The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus
Abstract
:1. Introduction
2. Purpose
- (a)
- To examine the main challenges in the field in relation to integration in the health domain.This point is addressed to answer the key question “What are the current challenges to be faced in integrating these technologies into the health domain?”
- (b)
- To deal with the topic of acceptance in the health domain and to address the relevant state of implementation of the used tools to assess this on the insiders.This point is addressed to answer the key question “What tools are currently used to evaluate the acceptance of these technologies among insiders?”
- (c)
- As a side objective, to analyse how the challenges and acceptance are connected to each other and what are the possible ways to proceed to improve the integration of consensus among insiders. This point is addressed to answer the key question “What suggestions emerge from the study to improve the tools used to assess acceptance among insiders, in light of what emerges in the previous points?”
3. Methods
4. Results
4.1. The Challenges
- These challenges according to the following thematic analysis are arranged into six paragraphs with the synopsis of each paper.
4.1.1. Challenges on Algorithms
4.1.2. Challenges Focused on the Professionalism of the Radiologist
4.1.3. Challenges on the Tools, Datasets, and the Workflow
4.1.4. Challenges on the Teamwork
4.1.5. Challenges on the Education
4.1.6. Challenges on the Ethical and Regulatory Issues
4.2. The Tools Used to Assess the Acceptance
4.2.1. General Considerations on the Tools for the Acceptance
- (a)
- (b)
- that the interest into this theme is recent, since publications of studies have started very recently, as the first ones are from 2019, and this reinforces the need for this study.
4.2.2. The Tools for the Acceptance in Details
5. Discussion
5.1. The First Point of View: The Challenges
5.2. Limits and Recommendation for Future Deepening on the Challenges
5.3. The Second Point of View: The Tools for Investigating the Acceptance
5.4. Limits and Recommendation for Future Deepening on the Tools for Acceptance
5.5. Limitation of the Study
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Giansanti, D. The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We? Healthcare 2020, 9, 30. [Google Scholar] [CrossRef]
- Special Issue "The Artificial Intelligence in Digital Pathology and Digital Radiology: Where Are We?". Available online: https://www.mdpi.com/journal/healthcare/special_issues/AI_Digital_Pathology_Radiology (accessed on 23 November 2021).
- Alsharif, M.H.; Alsharif, Y.H.; Yahya, K.; Alomari, O.A.; Albreem, M.A.; Jahid, A. Deep learning applications to combat the dissemination of COVID-19 disease: A review. Eur. Rev. Med. Pharmacol. Sci. 2020, 24, 11455–11460. [Google Scholar] [PubMed]
- Ozsahin, I.; Sekeroglu, B.; Musa, M.S.; Mustapha, M.T.; Ozsahin, D.U. Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. Comput. Math. Methods Med. 2020, 2020, 9756518. [Google Scholar] [CrossRef] [PubMed]
- Luce, B.R.; Drummond, M.; Jönsson, B.; Neumann, P.J.; Schwartz, J.S.; Siebert, U.; Sullivan, S.D. EBM, HTA, and CER: Clearing the Confusion. Milbank Q. 2010, 88, 256–276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- McGlynn, E.A.; Kosecoff, J.; Brook, R.H. Format and Conduct of Consensus Development Conferences. Multination Comparison. Int. J. Technol. Assess. Health Care 1990, 6, 450–469. [Google Scholar] [CrossRef]
- Boldrini, P.; Bonaiuti, D.; Mazzoleni, S.; Posteraro, F. Rehabilitation assisted by robotic and electromechanical devices for people with neurological disabilities: Contributions for the preparation of a national conference in Italy. Eur. J. Phys. Rehabil. Med. 2021, 57, 458–459. [Google Scholar] [CrossRef]
- Maccioni, G.; Ruscitto, S.; Gulino, R.A.; Giansanti, D. Opportunities and Problems of the Consensus Conferences in the Care Robotics. Healthcare 2021, 9, 1624. [Google Scholar] [CrossRef]
- Evidence-Based Medicine Guidelines. Available online: https://www.ebm-guidelines.com/dtk/ebmg/home (accessed on 7 March 2022).
