Journal article
Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review
Owain T Jones, Natalia Calanzani, Smiji Saji, Stephen W Duffy, Jon Emery, Willie Hamilton, Hardeep Singh, Niek J de Wit, Fiona M Walter
JOURNAL OF MEDICAL INTERNET RESEARCH | JMIR PUBLICATIONS, INC | Published : 2021
DOI: 10.2196/23483
Abstract
BACKGROUND: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care. OBJECTIVE: This study aimed to systematically review AI techniques that may facilitate earlier diagno..
View full abstractGrants
Awarded by National Institute for Health Research (NIHR) Policy Research Programme
Awarded by CanTest Collaborative (Cancer Research UK)
Awarded by Houston Veterans Administration Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety
Awarded by Agency for Healthcare Research and Quality
Funding Acknowledgements
This research was funded by the National Institute for Health Research (NIHR) Policy Research Programme, conducted through the Policy Research Unit in Cancer Awareness, Screening, and Early Diagnosis, PR-PRU-1217-21601. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. This work was also supported by the CanTest Collaborative (funded by Cancer Research UK C8640/A23385), of which FW and WH are directors and JE, HS, and NdW are associate directors. HS is additionally supported by the Houston Veterans Administration Health Services Research and Development Center for Innovations in Quality, Effectiveness, and Safety (CIN13-413) and the Agency for Healthcare Research and Quality (R01HS27363). The funding sources had no role in the study design, data collection, data analysis, data interpretation, writing of the report, or the decision to submit for publication. The authors would like to thank Isla Kuhn, Reader Services Librarian, University of Cambridge Medical Library, for her help in developing the search strategy.