{"title":"Exploring the capabilities and limitations of large language models in nuclear medicine knowledge with primary focus on GPT-3.5, GPT-4 and Google Bard","authors":"Sira Vachatimanont, K. Kingpetch","doi":"10.21037/jmai-23-180","DOIUrl":"https://doi.org/10.21037/jmai-23-180","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Alskaf, R. Crawley, C. Scannell, Avan Suinesiaputra, Alistair Young, Pier-Giorgio Masci, D. Perera, A. Chiribiri
{"title":"Hybrid artificial intelligence outcome prediction using features extraction from stress perfusion cardiac magnetic resonance images and electronic health records","authors":"E. Alskaf, R. Crawley, C. Scannell, Avan Suinesiaputra, Alistair Young, Pier-Giorgio Masci, D. Perera, A. Chiribiri","doi":"10.21037/jmai-24-1","DOIUrl":"https://doi.org/10.21037/jmai-24-1","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140400144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Efficient glioma grade prediction using learned features extracted from convolutional neural networks","authors":"Shyam Yathirajam, Sreedevi Gutta","doi":"10.21037/jmai-23-161","DOIUrl":"https://doi.org/10.21037/jmai-23-161","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140404942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Devil’s advocate: exploring the potential negative impacts of artificial intelligence on the field of surgery","authors":"Mina Sarofim","doi":"10.21037/jmai-23-158","DOIUrl":"https://doi.org/10.21037/jmai-23-158","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Khan, Abubakar Siddique, Asim Mustafa Khan, Bhavya Shetty, Ibrahim Fazal
{"title":"Artificial intelligence in periodontology and implantology—a narrative review","authors":"S. Khan, Abubakar Siddique, Asim Mustafa Khan, Bhavya Shetty, Ibrahim Fazal","doi":"10.21037/jmai-23-186","DOIUrl":"https://doi.org/10.21037/jmai-23-186","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140399423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lucy Bennett, Mohamed Mostafa, R. Hammersley, H. Purssell, Manish Patel, Oliver Street, V. Athwal, Karen Piper Hanley, The ID-LIVER Consortium, Neil A. Hanley, J. Morling, Indra Neil Guha
{"title":"Using a machine learning model to risk stratify for the presence of significant liver disease in a primary care population","authors":"Lucy Bennett, Mohamed Mostafa, R. Hammersley, H. Purssell, Manish Patel, Oliver Street, V. Athwal, Karen Piper Hanley, The ID-LIVER Consortium, Neil A. Hanley, J. Morling, Indra Neil Guha","doi":"10.21037/jmai-23-35","DOIUrl":"https://doi.org/10.21037/jmai-23-35","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139297638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Skin Cancer is on the rise and Melanoma is the most threatening type among the skin cancers. Early detection of skin cancer is vital in order to prevent the cancer to be spread to other parts. In this paper a transfer-learning based system is proposed for Melanoma lesions detection. In the proposed system first, the images are preprocessed for removing the noise and illumination effect. In the next step a convolutional neural network is trained based on transfer learning using the weights of ImageNet data set. In the third step the network is fine-tuned to become more specialized for detecting the Melanoma versus other types of benign cancers. The proposed system uses the information from the image in 3 stages. In each stage the focus will be more concentrate on the center on the image where the suspicious part is. The results from these parts are combined and applied to a fully connected neural network. Results shows the superiority of the proposed methods compare to other state-of-the arts methods.
