AI in Oncology Training: From Obedient Assistant to Intelligent Advisor

The field of oncology is rapidly being transformed by advances in artificial intelligence (AI), with growing evidence supporting its ability to improve the delivery and quality of cancer care. Yet, despite over two decades of interest, the integration of AI into clinical decision-making remains a source of debate within the medical community. While early representations of AI as a competitor to physicians generated unease, recent frameworks now emphasize its capacity as an intelligent collaborator. Notably, AI has demonstrated prowess in augmenting the reading of radiological images and pathology slides, streamlining pattern recognition, and generating diagnostic insights at a pace and accuracy previously unattainable. In addition, novel machine learning tools aggregate vast troves of literature and patient data, offering clinicians rapid, evidence-informed guidance, and assisting in navigation within cumbersome electronic health record systems. Despite these advances, challenges persist in optimizing AI’s integration, ensuring equity in clinical trial access, minimizing errors, and fully capturing its capacity to enhance prognostication and workload management.This Research Topic aims to explore how artificial intelligence can best be developed and deployed to serve as a trusted advisor in oncology, curating scientific knowledge, supporting electronic health record systems, elevating patient safety, and advancing clinical trial participation. Key questions include which clinical workflows benefit most from AI augmentation, how oncologists and patients perceive AI-enabled platforms, and what infrastructural or ethical considerations must be addressed to ensure equitable and impactful integration. The objective is to move beyond the vision of AI as a tool to replace clinicians and towards a model in which AI meaningfully complements the expertise of oncology professionals, improves patient experiences, and drives better outcomes.The scope of this Research Topic encompasses the use of artificial intelligence in supporting decision-making, workflow optimization, and patient care within the field of oncology. Contributions should focus on both established applications and emerging opportunities for AI to enhance clinical practice, as well as address limitations, barriers, and future prospects. To gather further insights into the transformative potential and challenges of AI in oncology, we welcome articles addressing, but not limited to, the following themes:- AI-powered curation of scientific literature for oncology decision-making.- Augmentation of electronic health record (EHR) systems using AI.- AI-driven strategies for improving clinical trial identification and patient enrolment.- The use of machine learning for outcome prediction and drug dosing optimization.- Ethical, infrastructural, and experiential perspectives on integrating AI in oncology practice.- Real-world studies and evidence of AI’s impact on clinician workload and patient safety.- Strategies to ensure equity and inclusivity in AI-supported oncology care.Please note that manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases that are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of the scope of this Research Topic. Topic editors: AVS Suresh, Purvish M Parikh Source: https://www.frontiersin.org/research-topics/78523/ai-in-oncology-training-from-obedient-assistant-to-intelligent-advisor