Evaluating the Effectiveness of Artificial Intelligence in Predicting
  Adverse Drug Reactions among Cancer Patients: A Systematic Review and
  Meta-Analysis
Fatma Zahra Abdeldjouad Menaouer Brahami Mohammed Sabri
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.