The potential role of AI agents in transforming nuclear medicine research and cancer management in India
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Abstract (arXiv)
India faces a significant cancer burden, with an incidence-to-mortality ratio indicating that nearly three out of five individuals diagnosed with cancer succumb to the disease. While the limitations of physical healthcare infrastructure are widely acknowledged as a primary challenge, concerted efforts by government and healthcare agencies are underway to mitigate these constraints. However, given the country's vast geography and high population density, it is imperative to explore alternative soft infrastructure solutions to complement existing frameworks. Artificial Intelligence agents are increasingly transforming problem-solving approaches across various domains, with their application in medicine proving particularly transformative. In this perspective, we examine the potential role of AI agents in advancing nuclear medicine for cancer research, diagnosis, and management in India. We begin with a brief overview of AI agents and their capabilities, followed by a proposed agent-based ecosystem that can address prevailing sustainability challenges in India nuclear medicine.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 10 Mar 2025.
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11 of 33 references.
References · 11
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2025Accelerating scientific discovery with Co-Scientistcited by 401
- 2023Recommender Systems with Generative Retrievalcited by 198
- 2019Causality matters in medical imagingcited by 193
- 2024Empowering Biomedical Discovery with AI Agentscited by 118
- 2023Matching Patients to Clinical Trials with Large Language Modelscited by 69
- 2025OctoTools: An Agentic Framework with Extensible Tools for Complex Reasoningcited by 69
- 2025Vision Foundation Models for Computed Tomographycited by 47
- 2024LLaVA Needs More Knowledge: Retrieval Augmented Natural Language Generation with Knowledge Graph for Explaining Thoracic Pathologiescited by 11
- 2024Panacea: A foundation model for clinical trial search, summarization, design, and recruitmentcited by 10
- 2018Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Codingcited by 1
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