Can AI agents improve Indian cancer care?
The potential role of AI agents in transforming nuclear medicine research and cancer management in India
This paper explores the potential of AI agents to revolutionize nuclear medicine research and cancer management in India, addressing challenges in infrastructure, research, and clinical practice.
LLM-based multi-agent systems can be leveraged for:
- Accelerated research: Automating tasks, analyzing large datasets, and refining hypotheses.
- Enhanced diagnostics: Integrating multimodal data (images, genomics) for personalized assessments and streamlining workflows.
- Improved clinical trials: Optimizing patient recruitment, monitoring compliance, and generating hypotheses from data trends.
- Public health initiatives: Identifying high-risk groups and optimizing resource allocation for targeted interventions.
- Personalized treatment: Predicting patient responses based on genomic and medical data using theranostic digital twins and pharmacokinetic modeling.
- Resource optimization: Managing radioisotope production and distribution.
Key modules for these agents include perception (multimodal data processing), interaction (natural language communication), memory (knowledge storage and retrieval), and reasoning (hypothesis generation and decision-making). A phased approach is proposed, addressing data and regulatory frameworks, agent development, infrastructure, education, and large-scale deployment. Challenges include data standardization, robustness/reliability of AI, ethical considerations, and evaluation/governance. Opportunities lie in improved diagnostic accuracy, optimized treatment plans, and faster drug discovery.