Evaluating the Effectiveness of Artificial Intelligence in Predicting Adverse Drug Reactions among Cancer Patients: A Systematic Review and Meta-Analysis

TL;DR

This systematic review and meta-analysis evaluates the performance of AI models in predicting adverse drug reactions in cancer patients, showing high accuracy but highlighting the need for standardized research.

Contribution

First comprehensive meta-analysis assessing AI effectiveness in predicting ADRs in cancer patients, pooling sensitivity, specificity, and AUC across multiple studies.

Findings

AI models show high sensitivity (0.82) and specificity (0.84) in predicting ADRs.

Biomarkers are effective but underutilized in current AI models.

Standardized, multicenter studies are needed to strengthen evidence.

Abstract

Adverse drug reactions considerably impact patient outcomes and healthcare costs in cancer therapy. Using artificial intelligence to predict adverse drug reactions in real time could revolutionize oncology treatment. This study aims to assess the performance of artificial intelligence models in predicting adverse drug reactions in patients with cancer. This is the first systematic review and meta-analysis. Scopus, PubMed, IEEE Xplore, and ACM Digital Library databases were searched for studies in English, French, and Arabic from January 1, 2018, to August 20, 2023. The inclusion criteria were: (1) peer-reviewed research articles; (2) use of artificial intelligence algorithms (machine learning, deep learning, knowledge graphs); (3) study aimed to predict adverse drug reactions (cardiotoxicity, neutropenia, nephrotoxicity, hepatotoxicity); (4) study was on cancer patients. The data were extracted and evaluated by three reviewers for study quality. Of the 332 screened articles, 17 studies (5%) involving 93,248 oncology patients from 17 countries were included in the systematic review, of which ten studies synthesized the meta-analysis. A random-effects model was created to pool the sensitivity, specificity, and AUC of the included studies. The pooled results were 0.82 (95% CI:0.69, 0.9), 0.84 (95% CI:0.75, 0.9), and 0.83 (95% CI:0.77, 0.87) for sensitivity, specificity, and AUC, respectively, of ADR predictive models. Biomarkers proved their effectiveness in predicting ADRs, yet they were adopted by only half of the reviewed studies. The use of AI in cancer treatment shows great potential, with models demonstrating high specificity and sensitivity in predicting ADRs. However, standardized research and multicenter studies are needed to improve the quality of evidence. AI can enhance cancer patient care by bridging the gap between data-driven insights and clinical expertise.