AI for Biomedical Technology

Aims & scope:

Recent advances in artificial intelligence—including deep learning, generative models, and multimodal data fusion—are catalyzing a paradigm shift in biomedical technology, transforming conventional diagnostic and therapeutic instruments into intelligent, adaptive systems capable of real-time prediction, precise biological interaction, and personalized intervention. These transformations are exemplified by AI-enhanced biomedical imaging and computational modeling, which provide dynamic insights into complex physiological mechanisms and enable more accurate and timely clinical decision-making.

Furthermore, the scope of AI's impact extends into public health, where its application to pathogen genomics is creating powerful new tools for real-time disease surveillance and clinical investigation of outbreaks.

This special issue aims to highlight cutting-edge research at the interface of AI and biomedical sciences. We focus on how AI is fundamentally transforming biomedical discovery, diagnosis, and therapeutic development, driving novel insights and innovative solutions that advance healthcare and life sciences.

Topics of interest include, but are not limited to:

• Generative AI approaches for de novo drug discovery and multi-objective optimization accelerating clinical translation.
• AI-powered neural decoding and closed-loop control for non-invasive brain–computer interfaces with applications in neurorehabilitation and assistive robotics.
• Large language models trained on biomedical corpora enabling explainable clinical reasoning and complex medical question answering.
• Vision–language foundation models for integrative analysis of multimodal biomedical data, including imaging, pathology, and electronic health records.
• AI applications in pathogen genomics for real-time outbreak surveillance, antimicrobial resistance prediction, and evolutionary dynamics.
• Multimodal deep learning frameworks combining genomics, imaging, and clinical data for early disease detection, prognosis, and patient stratification.
• Explainable AI techniques fostering clinical trust and facilitating regulatory approval of AI-driven medical devices and diagnostics.
• Federated and privacy-preserving learning methodologies for robust AI modeling on sensitive, distributed healthcare data.

Submission instructions: 

Please read the Guide for Authors before submitting. All articles should be submitted online and select [VSI-AI for Biomedical Technology].

Proposed deadline: Submission deadline 30 June 2026.

Guest editor: