Dr. Ohmini Krishnamurthy Rajendran

The Invisible Tumor: AI-Augmented Radiology for Ultra-Early Cancer Detection and Predictive Oncology

Abstract:

Cancer outcomes are profoundly influenced by the timing of detection, yet many malignancies remain radiologically occult during their earliest biological stages. This keynote explores how AI-augmented radiology is transforming oncology through ultra-early cancer detection, predictive imaging biomarkers, and multimodal data integration. By combining radiomics, deep learning, and longitudinal imaging analytics, next-generation diagnostic systems can identify subtle preclinical tumor signatures before conventional manifestation. The session highlights the emergence of predictive oncology frameworks that enable personalized risk stratification, earlier intervention, and precision-guided therapeutic decision-making, redefining radiology from a diagnostic specialty into a proactive engine of anticipatory cancer care.

Profile:

Dr. Ohmini Krishnamurthy Rajendran is a Consultant Radiologist–Clinical Researcher, whose work is centered on redefining the future of precision oncology through the convergence of radiology, artificial intelligence, and translational cancer research. Recognized for her forward-looking contributions in cognitive radiology and AI-driven oncologic innovation, she is part of a new generation of physician-researchers advancing data-centric, image-guided cancer care.
Her research focuses on radiomics, multimodal imaging analytics, foundation AI models, and intelligent diagnostic frameworks designed to enhance early cancer detection, tumor characterization, treatment response prediction, and precision therapeutic planning. By integrating advanced imaging biomarkers with real-world clinical and bioinformatic data, her work aims to accelerate the transition of oncology from conventional reactive medicine toward predictive, personalized, and computationally guided care pathways.
Dr. Ohmini has contributed to peer-reviewed scientific literature, multidisciplinary research initiatives, and international academic forums exploring the transformative role of artificial intelligence in oncology and radiology. Her academic interests extend to scalable AI-enabled imaging ecosystems, digital oncology platforms, and next-generation radiologic techniques capable of improving diagnostic accuracy, clinical efficiency, and patient-centered outcomes across diverse healthcare settings.
Through her expanding global academic presence and scientific contributions, She is increasingly recognized as a thought leader at the intersection of oncology, radiology, and artificial intelligence—helping shape the future of precision cancer medicine through innovation, interdisciplinary collaboration, and translational impact.