AI and Machine Learning in Medical Applications

Dear Colleagues,

Artificial intelligence and machine learning are no longer peripheral tools in biomedical research—they have become central engines of discovery across the full spectrum of medical science. The past decade has witnessed a fundamental transformation: algorithmic approaches that once required years of manual expert curation can now identify molecular disease signatures in hours, predict patient outcomes from routine clinical data, and propose drug candidates with mechanistic plausibility validated in preclinical models.

This Special Issue, “AI and Machine Learning in Medical Applications”, is dedicated to publishing rigorous, high-impact research that captures the breadth and depth of this transformation. The scope encompasses, but is not limited to the following: deep learning for medical image analysis and computer-aided diagnosis; transformer-based and large language models applied to clinical text, electronic health records, and biomedical literature; machine learning frameworks for multi-omics data integration, including genomics, transcriptomics, and proteomics; AI-powered drug repurposing and target identification pipelines; and intelligent monitoring systems leveraging wearable and sensor data for continuous patient assessment.

A defining challenge in medical AI is the gap between computational performance and clinical utility. High AUC scores on benchmark datasets do not automatically translate into tools that improve patient outcomes, respect biological complexity, or generalize across populations with diverse genetic backgrounds and healthcare contexts. This Special Issue, therefore, particularly encourages submissions that address model interpretability and explainability, fairness and equity across patient subgroups, rigorous external validation, and the integration of domain knowledge—biological, clinical, and epidemiological—into model design and evaluation.
We also welcome work that reflects the increasingly multimodal nature of modern medicine: studies that fuse imaging, molecular, clinical, and behavioral data streams to build richer, more predictive models of human health and disease.

This collection is intended to serve as a reference point for the state of the field—documenting both methodological advances and the open problems that define the frontier of medical AI research. We invite contributions from computational biologists, clinical data scientists, biomedical engineers, and clinician–researchers whose work exemplifies the responsible, rigorous, and impactful application of artificial intelligence to human health.

Dr. Wen Zhang
Guest Editor

Manuscript Submission Information

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