AI and digital technologies for cancer pathways: assessing promise; delivering impact

Closing date: 25/03/2024

MB-PhD Project: AI and digital technologies for cancer pathways: assessing promise; delivering impact

Lead Supervisors: Dr André Freitas
Dr Louise Carter; Dr Harriet Unsworth

Applications Deadline: Monday 25 March 2024

Project Keywords: Digital, AI, Clinical Trials
Research Opportunity: Intercalated PhD, leading to the award of PhD and MBChB

The project supervisor is offering a PhD in the below area during 2024/2025. Work during the Summer Placement might involve some of this work but could vary.

Project Outline

Digital and AI technologies have the potential to transform clinical trials: improving patient access to experimental treatments, generating novel and insightful data, and enabling the vision of personalised trials to improve patient outcomes. The dECMT has recently developed a portfolio of digital and AI tools to address existing challenges in cancer clinical trials. These tools have been developed through a large international collaboration that has allowed us insight into clinical work flows, to develop digital and AI solutions to real-world problems.

This project aims to evaluate the clinical and health system value of these tools, including assessments of their impacts on patients and healthcare professionals – we will be asking questions such as how well do these tools work? Who feels the benefit? What is the best way to measure these impacts, for a range of different digital and AI tools? This will enable an objective assessment of the value of each of the digital and AI tools.

In addition, we aim to build on the results of these individual evaluations, by designing a framework for the evaluation of digital and AI tools for use in clinical trials. Such a framework would enable clearer development cycles for digital and AI tools, by clearly outlining suitable evaluation pathways for a diverse range of such tools. Additionally, this framework would enable those planning clinical trials to better understand what kind of evaluation evidence can be used to identify tools that are likely to deliver real-world benefits to clinical trial processes.

About Dr Andre Freitas (project Lead Supervisor)

André Freitas leads the Reasoning & Explainable AI Lab at the Department of Computer Science at The University of Manchester and is a research group leader at the Idiap Research Institute. He is also the AI group leader at the digital Experimental Cancer Medicine Team (CancerResearchUK).

His main research interests are on enabling the development of AI methods to support abstract, explainable and flexible inference. In particular, he investigates how the combination of neural and symbolic data representation paradigms can deliver better inference. Some of his research topics include: explanation generation, natural language inference, explainable question answering, knowledge graphs and open information extraction. He is actively engaged in collaboration projects with industrial and clinical partners.

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Cancer Research UK Manchester Centre | AI and digital technologies for cancer pathways: assessing promise; delivering impact

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