Intersection of Artificial Intelligence and Cancer Care: Enhancing Equity and Clinical Decision-Making

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Shelly Soffer 1,2

1 Institute of Hematology, Davidoff Cancer Center, Rabin Medical Center; Petah-Tikva, Israel

2 Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel

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Artificial intelligence (AI), particularly large language models (LLMs), is increasingly being explored in oncology. Our research examines how advanced technologies such as generative adversarial networks (GANs) and transformers impact cancer treatment and pain management. Through comprehensive studies involving diverse patient populations, we explore whether AI alleviates or inadvertently reinforces healthcare disparities.

 

Our findings indicate that while AI may enhance clinical efficiency, it can also amplify existing biases, affecting historically marginalized groups disproportionately. These algorithmic biases risk reinforcing disparities rather than mitigating them, highlighting the critical need for careful evaluation.

 

This presentation will share specific case studies demonstrating AI's potential and challenges in clinical oncology. It emphasizes the importance of rigorous bias assessment, transparency, and standardized safeguards. Ultimately, it advocates a collaborative approach among researchers, clinicians, and policymakers to responsibly implement AI technologies, ensuring equitable, effective, and safe cancer care for all patients.