Digital Healthcare and AI in Thoracic and Breast Oncology: Turning Algorithms into Better Care

Artificial intelligence and digital health innovations are transforming how patients with lung and breast cancers are diagnosed, treated, and followed. The field is moving beyond algorithm development toward implementation, integrating AI into clinical decision making, workflow optimization, and risk management to improve patient outcomes and health system efficiency.This Research Topic focuses on AI and digital health applications that demonstrably improve care rather than prove technical feasibility. We prioritize work that links AI implementation to measurable benefits for patients, clinicians, and systems, including better diagnosis, treatment selection, surgical planning, prediction of toxicities and complications, improved survival, quality of life, patient-reported outcomes (PROMs), and economic efficiency.We invite contributions including but not limited to:• Prospective and real-world studies demonstrating AI impact on clinical decision making or patient outcomes.• Digital tools for precision screening, diagnosis, and treatment pathway optimization in lung and breast cancers.• Integrative AI models combining radiomics, pathomics, and genomic signals with clinical data.• Predictive modeling for treatment response, toxicity prediction, and survivorship care planning.• Implementation science and health economics studies evaluating workflow efficiency and cost-effectiveness.• AI-augmented tools that enhance communication, PROMs collection, and patient engagement.By centering practical impact, this special issue aims to define how digital health and AI can create meaningful improvements in thoracic and breast cancer care, from diagnosis to survivorship. We welcome original research, systematic reviews, and implementation frameworks focused on clinically validated, patient-centered AI solutions.Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Eduard-Alexandru Bonci, Luca Bertolaccini, Valeria Dionisi Source: https://www.frontiersin.org/research-topics/78903/digital-healthcare-and-ai-in-thoracic-and-breast-oncology-turning-algorithms-into-better-care