- Dunnmon, J. Separating Hope from Hype: Artificial Intelligence Pitfalls and Challenges in Radiology. Radiol. Clin. N. Am. 2021, 59, 1063–1074. [Google Scholar] [CrossRef]
- Fazal, M.I.; Patel, M.E.; Tye, J.; Gupta, Y. The past, present and future role of artificial intelligence in imaging. Eur. J. Radiol. 2018, 105, 246–250. [Google Scholar] [CrossRef]
- Moawad, A.W.; Fuentes, D.T.; ElBanan, M.G.; Shalaby, A.S.; Guccione, J.; Kamel, S.; Jensen, C.T.; Elsayes, K.M. Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities. J. Comput. Assist. Tomogr. 2022, 46, 78–90. [Google Scholar] [CrossRef]
- Kohli, M.; Alkasab, T.; Wang, K.; Heilbrun, M.E.; Flanders, A.E.; Dreyer, K.; Kahn, C.E. Bending the Artificial Intelligence Curve for Radiology: Informatics Tools From ACR and RSNA. J. Am. Coll. Radiol. 2019, 16, 1464–1470. [Google Scholar] [CrossRef] [PubMed]
- Pesapane, F.; Volonté, C.; Codari, M.; Sardanelli, F. Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights Imaging 2018, 9, 745–753. [Google Scholar] [CrossRef] [PubMed]
- Reeder, K.; Lee, H. Impact of artificial intelligence on US medical students’ choice of radiology. Clin. Imaging 2021, 81, 67–71. [Google Scholar] [CrossRef] [PubMed]
- Gampala, S.; Vankeshwaram, V.; Gadula, S.S.P. Is Artificial Intelligence the New Friend for Radiologists? A Review Article. Cureus 2020, 12, e11137. [Google Scholar] [CrossRef] [PubMed]
- Ahmad, R. Reviewing the relationship between machines and radiology: The application of artificial intelligence. Acta Radiol. Open 2021, 10, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Hameed, B.Z.; Prerepa, G.; Patil, V.; Shekhar, P.; Raza, S.Z.; Karimi, H.; Paul, R.; Naik, N.; Modi, S.; Vigneswaran, G.; et al. Engineering and clinical use of artificial intelligence (AI) with machine learning and data science advancements: Radiology leading the way for future. Ther. Adv. Urol. 2021, 13, 17562872211044880. [Google Scholar] [CrossRef] [PubMed]
- Kottler, N. Artificial Intelligence: A Private Practice Perspective. J. Am. Coll. Radiol. 2020, 17, 1398–1404. [Google Scholar] [CrossRef] [PubMed]
- Martín-Noguerol, T.; Paulano-Godino, F.; López-Ortega, R.; Górriz, J.; Riascos, R.; Luna, A. Artificial intelligence in radiology: Relevance of collaborative work between radiologists and engineers for building a multidisciplinary team. Clin. Radiol. 2021, 76, 317–324. [Google Scholar] [CrossRef] [PubMed]
- Yang, L.; Ene, I.C.; Belaghi, R.A.; Koff, D.; Stein, N.; Santaguida, P. (Lina) Stakeholders’ perspectives on the future of artificial intelligence in radiology: A scoping review. Eur. Radiol. 2021, 32, 1477–1495. [Google Scholar] [CrossRef] [PubMed]
- Pesapane, F. How scientific mobility can help current and future radiology research: A radiology trainee’s perspective. Insights Into Imaging 2019, 10, 85. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pianykh, O.S.; Langs, G.; Dewey, M.; Enzmann, D.R.; Herold, C.J.; Schoenberg, S.O.; Brink, J.A. Continuous Learning AI in Radiology: Implementation Principles and Early Applications. Radiology 2020, 297, 6–14. [Google Scholar] [CrossRef]
- D’Antonoli, T.A. Ethical considerations for artificial intelligence: An overview of the current radiology landscape. Diagn. Interv. Radiol. 2020, 26, 504–511. [Google Scholar] [CrossRef]
- The Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group; Jaremko, J.L.; Azar, M.; Bromwich, R.; Lum, A.; Cheong, L.H.A.; Gibert, M.; LaViolette, F.; Gray, B.; Reinhold, C.; et al. Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology. Can. Assoc. Radiol. J. 2019, 70, 107–118. [Google Scholar] [CrossRef] [Green Version]
- Banja, J.; Rousselle, R.; Duszak, R., Jr.; Safdar, N.; Alessio, A.M. Sharing and Selling Images: Ethical and Regulatory Considerations for Radiologists. J. Am. Coll. Radiol. 2021, 18, 298–304. [Google Scholar] [CrossRef] [PubMed]
