Blog Post

Artificial intelligence (AI) is no longer confined to futuristic headlines or speculative research papers—it is becoming an indispensable tool in the fight against cancer. Around the world, oncologists are beginning to rely on AI systems that enhance accuracy, speed up treatment planning, and open doors to more personalized care. One striking example comes from Dr. Raj Jena, a clinical professor of AI in radiation oncology, whose pioneering work demonstrates that AI has firmly “moved out of the hype phase” and into everyday clinical practice. His experience highlights not only the power of AI to transform cancer treatment, but also its broader potential to strengthen health systems, especially in regions where resources are limited.

From Hype to Healing: Dr. Raj Jena and the Osairis Breakthrough

Dr. Jena co-developed Osairis, an AI-powered tool in collaboration with Microsoft, to streamline radiotherapy treatment planning. Previously, oncologists spent up to two hours planning radiation beams to avoid damage to healthy tissues—often finishing the work at home. Now, Osairis completes these calculations in just minutes, enabling faster treatment especially for prostate and head and neck cancers, significantly improving efficiency and patient outcomes. This shift exemplifies how AI is transitioning from concept to clinic. [Financial Times]

Despite these gains, AI isn’t a full fix for healthcare strain. Only 36 % of patients in England begin radiotherapy within the national 62-day target, far below the 85 % goal. AI tools like Osairis help address bottlenecks, but clinician workforce shortages and growing demand persist.

To bridge innovation and clinical practice, Dr. Jena and colleagues are developing Apollo, a platform funded by Cancer Research UK. Apollo enables researchers to submit emerging AI models for early assessment by hospital clinicians—facilitating safe, efficient validation and refinement of tools before deployment.

Broader AI Advances: From Drug Discovery to Diagnosis

AI’s impact in oncology spans far beyond radiotherapy planning:

• Drug discovery and personalized treatment: AI models like DeepMind’s AlphaFold drastically accelerate protein structure prediction, enabling faster identification of therapeutic targets. Companies such as Exscientia and Evotec have developed oncology candidates in record time—some reaching phase 1 trials in only eight months, compared to the traditional 4–5 years.
  
  
• Precision medicine & biomarkers: AI assists in discovering new biomarkers to improve screening, diagnosis, and prognosis prediction, tailoring treatment plans to each patient’s molecular profile.
  
  
• Predicting immunotherapy response: The AI tool SCORPIO uses routine blood tests and medical records to predict tumor response to immune checkpoint inhibitors, displaying 72–76 % accuracy—surpassing existing FDA-approved tests.
  
  
• Early detection & improved outcomes:
  
  
  • A patient’s life was saved thanks to AI-based analysis of CT scans, which flagged a lesion with a 64 % cancer risk—leading to timely surgery and recovery.
    
    
  • iNav, an AI system developed by Northwell Health, identifies early pancreatic cancer on MRIs and CT scans—even those taken for unrelated reasons—cutting diagnosis-to-treatment time by 50 %.
    
    
• High-accuracy diagnostics: Tools like Derm—an AI-powered app used across NHS hospitals—scan skin lesions with 99.9 % accuracy to rule out melanoma, reducing waiting lists and streamlining care.
  
  

Why This Matters for Healthcare in the Global South

• Scarcity of trained specialists: AI tools such as Osairis or diagnostic assistants can help compensate for workforce shortages by speeding up workflows and improving access to quality care.
  
  
• Resource constraints: Faster diagnosis and treatment mean fewer hospital visits, less strain on infrastructure, and more prompt intervention—critical for under-resourced systems.
  
  
• Tailored solutions: Platforms like Apollo that facilitate clinician-researcher collaboration can spur context-appropriate AI tools—especially valuable where local disease patterns and data are unique.

Conclusion

Dr. Raj Jena’s work with Osairis illustrates how artificial intelligence has moved beyond theoretical promise and is now delivering measurable impact in cancer care. By accelerating treatment planning, supporting clinicians, and enabling new models of collaboration through platforms like Apollo, AI is proving to be a vital partner in oncology. Beyond radiotherapy, its influence is visible in drug discovery, diagnostics, early detection, and immunotherapy prediction—areas where speed and precision can make the difference between life and loss.

The future of cancer care will not be defined by technology alone, but by how effectively we integrate AI into existing health systems, invest in safe and validated tools, and ensure that innovation translates into better outcomes for patients. With continued collaboration between researchers, clinicians, and technology leaders, AI has the potential to transform cancer treatment into a faster, more accurate, and more personalized journey of care.