Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization
Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall i...
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| Veröffentlicht in: | Emergency radiology 2024-12, Vol.31 (6), p.887-901 |
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| creator | Fathi, Mobina Eshraghi, Reza Behzad, Shima Tavasol, Arian Bahrami, Ashkan Tafazolimoghadam, Armin Bhatt, Vivek Ghadimi, Delaram Gholamrezanezhad, Ali |
| description | Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.
Graphical abstract
A summary of most important contents reviewed in this paper. |
| doi_str_mv | 10.1007/s10140-024-02278-2 |
| format | Article |
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Graphical abstract
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Graphical abstract
A summary of most important contents reviewed in this paper.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Calcification</subject><subject>Decision making</subject><subject>Efficiency</subject><subject>Electronic health records</subject><subject>Emergency medical care</subject><subject>Emergency medical services</subject><subject>Emergency Medicine</subject><subject>Emergency Service, Hospital</subject><subject>Fractures</subject><subject>Hemorrhage</subject><subject>Humans</subject><subject>Imaging</subject><subject>Intracranial Hemorrhages - diagnostic imaging</subject><subject>Medical diagnosis</subject><subject>Medical imaging</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Pathology</subject><subject>Radiology</subject><subject>Review Article</subject><subject>Rib Fractures - diagnostic imaging</subject><subject>Spinal Fractures - diagnostic imaging</subject><issn>1438-1435</issn><issn>1070-3004</issn><issn>1438-1435</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kctu1TAQhiNERUvhBVggS2y6CR07dmKzQ1W5SJVgAWvLx5kEl8QOttPq8FQ8Ij4XLmLBwvbY880_Y_1V9YzCSwrQXSYKlEMNjJfFOlmzB9UZ5Y2syyYe_hWfVo9TugWAVrXyUXXaKKqANXBW_fgYMvrszERSjujH_IUY35N7NF89pkTCQEzMbnB2xzifcZrciN7i_jJGk13wJSY4Y9wltiSa3oUpjNtXxJCIdw7vdzq9M6MPKTtL1uwm931fmvb9zLJMzh4fithSwjIXsSYiCUt28xF_Up0MZkr49HieV5_fXH-6elfffHj7_ur1TW2ZaHPdSsEkNRyoFVJBJzvRWmW42FClSrQB3vfQ8N4Air6nrO3kBlocAHlJq-a8ujjoLjF8WzFlPbtky-eNx7Am3YDquOKCiYK--Ae9DWv0ZTrdUE6VZCChUOxA2RhSijjoJbrZxK2moHd-6oOfuvip935qVoqeH6XXzYz975JfBhagOQCppPyI8U_v_8j-BMUtrtk</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Fathi, Mobina</creator><creator>Eshraghi, Reza</creator><creator>Behzad, Shima</creator><creator>Tavasol, Arian</creator><creator>Bahrami, Ashkan</creator><creator>Tafazolimoghadam, Armin</creator><creator>Bhatt, Vivek</creator><creator>Ghadimi, Delaram</creator><creator>Gholamrezanezhad, Ali</creator><general>Springer International Publishing</general><general>Springer Nature B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6930-4246</orcidid></search><sort><creationdate>20241201</creationdate><title>Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization</title><author>Fathi, Mobina ; Eshraghi, Reza ; Behzad, Shima ; Tavasol, Arian ; Bahrami, Ashkan ; Tafazolimoghadam, Armin ; Bhatt, Vivek ; Ghadimi, Delaram ; Gholamrezanezhad, Ali</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c256t-685281a401c589078756c9a45b1996c9b04dd034da0e5dd12678b06ef0e46c993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Calcification</topic><topic>Decision making</topic><topic>Efficiency</topic><topic>Electronic health records</topic><topic>Emergency medical care</topic><topic>Emergency medical services</topic><topic>Emergency Medicine</topic><topic>Emergency Service, Hospital</topic><topic>Fractures</topic><topic>Hemorrhage</topic><topic>Humans</topic><topic>Imaging</topic><topic>Intracranial Hemorrhages - diagnostic imaging</topic><topic>Medical diagnosis</topic><topic>Medical imaging</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Pathology</topic><topic>Radiology</topic><topic>Review Article</topic><topic>Rib Fractures - diagnostic imaging</topic><topic>Spinal Fractures - diagnostic imaging</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fathi, Mobina</creatorcontrib><creatorcontrib>Eshraghi, Reza</creatorcontrib><creatorcontrib>Behzad, Shima</creatorcontrib><creatorcontrib>Tavasol, Arian</creatorcontrib><creatorcontrib>Bahrami, Ashkan</creatorcontrib><creatorcontrib>Tafazolimoghadam, Armin</creatorcontrib><creatorcontrib>Bhatt, Vivek</creatorcontrib><creatorcontrib>Ghadimi, Delaram</creatorcontrib><creatorcontrib>Gholamrezanezhad, Ali</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><jtitle>Emergency radiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fathi, Mobina</au><au>Eshraghi, Reza</au><au>Behzad, Shima</au><au>Tavasol, Arian</au><au>Bahrami, Ashkan</au><au>Tafazolimoghadam, Armin</au><au>Bhatt, Vivek</au><au>Ghadimi, Delaram</au><au>Gholamrezanezhad, Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization</atitle><jtitle>Emergency radiology</jtitle><stitle>Emerg Radiol</stitle><addtitle>Emerg Radiol</addtitle><date>2024-12-01</date><risdate>2024</risdate><volume>31</volume><issue>6</issue><spage>887</spage><epage>901</epage><pages>887-901</pages><issn>1438-1435</issn><issn>1070-3004</issn><eissn>1438-1435</eissn><abstract>Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. 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Graphical abstract
A summary of most important contents reviewed in this paper.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>39190230</pmid><doi>10.1007/s10140-024-02278-2</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-6930-4246</orcidid></addata></record> |
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| subjects | Accuracy Algorithms Artificial Intelligence Calcification Decision making Efficiency Electronic health records Emergency medical care Emergency medical services Emergency Medicine Emergency Service, Hospital Fractures Hemorrhage Humans Imaging Intracranial Hemorrhages - diagnostic imaging Medical diagnosis Medical imaging Medicine Medicine & Public Health Pathology Radiology Review Article Rib Fractures - diagnostic imaging Spinal Fractures - diagnostic imaging |
| title | Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization |
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