Guy S Handelman1,2, Hong Kuan Kok3,4, Ronil V Chandra5,6, Amir H Razavi7,8, Shiwei Huang9, Mark Brooks5,10, Michael J Lee2,11, Hamed Asadi5,10,12. 1. 1 Department of Radiology, Belfast City Hospital, 51 Lisburn Rd, Belfast, Antrim BT9 7AB, UK. 2. 2 Royal College of Surgeons in Ireland, Dublin, Ireland. 3. 3 Interventional Radiology Service, Northern Hospital Radiology, Epping, Australia. 4. 4 School of Medicine, Faculty of Health, Deakin University, Waurn Ponds, Australia. 5. 5 Interventional Neuroradiology Service, Monash Imaging, Monash Health, Clayton, Australia. 6. 6 Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Australia. 7. 7 School of Information Technology and Engineering, University of Ottawa, Ottawa, ON, Canada. 8. 8 BCE Corporate Security, Ottawa, ON, Canada. 9. 9 The Australian National University Medical School, Garran, Australia. 10. 10 Department of Radiology, Interventional Neuroradiology Service, Austin Health, Heidelberg, Australia. 11. 11 Department of Radiology, Beaumont Hospital, Dublin, Ireland. 12. 12 The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Australia.
Abstract
OBJECTIVE: Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have increased almost exponentially, and ML has become a hot topic at academic and industry conferences. However, despite the increased awareness of ML as a tool, many medical professionals have a poor understanding of how ML works and how to critically appraise studies and tools that are presented to us. Thus, we present a brief overview of ML, explain the metrics used in ML and how to interpret them, and explain some of the technical jargon associated with the field so that readers with a medical background and basic knowledge of statistics can feel more comfortable when examining ML applications. CONCLUSION: Attention to sample size, overfitting, underfitting, cross validation, as well as a broad knowledge of the metrics of machine learning, can help those with little or no technical knowledge begin to assess machine learning studies. However, transparency in methods and sharing of algorithms is vital to allow clinicians to assess these tools themselves.
OBJECTIVE: Machine learning (ML) and artificial intelligence (AI) are rapidly becoming the most talked about and controversial topics in radiology and medicine. Over the past few years, the numbers of ML- or AI-focused studies in the literature have increased almost exponentially, and ML has become a hot topic at academic and industry conferences. However, despite the increased awareness of ML as a tool, many medical professionals have a poor understanding of how ML works and how to critically appraise studies and tools that are presented to us. Thus, we present a brief overview of ML, explain the metrics used in ML and how to interpret them, and explain some of the technical jargon associated with the field so that readers with a medical background and basic knowledge of statistics can feel more comfortable when examining ML applications. CONCLUSION: Attention to sample size, overfitting, underfitting, cross validation, as well as a broad knowledge of the metrics of machine learning, can help those with little or no technical knowledge begin to assess machine learning studies. However, transparency in methods and sharing of algorithms is vital to allow clinicians to assess these tools themselves.
Authors: Andrew E Grothen; Bethany Tennant; Catherine Wang; Andrea Torres; Bonny Bloodgood Sheppard; Glenn Abastillas; Marina Matatova; Jeremy L Warner; Donna R Rivera Journal: JCO Clin Cancer Inform Date: 2020-11