Healthcare Heroes 2025: Panelists Discuss Al and Cancer In Revolutionising Diagnosis and Treatment

AI in Early Detection: Saving Lives Faster

Dr Ashok Sharma, Additional Professor, Biochemistry, AIIMS, New Delhi, stressed AI’s potential for early cancer diagnosis. “Early detection saves 60% of patients from death,” he said. His team developed iOncology.ai, a prototype software for breast and ovarian cancer detection, achieving 89% accuracy by merging radiological, genomic, and biochemical data from 4–5 million images and 2,500 patients.

“AI needs diverse data like lab reports, MRI scans, and genetic profiles to work,” he explained. “But no single expert can build this alone. Clinicians, radiologists, and data scientists must collaborate.” Dr Sharma cautioned that developing such advanced systems requires multidisciplinary collaboration. "This isn't a one-person achievement. It takes radiologists, molecular biologists, computational experts, and clinicians working together. As we gather more diverse patient data, these systems will only become more precise in their diagnoses."

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AI in Radiology: A Second Revolution

Dr Vikram Pratap Singh, Director and Chief Radiologist, Tirupati Diagnostics, Gurgaon, called AI the ‘second revolution in imaging.’ The first was MRI/CT scans. Now, AI can reduce radiologists’ workloads by 60–70% by automating tasks like measuring tumour sizes or comparing past scans.

He highlighted India’s rural healthcare gaps: “For every 1 lakh patients, there’s 1 radiologist and worse in remote areas. AI can analyse scans in villages and send results to urban experts.” This could cut delays in cancer diagnosis and ease pressure on radiologists, who currently handle 100+ cases daily versus the ideal 34.

Generative AI: Bridging Language and Distance Gaps

Deepak Tuli, Co-Founder of Eka Care, Gurugram, argued AI can bridge India’s doctor-patient divide. “A metro doctor can’t understand a rural patient’s dialect. AI translates conversations in real-time, saving hours,” he said.

AI also tackles documentation: “Doctors spend 40% of their time on paperwork. AI voice-to-text tools let them focus on patients.” He cited Google’s DeepMind, which detects breast cancer better than radiologists, as inspiration for Indian AI models tailored to local needs.

Skills for Healthcare Workers: Trust, but Verify

Resham Sethi, Senior Program Officer, Digital Health, South Asia, PATH, emphasised training healthcare workers to use AI ethically. “They don’t need to code, but they must question AI’s suggestions,” she said. For instance, if AI flags a cancer risk, doctors should verify it with tests.

She urged ‘digital fluency’ for rural workers: “Teach them to collaborate with AI, not depend on it. Trust their experience first.”

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Ethical AI: Local Data for Local Needs

Sethi warned against using Western AI tools in India. “An AI trained on light-skinned patients might miss cancer in darker skin. Our tools need Indian data.” She pushed for inclusive design: “Involve rural communities when building AI. Test tools in real clinics, not labs.” India’s new Data Privacy Act helps, but an ethical AI framework is critical to reduce bias and protect privacy.

Conclusion

The integration of AI into oncology represents a transformative shift in healthcare, offering unprecedented potential to improve early detection and personalised treatment. While challenges around data quality, implementation, and ethical considerations remain, the technology's ability to enhance diagnostic accuracy and bridge healthcare gaps is undeniable. 

For patients, this progress promises more accessible, timely, and effective cancer care like turning what was once a race against time into a manageable journey toward better health outcomes. The future of oncology lies in this powerful synergy of human expertise and artificial intelligence, working together to save lives.