Raj Jena, a prominent UK radiologist at Addenbrooke’s Hospital, has gained recognition for his pioneering work in artificial intelligence (AI) within the field of radiology. As Cambridge’s first Professor of AI in Radiology, Jena developed OSAIRIS, an AI tool designed to streamline radiotherapy scan preparation. The tool automates routine tasks, saving critical time for doctors and enabling faster patient treatment.
AI is poised to revolutionize the medical and healthcare sectors. It aims to accelerate biomedical discovery by improving our understanding of cellular functions, disease origins, and potential drug targets. Furthermore, AI could unlock vast datasets—such as genomics—to predict disease risks, detect early-stage diseases, and create targeted treatments.
Optimizing clinical trials is another area where AI can play a significant role. AI tools can help select suitable participants and analyze outcomes in real-time, potentially transforming treatment regimens and healthcare systems to offer personalized therapies that match individuals' specific needs.
Implementing AI in healthcare, however, requires careful navigation. Accessing the necessary data to train AI models presents challenges, particularly due to the volume, variety, and velocity of data. A coordinated effort and substantial investment from the UK government are needed to enhance researchers' access to well-curated datasets. The UK Biobank exemplifies the foresight and resources needed to propel such innovation.
Protecting sensitive clinical data is critical, necessitating secure environments like the Electronic Patient Record Research and Innovation (ERIN) at Cambridge University Hospitals NHS Foundation Trust. Developing a UK-wide database could streamline data accessibility across healthcare providers without requiring individual NHS trust permissions, maximizing the NHS's potential as a unified healthcare system.
It's vital to ensure AI tools do not perpetuate existing health disparities. AI models must be trained on diverse datasets; otherwise, their effectiveness across different populations—such as those at higher risks for specific diseases—could be compromised.
AI technology must translate effectively from laboratories to practical use in the NHS. Collaboration between developers, clinicians, and healthcare professionals from the outset can prevent AI solutions from failing in real-world applications. Public confidence is essential in AI tools, requiring professional training to evaluate algorithms accurately.
Jena's collaboration with Microsoft Research in developing OSAIRIS exemplifies the importance of including healthcare providers in the development process. His dual expertise as a radiologist and NHS insider ensured the tool was designed with practical usability in mind.
The Centre for AI in Medicine at Cambridge, established in 2020 with industry support from AstraZeneca, GSK, and Boehringer Ingelheim, seeks to advance AI and machine learning in biomedical science and healthcare. Despite differences in disciplinary approaches, Cambridge's strategic position as a hub of AI and medical expertise enables it to lead innovative developments in the field.
The center's goal is to enable AI's transformative potential in health and medicine. Its success could bring significant public benefits, driving innovations that are both necessary and essential for the future of healthcare.