for Clinical Safety

Robust LLM-based medication safety review systems depend on carefully curated and extensively annotated datasets. For instance, a large-scale collection effort harvested 47,457 medication Q&A pairs from NIH-affiliated resources, filtering for user-posed questions with explicit drug mentions and excluding editorial content or insufficient queries (Goncharok et al., 15 Sep 2025). Rigorous preprocessing removed non-clinical signals, standardized semantics, and focused on questions implicating potential misuse and risk.

Annotation employed explicit clinical risk schemas. Questions were classified as "Critical" if they implied overdose, dangerous drug interactions, or emergent symptoms, and "General" for routine clarifications. Expert MD annotators achieved substantial inter-rater reliability (Cohen’s κ = 0.78 on pilot). The resultant benchmark (N = 650) exhibited an imbalanced but realistic distribution ( $P(\text{crit}) = 0.154$ , $P(\text{gen}) = 0.846$ ), structuring the foundation for supervised safety prediction tasks.

This annotation paradigm supports generalizable model development: further extension to multi-tier criticality ("low", "moderate", "high") is recommended for deployment in real-world settings (Goncharok et al., 15 Sep 2025).

2. Model Architectures, Hybrid Pipelines, and Routing Strategies

LLM-based safety review systems leverage both classical ML and modern deep learning, frequently integrating several technical subsystems for robust triage and risk structuration.

Classical ML and Deep LLMs

Traditional pipelines use TF-IDF feature matrices with support vector machines, logistic regression, random forests, multinomial Naive Bayes, and gradient boosting. Dimensionality reduction (SVD, k=200 factors) and criticality-similarity metrics supplement discriminative power (Goncharok et al., 15 Sep 2025). LLM-based methods uniformly outperform classical models. Fine-tuned BERT and its biomedical variants (BioBERT, BlueBERT) reach macro-F1 > 0.90 and ROC AUC > 0.94; GPT-3.5 in-context few-shot variants achieve strong but slightly inferior performance.

Hybrid and Agentic Systems

Scalable structuration of posology (dosing instructions) deploys a hybrid architecture: low-confidence cases from a Named Entity Recognition/Linking (NERL) pipeline are dynamically routed to fine-tuned LLMs, selecting outputs based on calibrated uncertainty scores (threshold τ = 0.8) (Bobkova et al., 24 Jun 2025). This configuration attains 91% accuracy while reducing computational cost—LLM calls are limited to ≈30% of cases, with end-to-end latency ~1.2 min and ~60% GPU cost reduction versus LLM-only processing.

Knowledge graph–augmented multi-stage agents (e.g., Rx Strategist) further decompose reasoning into indication screening, dosage verification, and interaction checking, each stage tightly bound to structured domain knowledge and embedding retrieval (Van et al., 2024). This approach increases reliability (F₀.₅ = 82.67%) and precision, reducing harmful false positives relative to monolithic LLMs.

3. Evaluation Frameworks, Benchmarks, and Performance Metrics

Evaluation is grounded in explicit safety-centric benchmarks and multi-dimensional scoring paradigms.

Scenario-Based and MCQ Benchmarks

Simulated consultation environments (RxSafeBench) combine RxRisk DB (6,725 contraindications, 28,781 interactions) with 2,443 high-quality scenarios to systematically challenge LLMs on both explicit and implicit risk reasoning (Zhao et al., 6 Nov 2025). Multiple-choice question formats enforce correct medication selection under controlled contexts. Contemporary LLMs demonstrate limitations in integrating latent contraindication and interaction rules, particularly on implicit scenarios where accuracy drops by ≈20%.

Multidimensional Scoring

CSEDB introduces a dual-track, risk-weighted benchmark covering 30 criteria, 17 safety gates (11 medication-focused) with quantifiable consequence measures (Wang et al., 31 Jul 2025). Absolute contraindications (binary score), graded dose adjustments (contextual score), and composite risk indices (e.g., med-safety risk index $R = 1 - \frac{\sum w_i s_i}{\sum w_i}$ ) collectively inform operational thresholds. Domain-specific medical LLMs outperform general models in safety (0.912) and effectiveness (0.861) tracks.

