A New Era in Diagnosis: From Biomarkers to Artificial Intelligence

Dear Colleagues,

Exploring new diagnostic approaches utilizing biomarkers and artificial intelligence (AI) has become increasingly significant in recent years. In fact, technologies such as AI and machine learning possess potential power to change the diagnosis of cancer treatment and identify predictive biomarkers.

The scope of the Special Issue includes develo** multimodal machine learning, a subfield of machine learning that works on the development and training of models to leverage the potential of various data sources—genomic, proteomic, imaging data—to improve their predictability performance. It has the advantage of being able to integrate different data modalities. For example, imaging data can be converted into data in sound form to make some of the earlier systems differentiate between malignant and benign lesions more effectively. This integration of multimodal clinical data into a cohesive AI model represents a significant step toward making more holistic representations of clinical data.

In addition to AI's role in integrating multimodal data, there is a fundamental shift toward using quantitative digital data in clinical research, leveraging the massive amounts of biomarker data generated worldwide. AI, particularly machine learning and high-performance computing, has been identified as uniquely capable of combining vast datasets from genomics, proteomics and other 'omics' technologies. This shift enables the establishment of novel therapies and predictive models of drug response, moving beyond the concept of a single biomarker to utilizing combinations of biomarkers for enhanced diagnostic accuracy and treatment decisions.

In addition, most biomarker discovery for diseases has relied on using methodologies in AI toward obtaining predictive biomarkers or scores to accelerate the development of diagnosis and treatment. Using supervised and non-supervised machine learning algorithms while analyzing vast datasets without any bias is evident in the identification of novel biomarker candidates. Medicines for different illnesses have an unmet need for biomarker discovery, highlighting that AI will have a high impact in predictive diagnostics and therapeutic strategies.

These breakthroughs reinforce the critical role of AI and machine learning in enhancing understanding and capacity toward the proper diagnosis and treatment of diseases. These are necessary in an era of precision medicine, in which data-driven insights steer health solutions into more accurate and effective directions.

Prof. Dr. Tudor Drugan
Dr. Daniel Leucuta
Guest Editors

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• diagnosis
• precision medicine
• predictive diagnostics
• therapeutic strategies
• biomarkers
• artificial intelligence
• machine learning