University of Queensland researchers have supported a study to improve the performance of an artificial intelligence (AI) model in skin cancer diagnosis and management.
The group, comprised of researchers from around the world, demonstrated that by incorporating expert knowledge in the form of “reward tables”, they could significantly improve the AI’s accuracy and reduce overconfidence in its predictions.
Professor Peter Soyer, Director of the Dermatology Research Centre (DRC) at UQ’s Frazer Institute, was one of 10 expert dermatologists who worked on the project by providing reward tables and thresholds for different clinical scenarios.
Professor Soyer said reward tables acted as a reinforcement learning tool, embedding both patients’ and clinicians’ preferences into the AI-system.
“Through reinforcement learning, an algorithm then receives feedback on whether an action was correct, neutral or incorrect and then decides from these outcomes which action to take next,” Professor Soyer said.
“Medical scenarios are often complex but, when clinicians can rely on AI to provide more realistic and nuanced suggestions, they can make better care management decisions.
“Our work mainly focused on skin cancer diagnosis, but the basic ideas could be used in many other areas of medical decision making for better patient outcomes.”
UQ Faculty of Medicine’s Professor Cliff Rosendahl provided dermatoscopy images for the dataset which was used to train the AI and test the machines and humans involved in the studies.
He said the incorporation of patient preferences could lead to greater acceptance of AI in medical practice.
“As healthcare shifts towards a more patient-centred approach, the creation of reward tables should involve both doctors and patients,” Professor Rosendahl said.
“This collaborative approach allows for more personalised care. In addition, the transparency offered by reward tables helps make AI decisions more understandable, which is key to gaining trust in these new systems.”
The research was published in Nature Medicine on July 27.