- Barragán-Montero, A.; Javaid, U.; Valdés, G.; Nguyen, D.; Desbordes, P.; Macq, B.; Willems, S.; Vandewinckele, L.; Holmström, M.; Löfman, F.; et al. Artificial intelligence and machine learning for medical imaging: A technology review. Phys. Med. 2021, 83, 242–256. [Google Scholar] [CrossRef] [PubMed]
- Cushnan, D.; Berka, R.; Bertolli, O.; Williams, P.; Schofield, D.; Joshi, I.; Favaro, A.; Halling-Brown, M.; Imreh, G.; Jefferson, E.; et al. Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic. Digit. Health 2021, 7, 1–13. [Google Scholar] [CrossRef] [PubMed]
- Morrison, K. Artificial intelligence and the NHS: A qualitative exploration of the factors influencing adoption. Futur. Health J. 2021, 8, e648–e654. [Google Scholar] [CrossRef]
- Fischetti, C.; Bhatter, P.; Frisch, E.; Sidhu, A.; Helmy, M.; Lungren, M.; Duhaime, E. The Evolving Importance of Artificial Intelligence and Radiology in Medical Trainee Education. Acad. Radiol. 2021, 28, 916–921. [Google Scholar] [CrossRef]
- Lennartz, S.; Dratsch, T.; Zopfs, D.; Persigehl, T.; Maintz, D.; Hokamp, N.G.; dos Santos, D.P. Use and Control of Artificial Intelligence in Patients Across the Medical Workflow: Single-Center Questionnaire Study of Patient Perspectives. J. Med. Int. Res. 2021, 23, e24221. [Google Scholar] [CrossRef]
- Zhang, Z.; Citardi, D.; Wang, D.; Genc, Y.; Shan, J.; Fan, X. Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data. Health Inform. J. 2021, 27, 1–13. [Google Scholar] [CrossRef]
- Ongena, Y.P.; Haan, M.; Yakar, D.; Kwee, T.C. Patients’ views on the implementation of artificial intelligence in radiology: Development and validation of a standardized questionnaire. Eur. Radiol. 2020, 30, 1033–1040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hendrix, N.; Hauber, B.; Lee, C.I.; Bansal, A.; Veenstra, D.L. Artificial intelligence in breast cancer screening: Primary care provider preferences. J. Am. Med. Inform. Assoc. 2021, 28, 1117–1124. [Google Scholar] [CrossRef] [PubMed]
- Abuzaid, M.M.; Elshami, W.; McConnell, J.; Tekin, H.O. An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice. Health Technol. 2021, 11, 1045–1050. [Google Scholar] [CrossRef]
- Abuzaid, M.; Tekin, H.; Reza, M.; Elhag, I.; Elshami, W. Assessment of MRI technologists in acceptance and willingness to integrate artificial intelligence into practice. Radiography 2021, 27, S83–S87. [Google Scholar] [CrossRef] [PubMed]
- Giansanti, D.; Rossi, I.; Monoscalco, L. Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders. Healthcare 2021, 9, 331. [Google Scholar] [CrossRef]
- Abuzaid, M.M.; Elshami, W.; Tekin, H.; Issa, B. Assessment of the Willingness of Radiologists and Radiographers to Accept the Integration of Artificial Intelligence Into Radiology Practice. Acad. Radiol. 2020, 29, 87–94. [Google Scholar] [CrossRef]
- Alelyani, M.; Alamri, S.; Alqahtani, M.S.; Musa, A.; Almater, H.; Alqahtani, N.; Alshahrani, F.; Alelyani, S. Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology. Healthcare 2021, 9, 834. [Google Scholar] [CrossRef]
- European Society of Radiology (ESR). Impact of artificial intelligence on radiology: A EuroAIM survey among members of the European Society of Radiology. Insights Imaging 2019, 10, 105. [Google Scholar] [CrossRef] [Green Version]
- Galán, G.C.; Portero, F.S. Percepciones de estudiantes de Medicina sobre el impacto de la inteligencia artificial en radiología. Radiología 2021, in press. [Google Scholar] [CrossRef]
- Di Basilio, F.; Esposisto, G.; Monoscalco, L.; Giansanti, D. The Artificial Intelligence in Digital Radiology: Part 2: Towards an Investigation of acceptance and consensus on the Insiders. Healthcare 2022, 10, 153. [Google Scholar] [CrossRef] [PubMed]
