Emergence of Intelligent Photonic Technologies in Early Medical Diagnostics

The field of medical diagnostics is undergoing a paradigm shift driven by the integration of photonic technologies and artificial intelligence (AI). Advances in ultrafast optical imaging, engineered sensing platforms, and plasmonic‐enhanced detection modalities enable noninvasive, high‐resolution probing of biological tissues and biomarkers in real time. When coupled with machine learning (ML) algorithms, the integrated intelligent photonic systems can reveal subtle physiological and molecular changes, which transform patient care through faster, more accurate, and minimally invasive diagnostics. This perspective surveys recent advances in photonic modalities, examines AI‐driven processing frameworks, integration with ML and highlights key applications in oncology, neurology, and cardiovascular health. We discuss critical technical challenges, including device fabrication, algorithm interpretability, clinical translation, and outline future directions for realizing next‐generation diagnostic platforms.

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Publication Details

Journal
physica status solidi (a)
Published
2025-11-20
DOI
https://doi.org/10.1002/pssa.202500637
Primary Topic
Neural Networks and Reservoir Computing
Type
article
Field-Weighted Citation Impact
0.00
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article

Emergence of Intelligent Photonic Technologies in Early Medical Diagnostics

P. C. Agarwal, Anand Babu, Deepak Kumar
physica status solidi (a)
Neural Networks and Reservoir Computing
article

Emergence of Intelligent Photonic Technologies in Early Medical Diagnostics

P. C. Agarwal, Anand Babu, Deepak Kumar
article en

Abstract

The field of medical diagnostics is undergoing a paradigm shift driven by the integration of photonic technologies and artificial intelligence (AI). Advances in ultrafast optical imaging, engineered sensing platforms, and plasmonic‐enhanced detection modalities enable noninvasive, high‐resolution probing of biological tissues and biomarkers in real time. When coupled with machine learning (ML) algorithms, the integrated intelligent photonic systems can reveal subtle physiological and molecular changes, which transform patient care through faster, more accurate, and minimally invasive diagnostics. This perspective surveys recent advances in photonic modalities, examines AI‐driven processing frameworks, integration with ML and highlights key applications in oncology, neurology, and cardiovascular health. We discuss critical technical challenges, including device fabrication, algorithm interpretability, clinical translation, and outline future directions for realizing next‐generation diagnostic platforms.

physica status solidi (a)Vol. 222(24)
Agency for Science, Technology and Research (SG), University of Leeds (GB), Institute of Materials Research and Engineering (SG), UCLouvain (BE)
Openalex Percentile: Top 81%
Neural Networks and Reservoir Computing
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