{"title":"Machine learning and big data in precision medicine: what is the role of the Radiologist?","authors":"Giovanni MORANA","doi":"10.23736/s2723-9284.23.00252-0","DOIUrl":null,"url":null,"abstract":"With the advent of artificial intelligence (AI) in the field of radiology, a new perspective opens up in terms of diagnosis and management of patients. There is a need to review the way radiologists work so as to rebuild the doctor-patient relationship that has been sidelined over the years to increase our productivity. It is precisely the improvement in productivity that will be made possible by AI that will be able to free the radiology physician from time-consuming activities that add little to the diagnostic value of our work; this “gift of time” will have to be used to have a direct relationship with the patient, who can be followed up directly by the radiology physician, and not just sent by other physicians. This will be all the more necessary since with the new methods of image analysis (deep learning, texture analysis) the radiologist physician will not only have the task of diagnosing a lesion as accurately as possible, but also of indicating its evolution and progression, what makes indispensable a new pact with the patient, who will have to not only “accept” the diagnosis of an existing lesion but, above all, will have to trust the prognosis of that lesion, a trust based on an immaterial datum (the advanced image analysis) but which weighs like a boulder on the psyche of the patient. Only a relationship of great trust with his new physician, the radiologist, can make him follow our directions.","PeriodicalId":369070,"journal":{"name":"Journal of Radiological Review","volume":"180 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Radiological Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23736/s2723-9284.23.00252-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
With the advent of artificial intelligence (AI) in the field of radiology, a new perspective opens up in terms of diagnosis and management of patients. There is a need to review the way radiologists work so as to rebuild the doctor-patient relationship that has been sidelined over the years to increase our productivity. It is precisely the improvement in productivity that will be made possible by AI that will be able to free the radiology physician from time-consuming activities that add little to the diagnostic value of our work; this “gift of time” will have to be used to have a direct relationship with the patient, who can be followed up directly by the radiology physician, and not just sent by other physicians. This will be all the more necessary since with the new methods of image analysis (deep learning, texture analysis) the radiologist physician will not only have the task of diagnosing a lesion as accurately as possible, but also of indicating its evolution and progression, what makes indispensable a new pact with the patient, who will have to not only “accept” the diagnosis of an existing lesion but, above all, will have to trust the prognosis of that lesion, a trust based on an immaterial datum (the advanced image analysis) but which weighs like a boulder on the psyche of the patient. Only a relationship of great trust with his new physician, the radiologist, can make him follow our directions.