Choosing the right artificial intelligence solutions for your radiology department: key factors to consider
The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusi...
| Main Authors: | , , , , , , , |
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| Formato: | Artigo |
| Idioma: | English |
| Publicado em: |
Galenos Publishing House
2024-11-01
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| Colecção: | Diagnostic and Interventional Radiology |
| Assuntos: | |
| Acesso em linha: | https://www.dirjournal.org/articles/choosing-the-right-artificial-intelligence-solutions-for-your-radiology-department-key-factors-to-consider/doi/dir.2024.232658 |
| _version_ | 1826982597567184896 |
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| author | Deniz Alis Toygar Tanyel Emine Meltem Mustafa Ege Seker Delal Seker Hakkı Muammer Karakaş Ercan Karaarslan İlkay Öksüz |
| author_facet | Deniz Alis Toygar Tanyel Emine Meltem Mustafa Ege Seker Delal Seker Hakkı Muammer Karakaş Ercan Karaarslan İlkay Öksüz |
| author_sort | Deniz Alis |
| collection | DOAJ |
| description | The rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care. |
| first_indexed | 2025-02-18T06:23:07Z |
| format | Article |
| id | doaj.art-be03741b79b24f8bb511d29b51d6162e |
| institution | Directory Open Access Journal |
| issn | 1305-3825 1305-3612 |
| language | English |
| last_indexed | 2025-02-18T06:23:07Z |
| publishDate | 2024-11-01 |
| publisher | Galenos Publishing House |
| record_format | Article |
| series | Diagnostic and Interventional Radiology |
| spelling | doaj.art-be03741b79b24f8bb511d29b51d6162e2024-11-11T11:29:07ZengGalenos Publishing HouseDiagnostic and Interventional Radiology1305-38251305-36122024-11-0130635736510.4274/dir.2024.232658Choosing the right artificial intelligence solutions for your radiology department: key factors to considerDeniz Alis0https://orcid.org/0000-0002-7045-1793Toygar Tanyel1https://orcid.org/0000-0002-2421-6880Emine Meltem2https://orcid.org/0000-0003-3927-321XMustafa Ege Seker3https://orcid.org/0000-0001-7664-5786Delal Seker4https://orcid.org/0000-0002-6863-7150Hakkı Muammer Karakaş5https://orcid.org/0000-0002-1328-8520Ercan Karaarslan6https://orcid.org/0000-0002-4581-4273İlkay Öksüz7https://orcid.org/0000-0001-6478-0534Acıbadem Mehmet Ali Aydınlar University Faculty of Medicine, Department of Radiology, İstanbul, Türkiyeİstanbul Technical University, Biomedical Engineering Graduate Program, İstanbul, TürkiyeUniversity of Health Sciences Türkiye, İstanbul Training and Research Hospital, Clinic of Diagnostic and Interventional Radiology, İstanbul, TürkiyeAcıbadem Mehmet Ali Aydınlar University Faculty of Medicine, Department of Radiology, İstanbul, TürkiyeDicle University Faculty of Engineering, Department of Electrical-Electronics Engineering, Diyarbakır, TürkiyeUniversity of Health Sciences, Clinic of Radiology, İstanbul, TürkiyeAcıbadem Mehmet Ali Aydınlar University Faculty of Medicine, Department of Radiology, İstanbul, Türkiyeİstanbul Technical University Faculty of Engineering, Department of Computer Engineering, İstanbul, TürkiyeThe rapid evolution of artificial intelligence (AI), particularly in deep learning, has significantly impacted radiology, introducing an array of AI solutions for interpretative tasks. This paper provides radiology departments with a practical guide for selecting and integrating AI solutions, focusing on interpretative tasks that require the active involvement of radiologists. Our approach is not to list available applications or review scientific evidence, as this information is readily available in previous studies; instead, we concentrate on the essential factors radiology departments must consider when choosing AI solutions. These factors include clinical relevance, performance and validation, implementation and integration, clinical usability, costs and return on investment, and regulations, security, and privacy. We illustrate each factor with hypothetical scenarios to provide a clearer understanding and practical relevance. Through our experience and literature review, we provide insights and a practical roadmap for radiologists to navigate the complex landscape of AI in radiology. We aim to assist in making informed decisions that enhance diagnostic precision, improve patient outcomes, and streamline workflows, thus contributing to the advancement of radiological practices and patient care.https://www.dirjournal.org/articles/choosing-the-right-artificial-intelligence-solutions-for-your-radiology-department-key-factors-to-consider/doi/dir.2024.232658radiologyartificial intelligenceclinical decision-makingcomputer-assisted healthcare economics and organizationsdata security in healthcareregulatory compliance in medicine |
| spellingShingle | Deniz Alis Toygar Tanyel Emine Meltem Mustafa Ege Seker Delal Seker Hakkı Muammer Karakaş Ercan Karaarslan İlkay Öksüz Choosing the right artificial intelligence solutions for your radiology department: key factors to consider Diagnostic and Interventional Radiology radiology artificial intelligence clinical decision-making computer-assisted healthcare economics and organizations data security in healthcare regulatory compliance in medicine |
| title | Choosing the right artificial intelligence solutions for your radiology department: key factors to consider |
| title_full | Choosing the right artificial intelligence solutions for your radiology department: key factors to consider |
| title_fullStr | Choosing the right artificial intelligence solutions for your radiology department: key factors to consider |
| title_full_unstemmed | Choosing the right artificial intelligence solutions for your radiology department: key factors to consider |
| title_short | Choosing the right artificial intelligence solutions for your radiology department: key factors to consider |
| title_sort | choosing the right artificial intelligence solutions for your radiology department key factors to consider |
| topic | radiology artificial intelligence clinical decision-making computer-assisted healthcare economics and organizations data security in healthcare regulatory compliance in medicine |
| url | https://www.dirjournal.org/articles/choosing-the-right-artificial-intelligence-solutions-for-your-radiology-department-key-factors-to-consider/doi/dir.2024.232658 |
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