Transforming Cancer Care: A Narrative Review on Leveraging Artificial Intelligence to Advance Immunotherapy in Underserved Communities

Abstract

Purpose: Cancer immunotherapy has transformed oncology, but underserved populations face persistent disparities in access and outcomes. This review explores how artificial intelligence (AI) can help mitigate these barriers. Methods: We conducted a narrative review based on peer-reviewed literature selected for relevance to artificial intelligence, cancer immunotherapy, and healthcare challenges, without restrictions on publication date. We searched three major electronic databases: PubMed, IEEE Xplore, and arXiv, covering both biomedical and computational literature. The search included publications from January 2015 through April 2024 to capture contemporary developments in AI and cancer immunotherapy. Results: AI tools such as machine learning, natural language processing, and predictive analytics can enhance early detection, personalize treatment, and improve clinical trial representation for historically underrepresented populations. Additionally, AI-driven solutions can aid in managing side effects, expanding telehealth, and addressing social determinants of health (SDOH). However, algorithmic bias, privacy concerns, and data diversity remain major challenges. Conclusions: With intentional design and implementation, AI holds the potential to reduce disparities in cancer immunotherapy and promote more inclusive oncology care. Future efforts must focus on ethical deployment, inclusive data collection, and interdisciplinary collaboration.

Keywords: Predictive Modeling; artificial intelligence; cancer immunotherapy; health disparities; social determinants of health (SDOH).

Figure 1

AI Applications and Predictive Models in Addressing Disparities and Adverse Event Management. Figure 1 illustrates key applications of artificial intelligence in healthcare—Data Analysis, Predictive Modeling, Clinical Trials, and Side Effect Mitigation—and their collective impact on diverse populations. Positioned at the center of the figure, the outcomes emphasize AI’s potential to reduce health disparities and proactively manage adverse events through inclusive, data-driven strategies.

Figure 2

AI-driven algorithm to improve immunotherapy accessibility in underserved communities. This flowchart presents a six-step AI framework—ranging from data integration and risk stratification to AI-enhanced diagnosis, decision support, remote monitoring, and ethical oversight—designed to reduce disparities and enhance delivery of advanced immunotherapy. The model emphasizes equitable data use, clinical support for non-specialists, patient engagement in low-resource settings, and transparent, bias-aware implementation.