Advancing cancer diagnostics with artificial intelligence
Teesside University researchers are using artificial intelligence (AI) to enhance cancer diagnostics, training deep learning models to improve accuracy and revolutionise treatment.
Challenge
Generative AI is reshaping industries, with healthcare among the sectors experiencing significant transformation. At Teesside University, researchers are pioneering AI applications in cancer diagnostics, working to improve the speed and accuracy of personalised treatments.
In a recent BBC One interview featured on BBC Look North and BBC Politics North, Professors Claudio Angione and Dr Annalisa Occhipinti discussed their research into AI-driven cancer treatment advancements. Their work aligns with a global shift towards AI-powered healthcare solutions, where enhanced diagnostics and treatment precision are improving patient outcomes and optimising healthcare efficiency.
Solution
Professors Angione and Occhipinti are developing AI-driven deep learning models to identify tumour regions with exceptional precision. These models process vast medical image datasets, integrating multimodal data to enhance diagnostic accuracy. By analysing thousands, potentially millions, of cases, the AI models could eventually outperform traditional diagnostic methods, providing earlier and more reliable detection of cancerous regions.
This research highlights AI’s expanding role in medical diagnostics, supporting healthcare professionals by reducing diagnostic errors and streamlining treatment planning. The integration of AI into these processes has the potential to transform patient care by enabling faster, data-driven decision-making.
Impact
The research at Teesside University demonstrates the transformative potential of AI in healthcare. Beyond improving diagnostic accuracy, AI-driven innovations are driving economic growth within the medical technology sector.
By incorporating multimodal AI, combining imaging data with patient history, genetic factors, and other critical health indicators, this work enhances decision-making for diagnosis and treatment planning. Ultimately, these advancements enable healthcare providers to deliver more precise, efficient, and effective care, improving patient outcomes and optimising healthcare resources.
