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Dr. Indrajeet Das, a consultant radiologist at the University Hospitals of Leicester NHS Trust, recently shared his insights on the transformative role of artificial intelligence (AI) in radiology and patient care. As part of one of the UK’s largest acute healthcare providers, his work covers a wide range of areas, including respiratory and cardiac imaging, serving a population nearing one million across Leicester and Rutland. Leicester’s NHS Trust also functions as a key referral and research center, especially in cancer and cardiovascular services, which puts Dr. Das at the forefront of integrating new technologies like AI in clinical practice.
Dr. Das discussed how AI is shaping a new era in radiology, especially with tools like Qure.ai’s qXR, an AI application designed to interpret chest X-rays for various conditions, including lung cancer. He described AI’s progression as “an exciting revolution,” and while the technology is still in its early stages in Leicester, there is great promise. Dr. Das emphasized the importance of using AI in clinical trials that offer real-world evidence, explaining how collaboration with technology partners like Qure.ai helps refine AI’s effectiveness in clinical settings.
One of the critical initiatives discussed was the National Lung Cancer Optimal Pathway, which aims to significantly reduce the time between diagnosis and treatment of lung cancer. Dr. Das pointed out that early detection can often make the difference in outcomes, particularly in a disease with notoriously late-stage symptoms. The pathway initiative seeks to cut diagnosis times from an average of 62 days to under 49 days. To achieve this, the pathway recommends that chest X-rays suspected of showing lung cancer be reviewed within 24 hours, followed by a CT scan within 72 hours. Dr. Das suggested that implementing AI in radiology could help meet these tight deadlines, accelerating the diagnostic process and improving outcomes.
Reflecting on his early experience with qXR, Dr. Das mentioned a retrospective study of around 1,000 chest X-rays, where qXR demonstrated high specificity and sensitivity. The AI tool quickly flagged eight out of ten confirmed lung cancer cases, pinpointing their location accurately and performing faster than traditional reporting methods. He noted that had qXR been used in real-time, it could have expedited reporting and potentially led to quicker intervention.
Dr. Das is optimistic about the future of AI in the NHS, not only for cancer detection but also for conditions like tuberculosis, pneumothorax, and trauma injuries. He believes that AI can play a critical role in flagging urgent cases, helping radiologists prioritize cases when resources are stretched. This prioritization could improve patient outcomes by ensuring those in most critical need are attended to sooner.
For colleagues hesitant about AI, Dr. Das reassured that AI isn’t intended to replace radiologists but rather to support them, much like a car’s lane-keeping feature prevents accidents without taking over the driver’s role. He sees AI as a safety net that enhances diagnostic accuracy and efficiency, supporting radiologists by catching potential issues and allowing them to focus more effectively on complex cases. He is hopeful that with adequate governance, data protection, and research, AI could evolve into a new standard of care, offering a transformative tool for the healthcare system.
