Enhancing Medical Diagnostics with AI-driven Raman Spectroscopy

The synergy of optical technologies and artificial intelligence (AI) offers a promising path for solving complex medical challenges. Photonic techniques enable faster and more precise diagnoses, while AI-driven analytics improve data interpretation and support the implementation of personalized treatments. Despite these advancements, challenges remain, particularly in integrating these technologies into clinical practice and addressing technical limitations. However, the potential for photonics and AI to revolutionize healthcare and significantly improve patient outcomes is substantial.
In this presentation, we will discuss our recent efforts to bring optical techniques and advanced AI-based data analysis into clinical settings. The focus will be on molecular-sensitive spectroscopic methods, particularly Raman spectroscopy, and its combination with other spectroscopic and optical approaches for a multimodal strategy. We will highlight two key medical applications: (i) real-time intraoperative histopathological diagnosis of tumors, and (ii) the on-site diagnosis and treatment of infectious diseases. Time is a critical factor in both cases; the faster an infectious agent is identified or a tumor is completely excised, the better the patient's chances of a positive outcome.
Our approach covers the entire diagnostic process, from sample collection to final diagnosis, with the potential to significantly reduce time to treatment. To accelerate the transition into clinical practice, we are developing a translational infrastructure aimed at bridging the gap between research and product development, ensuring these innovations benefit patients as quickly as possible.
Acknowledgements
Financial support of the EU, the ”Thüringer Ministerium für Wirtschaft, Wissenschaft und Digitale Gesellschaft”, the ”Thüringer Aufbaubank”, the Federal Ministry of Education and Research, Germany, the German Science Foundation, and the Carl-Zeiss Foundation are greatly acknowledged