From Pixels to Precision: AI's Emerging Role in Personalized Cancer Treatment
New Delhi: Artificial intelligence (AI) is steadily emerging as a vital co-pilot in the fight against cancer, offering new possibilities in diagnosis, treatment planning, and clinical efficiency. In an exclusive interaction with journalist Rashmi Mabiyan Kaur,
Dr. Nikhil S Ghadyalpatil
, Director of Medical Oncology at
Apollo Cancer Centre
, Hyderabad, shared how AI is being seamlessly integrated into their clinical workflow—not to replace doctors, but to enhance their decision-making capabilities and streamline the delivery of
personalized cancer care
.
As per Dr Ghadyalpatil, the AI system acts as a digital assistant that scans new patient data, flags urgent findings, and suggests evidence-based treatments. It's currently being integrated across imaging, pathology, and electronic medical records (EMRs), with a particular focus on rare cancers and complex genomic profiles.
'The real value of AI lies in shrinking the time between data generation and clinical decision-making,' he noted. 'Often, treatment options go beyond textbook guidelines, and AI helps ensure no actionable mutation or imaging clue is missed.'
AI applications are already showing promise. In radiology, genomics, and pathology, the team is exploring ways to use AI to correlate MRI scans with genomic data or pathology slides to determine genetic mutations more rapidly and affordably. AI is also being tested to reduce human error in oncology pharmacology prescriptions—errors that, while rare, can have serious consequences.
Building this AI ecosystem has not been without hurdles. Standardizing data across disparate formats—particularly handwritten clinical notes and non-standard pathology images—has been time-consuming. Another challenge? Earning clinician trust. 'AI success is less about algorithms and more about clinician trust,' Dr. Ghadyalpatil explained. 'If the tools are transparent and support clinical judgment, they will be embraced.'
The hospital's strategy emphasizes smart scaling: start narrow, validate rigorously, and adapt to local needs. 'Don't blindly adopt Western models,' he cautioned. 'India is different. AI tools must be trained and validated on Indian data to offer meaningful insights.'
Their current focus areas include AI-driven radiomics in early-stage lung cancer, predictive models combining PET/CT scans with biomarkers in breast and lung cancer, and structured dashboards that summarize clinical notes to detect early warning signs of complications like neutropenia or sepsis.
He also stressed the importance of addressing systemic challenges: data interoperability, clinician training, lack of local validation, and limited infrastructure in smaller cities. 'Less than 20% of Indian cancer centers have structured digital archives,' he noted. 'That has to change.'
Looking forward, Dr. Ghadyalpatil sees immense potential for AI in early cancer detection and rural screening—through tools like smartphone cytology and low-cost thermal imaging. 'If we build the right digital infrastructure, train our teams, and choose our pilots wisely,' he said, 'AI can become a silent, steady partner in delivering truly personalized cancer care—across all corners of India.'
He envisions India developing its own treatment insights through privacy-preserving AI trained on data from hundreds of hospitals.
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