- Diaz, O.; Guidi, G.; Ivashchenko, O.; Colgan, N.; Zanca, F. Artificial intelligence in the medical physics community: An international survey. Phys. Med. 2021, 81, 141–146. [Google Scholar] [CrossRef] [PubMed]
- Coppola, F.; Faggioni, L.; Regge, D.; Giovagnoni, A.; Golfieri, R.; Bibbolino, C.; Miele, V.; Neri, E.; Grassi, R. Artificial intelligence: Radiologists’ expectations and opinions gleaned from a nationwide online survey. La Radiol. Med. 2021, 126, 63–71. [Google Scholar] [CrossRef] [PubMed]
- Aldosari, B. User acceptance of a picture archiving and communication system (PACS) in a Saudi Arabian hospital radiology department. BMC Med. Inform. Decis. Mak. 2012, 12, 44. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Goldberg, J.E.; Rosenkrantz, A. Artificial Intelligence and Radiology: A Social Media Perspective. Curr. Probl. Diagn. Radiol. 2019, 48, 308–311. [Google Scholar] [CrossRef] [PubMed]
- Sideris, G.A.; Nikolakea, M.; Karanikola, A.-E.; Konstantinopoulou, S.; Giannis, D.; Modahl, L. Imaging in the COVID-19 era: Lessons learned during a pandemic. World J. Radiol. 2021, 13, 192–222. [Google Scholar] [CrossRef] [PubMed]
- Pezzutti, D.L.; Wadhwa, V.; Makary, M.S. COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances. World J. Radiol. 2021, 13, 171–191. [Google Scholar] [CrossRef] [PubMed]
- El Naqa, I.M.; Li, H.; Fuhrman, J.D.; Hu, Q.; Gorre, N.; Chen, W.; Giger, M.L. Lessons learned in transitioning to AI in the medical imaging of COVID-19. J. Med. Imaging 2021, 8 (Suppl. S1), 010902. [Google Scholar] [CrossRef]
- Currie, G.; Rohren, E. Social Asymmetry, Artificial Intelligence and the Medical Imaging Landscape. Semin. Nucl. Med. 2021; in press. [Google Scholar] [CrossRef] [PubMed]
Parameter | Description | Score (1 = min; 5 = max) |
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1 | Is the research design appropriate? | |
2 | Are the methods adequately described? | |
3 | Are the results clearly presented? | |
4 | Are the conclusions supported by results | |
5 | Added contribute to the field | |
6 | Topicality level of the study | |
7 | Focus on the health domain |
Key |
---|
“artificial intelligence”[Title/Abstract] AND “radiology”[Title/Abstract] AND (“challenges”[Title/Abstract] OR “future research”[Title/Abstract] OR “integration”[Title/Abstract] OR “opportunity”[Title/Abstract] OR “future direction”[Title/Abstract]) |
Key |
---|
“artificial intelligence”[Title/Abstract] AND (“radiology”[MeSH Terms] OR “radiology”[All Fields] OR “radiography”[MeSH Terms] OR “radiography”[All Fields] OR “radiology s”[All Fields]) AND (“accept”[All Fields] OR “acceptabilities”[All Fields] OR “acceptability”[All Fields] OR “acceptable”[All Fields] OR “acceptably”[All Fields] OR “acceptance”[All Fields] OR “acceptances”[All Fields] OR “acceptation”[All Fields] OR “accepted”[All Fields] OR “accepter”[All Fields] OR “accepters”[All Fields] OR “accepting”[All Fields] OR “accepts”[All Fields]) |
(“consensual”[All Fields] OR “consensually”[All Fields] OR “consensus”[MeSH Terms] OR “consensus”[All Fields]) AND “artificial intelligence”[Title/Abstract] AND “radiology”[Title/Abstract] |
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Giansanti, D.; Di Basilio, F. The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus. Healthcare 2022, 10, 509. https://doi.org/10.3390/healthcare10030509
Giansanti D, Di Basilio F. The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus. Healthcare. 2022; 10(3):509. https://doi.org/10.3390/healthcare10030509
Chicago/Turabian StyleGiansanti, Daniele, and Francesco Di Basilio. 2022. "The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus" Healthcare 10, no. 3: 509. https://doi.org/10.3390/healthcare10030509