Emerging Technologies in Cancer Care and Research: Exploring the Intersections of AI, Blockchain and Connected Systems for a New Era of Oncology

Cancer care is undergoing a paradigm shift driven by the integration of advanced technologies across the continuum of diagnosis, treatment, and long-term management. Innovations in blockchain, Internet of Things (IoT), embedded systems, and federated learning, alongside artificial intelligence (AI) are opening new possibilities for personalized, efficient, and secure oncology care.
AI is being applied to enhance diagnostic accuracy through medical imaging, histopathology, and genomic profiling. It also supports clinical decision-making through risk prediction, treatment optimization, and survival forecasting. At the same time, blockchain technology is being explored for secure health data management, interoperable patient records, and transparent clinical trial systems. IoT devices, wearable sensors, and embedded systems enable real-time monitoring, automated drug delivery, and portable point-of-care diagnostics. These are particularly impactful in home-based or rural cancer care. Federated learning and edge computing further support privacy-preserving collaboration across distributed clinical datasets.
Despite their promise, these technologies face several implementation challenges. Clinical validation, regulatory compliance, cybersecurity, and ethical concerns around data privacy and algorithmic bias must be carefully addressed. Moreover, access to these innovations remains uneven across healthcare systems, underscoring the importance of scalable, inclusive, and context-sensitive solutions that can be deployed in real-world oncology practice.
This Research Topic seeks to highlight the growing role of emerging technologies beyond AI alone in cancer research and clinical practice. We welcome contributions that explore how digital tools and intelligent systems improve outcomes, reduce disparities, and shape oncology's future.
Sub Topics of interest include, but are not limited to:
● Applications of AI and machine learning in oncology for diagnosis, prognosis, and treatment planning
● Use of blockchain in clinical data security, research integrity, and decentralized health systems
● Development and deployment of IoT-enabled or wearable devices for cancer monitoring and intervention
● Embedded systems for point-of-care diagnostics or smart therapeutic delivery
● Federated learning, edge computing, and privacy-preserving AI in oncology settings
● Technology-driven approaches to improve cancer care in low-resource or remote environments
● Multidisciplinary innovations that integrate oncology with biomedical engineering, health informatics, and data science
● Ethical and policy considerations related to emerging technologies in cancer care
Submissions may include original research articles, systematic reviews, mini-reviews, clinical trial reports, brief research reports, or expert perspectives. Case Reports are not within the scope of this Topic. Studies focusing on both adult and pediatric populations are welcome. Collaborative work across clinical, technical, and academic disciplines is particularly encouraged. 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.