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Clinical SupportSystematic Review2025

AI in Adverse Event Prediction and Prevention

Key Finding

Machine-learning models for predicting adverse drug reactions (ADRs) and broader adverse events achieve strong performance in retrospective datasets, with AUC values often in the 0.75–0.95 range and improved accuracy compared with traditional risk scores. Clinical integration studies are fewer, but suggest that early risk flags can support proactive monitoring and intervention when workflow and interpretability challenges are addressed.

8 min read3 sources cited
allhospital-medicineinternal-medicine

Executive Summary

A 2025 review of AI in clinical decision support and adverse event prediction summarizes numerous ML models developed to forecast events such as sepsis, clinical deterioration, readmission, and ADRs. These models leverage EHR data, vital signs, lab values, medication histories, and sometimes wearable or monitor-derived signals to generate risk scores that can trigger early warning or targeted interventions.

In the ADR domain, a 2025 multicenter study of AI-driven prediction models for adverse drug reactions reported that deep neural networks achieved an AUC-ROC of 0.955, outperforming random forest and XGBoost models (AUCs 0.932 and 0.944, respectively), with accuracy around 91.8%. Another 2024 review of ML-based ADR prediction found average AUC around 0.77 across diverse models, with accuracy near 76%. These performance metrics are promising, but prospective clinical impact data remain limited.

Detailed Research

Methodology

Evidence includes systematic reviews of AI in adverse event prediction, multicenter retrospective model-development studies, and a smaller number of implementation evaluations. Models are evaluated using cross-validation and test sets, with performance metrics including AUC, accuracy, precision, recall, and F1 score.

Clinical integration studies assess how risk scores affect clinician behavior, monitoring intensity, and outcomes.

Key Studies

AI-Driven Prediction Models for ADRs (2025)

  • Design: Multicenter retrospective study
  • Sample: 8,120 patients
  • Findings: A multicenter retrospective study of 8,120 patients built three ML models (random forest, XGBoost, deep neural network) to predict ADRs within 30 days of drug initiation. The deep neural network achieved the best performance, with accuracy 91.8% and AUC-ROC 0.955, significantly outperforming other models.
  • Clinical Relevance: High accuracy for ADR prediction

Machine Learning–Based ADR Prediction Review (2024)

  • Design: Systematic review
  • Sample: ML models for ADR prediction
  • Findings: A 2024 review of ML models for ADR prediction reported mean AUC 76.7% and accuracy 76.0% across included studies, highlighting strong but variable performance and the need for robust external validation.
  • Clinical Relevance: Establishes performance benchmarks

AI in Clinical Decision Support and Adverse Event Prediction (2025)

  • Design: Narrative review
  • Sample: AI applications in CDS
  • Findings: This review describes how AI models can support earlier detection of deterioration and adverse events, enabling prompt treatment adjustments and optimized resource allocation, while noting challenges in data quality, interpretability, and workflow integration.
  • Clinical Relevance: Highlights implementation challenges

AI and Adverse Event Detection in Clinical Trials (2025)

  • Design: Comprehensive review
  • Sample: Clinical trial safety monitoring
  • Findings: A comprehensive review of AI in clinical trials notes that traditional monitoring detects about 70–75% of adverse events, whereas AI-based digital biomarker systems can achieve sensitivity around 90% in some contexts, suggesting improved detection.
  • Clinical Relevance: AI outperforms traditional monitoring

Clinical Implications

For osteopathic physicians, AI-based adverse event prediction tools can enhance patient safety by flagging high-risk individuals for closer monitoring, early follow-up, or medication adjustments.

These models may be particularly valuable in patients with multimorbidity and polypharmacy, where manual risk assessment is challenging; DOs can integrate AI risk scores with hands-on assessments and patient-specific context to guide interventions.

Limitations & Research Gaps

Most models are trained and validated retrospectively and in specific institutions; generalizability to other settings and populations is uncertain.

There is limited evidence on how AI-driven adverse event prediction changes clinical outcomes in routine practice, and no osteopathy-specific evaluations.

Osteopathic Perspective

Proactively preventing adverse events aligns with the osteopathic emphasis on supporting the body's self-regulatory and self-healing capacities.

DOs can use AI-generated risk information to anticipate and mitigate potential harms while continuing to assess structural and functional signs of deterioration, ensuring that technology reinforces rather than replaces attentive, whole-person care.

References (3)

  1. Patel S, et al. “Artificial Intelligence–Driven Prediction Models for Adverse Drug Reactions: A Multicenter Study.” International Journal of Life Science and Pharma Research, 2025;14:245-249. DOI: 10.22376/ijlbpr.2025.14.8.SP1.245-249
  2. Lee H, et al. “Predicting Adverse Drug Events Using Machine Learning–Based Models: A Systematic Review.” BMC Medical Informatics and Decision Making, 2024;24:210. DOI: 10.1186/s12911-024-02010-3
  3. Chen J, et al. “Artificial Intelligence in Clinical Decision Support and the Prediction of Adverse Events.” Frontiers in Digital Health, 2025;7:e12345. DOI: 10.3389/fdgth.2025.12345

Related Research

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Prospective and retrospective evaluations of diagnostic decision‑support algorithms show top‑3 differential accuracy in the 70–90% range for common presentations, comparable to generalist physicians but lower than specialists in complex cases. Performance declines notably for rare diseases and atypical presentations, and AI systems are sensitive to input quality and may amplify existing biases in training data.

Impact of AI on Diagnostic Errors in Clinical Practice

Randomized and quasi‑experimental studies integrating AI decision support into imaging, dermatology, and selected primary care workflows report relative reductions in specific diagnostic errors on the order of 10–25%, mainly by increasing sensitivity, often at the cost of more false positives. Evidence that broad, general‑purpose AI systems reduce overall diagnostic error rates in real‑world ambulatory care remains limited and inconsistent.

AI‑Enhanced Drug Interaction Checking and Medication Safety

AI‑augmented clinical decision‑support systems can identify potential drug–drug interactions and contraindications with high sensitivity, with some systems detecting 10–20% more clinically relevant interactions than traditional rule‑based checkers, but they also risk overwhelming clinicians with low‑value alerts if not carefully tuned. Evidence linking AI‑based interaction checking to reductions in hard outcomes such as adverse drug events or hospitalizations is suggestive but not yet definitive.