Harnessing Machine Learning for Enhanced Biomedical Diagnosis and Early Disease Detection: Bridging Data Science and Healthcare

Advancements in biomedical technologies have significantly facilitated the diagnosis and monitoring of diseases. Nonetheless, traditional diagnostic methods can be labor-intensive, require expert interpretation of results, and may be time-consuming. In recent years, machine learning (ML) has emerged as a transformative technology in the field of biomedicine. Machine learning models can discern trends in data that may be difficult for humans to detect, including those found in medical imaging, genetic sequences, and physiological readings. These novel data technologies are demonstrating efficacy in enhancing illness diagnosis, formulating personalized treatment alternatives, and facilitating the early identification of cancer, cardiovascular diseases, and neurological disorders.

This Research Topic seeks to analyze the role of machine learning in biomedical assessment and identification tasks. The aim is to investigate methods by which machine learning algorithms, encompassing traditional classifiers and deep learning models, can enhance the speed, accuracy, and scalability of diagnostic procedures. This topic requests authors to address novel model structure designs, innovative feature selection techniques, the development of explainable models, and their adaptation for clinical utility. Emphasis is placed on integrating knowledge from medicine, bioinformatics, and computer science to address challenges. The utilization of extensive data sets, alongside novel methodologies from artificial intelligence and learning technologies, results in enhanced and more dependable diagnostic tools. The goal is to ascertain the application and evaluation of these methodologies in real-world healthcare contexts.

This Research Topic invites contributions that focus on the development, validation, and application of machine learning methods for biomedical diagnosis. We welcome original research articles, reviews, and case studies that address the following themes:

• Machine learning for medical image analysis
• Predictive models for disease diagnosis
• Deep learning for signal and genomic data
• Explainable AI in clinical decision-making
• Federated and privacy-preserving learning in healthcare
• Model generalization and validation across populations
• Integration of ML models into diagnostic workflows

Please note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases that are not supplemented by relevant functional validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic.