Carly E Waldman, Melody Hermel, Jonathan A Hermel, Francis Allinson, Mark N Pintea, Natalie Bransky, Emem Udoh, Laura Nicholson, Austin Robinson, Jorge Gonzalez, Christopher Suhar, Keshav Nayak, George Wesbey, Sanjeev P Bhavnani
{"title":"Artificial intelligence in healthcare: a primer for medical education in radiomics.","authors":"Carly E Waldman, Melody Hermel, Jonathan A Hermel, Francis Allinson, Mark N Pintea, Natalie Bransky, Emem Udoh, Laura Nicholson, Austin Robinson, Jorge Gonzalez, Christopher Suhar, Keshav Nayak, George Wesbey, Sanjeev P Bhavnani","doi":"10.2217/pme-2022-0014","DOIUrl":null,"url":null,"abstract":"<p><p>The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.</p>","PeriodicalId":19753,"journal":{"name":"Personalized medicine","volume":"19 5","pages":"445-456"},"PeriodicalIF":1.7000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Personalized medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2217/pme-2022-0014","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/7/26 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"PHARMACOLOGY & PHARMACY","Score":null,"Total":0}
引用次数: 7
Abstract
The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.
期刊介绍：
Personalized Medicine (ISSN 1741-0541) translates recent genomic, genetic and proteomic advances into the clinical context. The journal provides an integrated forum for all players involved - academic and clinical researchers, pharmaceutical companies, regulatory authorities, healthcare management organizations, patient organizations and others in the healthcare community. Personalized Medicine assists these parties to shape thefuture of medicine by providing a platform for expert commentary and analysis.
The journal addresses scientific, commercial and policy issues in the field of precision medicine and includes news and views, current awareness regarding new biomarkers, concise commentary and analysis, reports from the conference circuit and full review articles.
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