INDICATE: AI-enabled data curation, quality and fact-checking for medical documents

Clinicians, patients and policy makers lack access to accurate, real time information on new treatments for treating cancer. This is because such a large amount of information is continuously generated, and it is too complicated to be manually analysed in a timely fashion. This is sometimes referred to as a health 'infodemic'. Information analysed to create clinical evidence (known as systematic reviews) quickly goes out of date, and national bodies responsible for appraising new treatments such as the National Institute for Clinical Excellence are unable to keep up. It is increasingly hard to detect misinformation published within medical literature, and an increasing number of papers have to be withdrawn after publication. INDICATE is a deep learning tool for the autonomous generation of systematic reports and analysis of both structured and unstructured data from published literature on cancer. It has been developed through a collaboration between Imperial College London and Amazon Web Services, NICE and the British Medical Journal (BMJ). The aim is to develop a methodology for the real time analysis of healthcare infodemics that can be used to autonomously create clinical guidance and identify misinformation. This project will build on previous work to develop AI methodologies that automate how we search for medical literature and it will intelligently support peer reviewers as they appraise and assess the quality of research papers. This work has three main goals: 1. To develop a tool for detecting research fraud. 2. To asses if our AI tools can speed up the creation of NICE guidance. 3. To develop autonomous summary reports of clinical evidence of breast cancer treatment that could be used by medical publishers. The study group will work with clinicians, researchers and NICE to define and prioritise critical questions that require answering and to refine the user interface for the system. Moreover, we will prospectively validate the performance of the system to determine the accuracy and performance of its reporting mechanism. The validated data generated by this study will form the basis of a phase II study that scales the number of cancer types and the trial of the technology in a real world clinical environment.