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The integration of AI-based decision tools into routine clinical care is opening the door to a completely new paradigm where doctors and machines can collaborate to decide a right diagnose or treatment for a patient, based on individual patient's biomedical information. A number of important ethical challenges rise from the development of the AI tools to their implementation.
In this talk, Raquel will introduce Fair modelling, a qualitative framework that aims to serve as an interrogation for an ethical integration of AI decision systems in healthcare. During her talk, the role that clinicians, developers and patients have in ensuring an ethical development and deployment of AI models will be discussed. Several ethical challenges will be identified and connected with the four ethical principles of the medical profession —Respect for autonomy, Beneficence, Non-Maleficence and Justice.
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This event is part of the King's Festival of Artificial Intelligence, running from Wednesday 24 to Sunday 28 May 2023, which brings together speakers, exhibits, performances, demos and screenings in an exciting programme of events. Take a look at the other events here.
Dr Raquel Iniesta is a Senior Lecturer in Statistical-Machine Learning for personalised medicine at the Biostatistics and Health Informatics Department, King's College London. Her research interests have covered the development of Machine Learning and Topological Data Analysis models to allow for precision medicine using clinical and genetic information, with main works on treatment personalisation for Depression and Hypertension. After years of experience as a researcher and lecturer in data sciences, Raquel realised about the need of emphasising the key role that human agents —clinicians, developers, patients— have towards enabling an ethical development, implementation and use of AI-based models in healthcare. Her most recent work is seeking to identify the main ethical underpinnings of integrating Machine Learning models for personalised medicine.
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Senior Lecturer in Statistical Learning for Precision Medicine
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