Agentic and Chain-of-Thought Evaluation

Multi-agent frameworks (MDT-mimic MAS) enable dynamic allocation of conflicts, round-based expert consensus, and mediation (Wu et al., 15 Jul 2025). Metrics surpass basic precision/recall, incorporating clinical goal satisfaction ( $S_{\text{goal}}$ ), medication burden ( $M_{\text{burden}}$ ), and conflict ratios (e.g., DDI-R, CR), enabling meaningful inspection of clinical value.

Comparative Results Table (Sample)

Method / Model	Accuracy	F1	ROC AUC
BioBERT (LLM, QA Risk)	0.92	0.90	0.94
Rx Strategist (Hybrid KG)	0.76	0.83	N/A
MAS (Multi-Agent)	N/A	0.90	N/A
RxSafeBench (Best LLMs)	0.59	0.62	0.85

Interpretation: Fine-tuned biomedical LLMs set current accuracy benchmarks for criticality classification; hybrid agentic systems excel on precision-weighted tasks; multi-agent architectures marginally boost completeness and conflict resolution.

4. Alerting, Guardrail Mechanisms, and Operational Deployment

Real-time triage systems require sub-second latency, robust throughput, and layered safety interventions.

Data is ingested via streaming APIs, normalized, and fed into LLM classifiers (e.g., BioBERT). Decision thresholds (τ = 0.5 or F1-tuned) trigger alerts pushed to clinician dashboards (Goncharok et al., 15 Sep 2025). All critical queries are subject to post-decision filtering, model versioning, deployment in containerized microservices, rate-limiting, secure storage, and human-in-the-loop verification in the initial production phase.

Safety guardrails comprise hybrid rule-based filters for high-risk keywords (“suicide,” “overdose”), adversarial prompt filtering, toxic-content detectors, and schema validation against domain standards. Continuous logging and periodic calibration capture both false positives/negatives and emergent failure modes.

Alert fatigue mitigation strategies employ individualized suppression (e.g., ABR, threshold ascent), continuous active learning, and retraining protocols if per-class F1 dips below operational thresholds (Vito et al., 2024).

5. Limitations and Dominant Error Patterns

Scaling from synthetic and benchmark data to real-world populations unveils persistent gaps in contextual reasoning and workflow alignment.

A real-world NHS study deploying a 120B-parameter LLM on primary care EHRs revealed perfect sensitivity (100%) and high specificity (83.1%) for issue detection, yet only 46.9% full correctness in issue identification and intervention recommendation (Normand et al., 24 Dec 2025). Dominant error patterns included:

Overconfidence in uncertainty (premature actions absent data, overlooked safety concerns)
Protocol-versus-patient gap (rigid rule application to frail/palliative contexts)
Protocol-versus-practice gap (misreading prescribing conventions, omitting secondary-care context)
Hallucinated facts (incorrect drug composition, guideline misapplication)
Process blindness (unsafe sequencing, ignores stepwise clinical logic)

*Contextual reasoning outweighs factual knowledge gaps as the major failure driver (86% of all errors), emphasizing the need for explicit process modeling, uncertainty calibration, and integration with practice-oriented logic modules (Normand et al., 24 Dec 2025).

6. Future Directions and Clinical Integration

Multi-agent orchestration, risk-weighted dynamic scoring, and continual learning underpin next-step recommendations:

Embed retrieval-augmented pipelines and structured expert consensus scoring into clinical workflow (CSEDB, RxSafeBench, RAG-LLM) (Wang et al., 31 Jul 2025, Zhao et al., 6 Nov 2025, Garza et al., 1 Oct 2025).
Implement agentic architectures that solicit information, defer decisions when data is missing, and explicitly sequence clinical reasoning (Normand et al., 24 Dec 2025).
Expand datasets to encompass real EHR-labeled ADE events, broader drug-condition knowledge, and patient-specific risk modeling.
Benchmark and calibrate operational safety using multi-rater consensus, dynamic override tracking, and periodic re-annotation.
Evaluate human–AI teaming (co-pilot junior pharmacist + LLM) in live deployments, with performance superiority validated in context-aware DRP detection (accuracy boost +23%) (Ong et al., 2024).
Deploy systems with modular extension for new practice standards, targeted retraining, and escalation protocols under high residual risk indices.

LLM-based medication safety review systems represent a rapidly maturing intersection of deep learning, structured knowledge integration, clinical workflow engineering, and regulatory-grade safety assurance. Their continued evolution depends critically on aligning technological advances with the operational realities, error profiles, and documentation rigor of clinical medicine.