Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care |
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| Affiliation: | 1. Computational Oncology Group, Institute for Global Health Innovation, Imperial College London, London, UK;2. Department of Radiotherapy, Charing Cross Hospital, Imperial College NHS Trust, London, UK;1. The Christie NHS Foundation Trust, Manchester, UK;2. University Hospitals Sussex NHS Foundation Trust, West Sussex, UK;1. Department of Oncology and Dermatological Oncology, IDI-IRCCS, Rome, Italy;2. Epidemiology Unit, IDI-IRCCS, Rome, Italy;3. Laboratory of Experimental Immunology, IDI-IRCCS, Rome, Italy;4. Department of Clinical and Molecular Medicine, Oncology Unit, Sant''Andrea Hospital, Sapienza University, Rome, Italy;1. Department of Radiation Oncology, Ewha Womans University College of Medicine, Seoul, South Korea;2. Department of Radiation Oncology, Soonchunhyang University College of Medicine, Seoul, South Korea;3. Department of Radiation Oncology, Hanyang University College of Medicine, Seoul, South Korea;4. Department of Radiation Oncology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea;1. Dana-Farber Cancer Institute, Boston, MA, USA;2. Brigham and Women''s Hospital, Boston, MA, USA;3. Computational Health Informatics Program, Boston Children''s Hospital, Boston, MA, USA;4. MD Anderson Cancer Center, Houston, Texas, USA;5. Vanderbilt University Medical Center, Nashville, Tennessee, USA;1. Swansea Bay University Health Board, South West Wales Cancer Centre, Swansea, UK;2. The University of Manchester, Manchester, UK;3. Swansea University Medical School, Swansea, UK |
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| Abstract: | Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches. |
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| Keywords: | Artificial intelligence Big Data database deep learning registries repository |
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