AI-Assisted Chemotherapy Regimen Selection and Its Effects on Clinical Outcomes and Adverse Drug Reactions: A Systematic Review.

Authors

Abhishek Vadher, Swati Baraiya, Bobbadi Gajendra Siva Krishna Pavan Kumar, Utsav R Thakkar, Mohmadmahikhan F Pathan, Swathi Kambhatla, Faizkhan F Pathan, Pranay Gupta, Mayur Patel, Sujata Kambhatla

Published in

Cureus. Volume 18. Issue 6. Pages e111337. Epub Jun 23, 2026.

Abstract

Selection of optimal chemotherapy regimens remains a complex clinical challenge due to interpatient heterogeneity, evolving therapeutic options, and the limitations of population-based clinical guidelines. AI has emerged as a promising tool to support precision oncology by integrating multidimensional data to guide individualized treatment decisions. This systematic review evaluates the role of AI-based models in chemotherapy regimen selection, focusing on their impact on treatment efficacy and adverse drug reactions compared with conventional physician-driven decision-making. A systematic literature search was conducted up to March 21, 2026. Studies evaluating AI-guided chemotherapy selection or treatment decision-support systems in cancer patients were included. The population, exposure, comparison, and outcomes (PECO) framework included cancer patients receiving AI-guided chemotherapy selection versus physician judgment or guideline-based care, with outcomes including survival, treatment response, and toxicity. A total of 1,409 records were identified, with 15 studies meeting the inclusion criteria after screening and eligibility assessment. The included studies encompassed diverse malignancies, including breast, prostate, pancreatic, lung, head and neck, glioblastoma (GBM), hepatocellular carcinoma (HCC), nasopharyngeal carcinoma (NPC), and acute myeloid leukemia (AML). AI models utilized multimodal data sources, such as clinical variables, histopathology, imaging, and multi-omics datasets. Across studies, AI-guided treatment selection was associated with improvements in several clinical outcomes, including overall survival, progression-free survival, and pathological response rates. Several models showed an enhanced ability to identify patients unlikely to benefit from specific chemotherapies, thereby enabling treatment de-escalation. Limited but notable evidence suggested reductions in treatment-related toxicity, particularly cardiotoxicity, when AI-guided strategies were employed. Most studies compared AI performance against physician clinical judgment or guideline-based approaches. AI-assisted chemotherapy regimen selection shows considerable potential to improve treatment efficacy and personalize oncology care while reducing unnecessary toxicity. Although current evidence is largely retrospective and heterogeneous, findings consistently support AI as a valuable adjunct to clinical decision-making. Prospective validation and integration into real-world workflows are essential to establish its role in routine cancer care.

PMID:
42495464
Bibliographic data and abstract were imported from PubMed on 24 Jul 2026.

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