{"title":"Skin cancer detection using multi-scale deep learning and transfer learning","authors":"Mohammadreza Hajiarbabi","doi":"10.21037/jmai-23-67","DOIUrl":"https://doi.org/10.21037/jmai-23-67","url":null,"abstract":"Skin Cancer is on the rise and Melanoma is the most threatening type among the skin cancers. Early detection of skin cancer is vital in order to prevent the cancer to be spread to other parts. In this paper a transfer-learning based system is proposed for Melanoma lesions detection. In the proposed system first, the images are preprocessed for removing the noise and illumination effect. In the next step a convolutional neural network is trained based on transfer learning using the weights of ImageNet data set. In the third step the network is fine-tuned to become more specialized for detecting the Melanoma versus other types of benign cancers. The proposed system uses the information from the image in 3 stages. In each stage the focus will be more concentrate on the center on the image where the suspicious part is. The results from these parts are combined and applied to a fully connected neural network. Results shows the superiority of the proposed methods compare to other state-of-the arts methods.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alaa Aljiffry, Yanbo Xu, Shenda Hong, Justin B. Long, Jimeng Sun, Kevin O. Maher
{"title":"Artificial intelligence and clinical stability after the Norwood operation","authors":"Alaa Aljiffry, Yanbo Xu, Shenda Hong, Justin B. Long, Jimeng Sun, Kevin O. Maher","doi":"10.21037/jmai-22-35","DOIUrl":"https://doi.org/10.21037/jmai-22-35","url":null,"abstract":"","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139302480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Machine learning (ML) has emerged as a promising tool to assist physicians in diagnosis and classification of patient conditions from medical imaging data. However, as clinical applications of ML become more common, there is concern about the prevalence of ethnoracial biases due to improper algorithm training. It has long been known that cancer outcomes vary for different racial/ethnic groups. Methods: We reviewed 84 studies that reported results of ML algorithms compared to radiologists for cancer prediction to evaluate if algorithms targeted at cancer prediction account for potential ethnoracial biases in their training samples. The search engines used to extract the articles were: PubMed, MEDLINE, and Google Scholar. All studies published before May 2022 were extracted. Two researchers independently reviewed 115 articles and evaluated them for incorporation and inclusion of demographic information in the algorithm. Exclusion criteria were if an inappropriate imaging type was used, if they did not report benign vs. malignant cancer results, if the algorithm was not compared to a board-certified radiologist, or if they were not in English. Results: Of the 84 studies included, 87% (n=73) reported demographic information and 38% (n=32) evaluated the effect of demographic information on model performance. However, only about 11% (n=9) of the articles reported racial/ethnic groups and about 4% (n=3) incorporated racial/ethnic information into their models. Of the nine studies that reported racial/ethnic information, the specified racial/ethnic minorities that were included the most were White/Caucasian (n=9/9) and Black/African American (n=8/9). Asian (n=4/9), American Indian (n=3/9), and Hispanic (n=2/9) were reported in less than half of the studies. Conclusions: The lack of inclusion of not only racial/ethnic information but also other demographic information such as age, gender, body mass index (BMI), or patient history is indicative of a larger problem that exists within artificial intelligence (AI) for cancer imaging. It is crucial to report and consider demographics when considering not only AI for cancer, but also overall care of a cancer patient. The findings from this study highlight a need for greater consideration and evaluation of ML algorithms to consider demographic information when evaluating a patient population for training the algorithm.
{"title":"Racial/ethnic reporting differences in cancer literature regarding machine learning vs. a radiologist: a systematic review and meta- analysis","authors":"Rahil Patel, Destie Provenzano, Sherrie Flynt Wallington, Murray Loew, Yuan James Rao, Sharad Goyal","doi":"10.21037/jmai-23-31","DOIUrl":"https://doi.org/10.21037/jmai-23-31","url":null,"abstract":"Background: Machine learning (ML) has emerged as a promising tool to assist physicians in diagnosis and classification of patient conditions from medical imaging data. However, as clinical applications of ML become more common, there is concern about the prevalence of ethnoracial biases due to improper algorithm training. It has long been known that cancer outcomes vary for different racial/ethnic groups. Methods: We reviewed 84 studies that reported results of ML algorithms compared to radiologists for cancer prediction to evaluate if algorithms targeted at cancer prediction account for potential ethnoracial biases in their training samples. The search engines used to extract the articles were: PubMed, MEDLINE, and Google Scholar. All studies published before May 2022 were extracted. Two researchers independently reviewed 115 articles and evaluated them for incorporation and inclusion of demographic information in the algorithm. Exclusion criteria were if an inappropriate imaging type was used, if they did not report benign vs. malignant cancer results, if the algorithm was not compared to a board-certified radiologist, or if they were not in English. Results: Of the 84 studies included, 87% (n=73) reported demographic information and 38% (n=32) evaluated the effect of demographic information on model performance. However, only about 11% (n=9) of the articles reported racial/ethnic groups and about 4% (n=3) incorporated racial/ethnic information into their models. Of the nine studies that reported racial/ethnic information, the specified racial/ethnic minorities that were included the most were White/Caucasian (n=9/9) and Black/African American (n=8/9). Asian (n=4/9), American Indian (n=3/9), and Hispanic (n=2/9) were reported in less than half of the studies. Conclusions: The lack of inclusion of not only racial/ethnic information but also other demographic information such as age, gender, body mass index (BMI), or patient history is indicative of a larger problem that exists within artificial intelligence (AI) for cancer imaging. It is crucial to report and consider demographics when considering not only AI for cancer, but also overall care of a cancer patient. The findings from this study highlight a need for greater consideration and evaluation of ML algorithms to consider demographic information when evaluating a patient population for training the algorithm.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135714324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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