Skip to main content Skip to main navigation menu Skip to site footer
Articles
Published: 2024-12-19

Capturing patient voices: A focus group-based study unveiling the potential of AI in medical diagnosis

Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, Naples, 80125 NA, Italy
Unit of Diagnostic Imaging and Stereotactic Radiosurgery, CDI Centro Diagnostico Italiano S.p.A., Via Simone Saint Bon 20, Milan, 20147, Italy
RE:LAB s.r.l. Via Tamburini, 5, Reggio Emilia, 42122, Italy
Radiology Department, ASST Fatebenefratelli Sacco Piazza Principessa Clotilde 3, Milan, 20121, Italy
Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, Naples, 80125 NA, Italy
Suor Orsola Benincasa University, Corso Vittorio Emanuele 292, Naples, 80125 NA, Italy
Bracco Imaging S.p.A. Via Egidio Folli, 50, Milan, 20134, Italy
RE:LAB s.r.l. Via Tamburini, 5, Reggio Emilia, 42122, Italy
RE:LAB s.r.l. Via Tamburini, 5, Reggio Emilia, 42122, Italy
Artificial Intelligence medical diagnosis patient perspective focus group UX

Abstract

Purpose: This study examines patients' perspectives on the integration of artificial intelligence (AI) in radiology through focus groups, aiming to identify the main issues and areas for improvement. It is part of a larger research project that employs various methodologies to explore the views of both patients and radiologists regarding AI tools. Methods: We conducted two focus groups using a narrative story and vignettes: one with patients who self-assessed as AI experts and the other with non-AI experts. Results: The focus groups revealed diverse opinions on AI use in diagnostics, focusing on six main topics: acceptance, concerns, communication between radiologists and patients, explainability of AI, medical records, and emotional aspects. Conclusions: The findings underscore the importance of developing patient-centric AI solutions to build trust in AI-assisted diagnostic tools, considering emotional and communicational aspects and addressing both physician and patient concerns to facilitate smoother integration of AI in radiology.

Metrics

Metrics Loading ...

References

  1. Alì, M., Fantesini, A., Morcella, M. T., Ibba, S., D'Anna, G., Fazzini, D., & Papa, S. (2024). Adoption of AI in Oncological Imaging: Ethical, Regulatory, and Medical-Legal Challenges. Critical Reviews™ in Oncogenesis, 29(2).
  2. Beets, B., Newman, T.P., Howell, E.L., Bao, L., Yang, S., 2023. Surveying public perceptions of artificial intelligence in health care in the united states: Systematic review. Journal of Medical Internet Research 25, e40337.
  3. Cè, M., Ibba, S., Cellina, M., Tancredi, C., Fantesini, A., Fazzini, D., Fortunati, A., ... & Alì, M. (2024). Radiologists’ perceptions on AI integration: An in-depth survey study. European Journal of Radiology, 111590. ISBN: 0720-048X
  4. Center, P.R., 2021. Ai and human enhancement: Americans’ openness is tempered by a range of concerns. Pew Research Center.
  5. Davenport, T., Kalakota, R., 2019. The potential for artificial intelligence in healthcare. Future healthcare journal 6, 94.
  6. D’Amico, N.C., Grossi, E., Valbusa, G., Rigiroli, F., Colombo, B., Buscema, M., Fazzini, D., Ali, M., Malasevschi, A., Cornalba, G., et al., 2020. A machine learning approach for differentiating malignant from benign enhancing foci on breast mri. European Radiology Experimental 4, 1–8.
  7. Esmaeilzadeh, P., Mirzaei, T., Dharanikota, S., 2021. Patients’ perceptions toward human–artificial intelligence interaction in health care: experimental study. Journal of medical Internet research 23, e25856.
  8. Fitzgerald, R., 2001. Error in radiology. Clinical radiology 56, 938–946.
  9. Gampala, S., Vankeshwaram, V., Gadula, S.S.P., 2020. Is artificial intelligence the new friend for radiologists? a review article. Cureus 12.
  10. Greco, F., Picozzi, M., et al., 2022. Understanding the impact of artificial intelligence on physician-patient relationship: a revisitation of conventional relationship models in the light of new technological frontiers. MEDICINA HISTORICA 6, 1–9.
  11. Ho, M. T., Le, N. T. B., Mantello, P., Ho, M. T., & Ghotbi, N. (2023). Understanding the acceptance of emotional artificial intelligence in Japanese healthcare system: a cross-sectional survey of clinic visitors’ attitude. Technology in Society, 72, 102166.
  12. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L.H., Aerts, H.J., 2018. Artificial intelligence in radiology. Nature Reviews Cancer 18, 500–510.
  13. Hummel, P., Braun, M., Bischoff, S., Samhammer, D., Seitz, K., Fasching, P. A., & Dabrock, P. (2024). Perspectives of patients and clinicians on big data and AI in health: a comparative empirical investigation. AI & SOCIETY, 1-15.
  14. Ibba, S., Tancredi, C., Fantesini, A., Cellina, M., Presta, R., Montanari, R., Papa, S., Alì, M., 2023. How do patients perceive the ai-radiologists interaction? results of a survey on 2,119 responders. European Journal of Radiology , 110917.
  15. Kang, E. Y. N., Chen, D. R., & Chen, Y. Y. (2023). Associations between literacy and attitudes toward artificial intelligence–assisted medical consultations: The mediating role of perceived distrust and efficiency of artificial intelligence. Computers in Human Behavior, 139, 107529.
  16. Kulkarni, S., Seneviratne, N., Baig, M.S., Khan, A.H.A., 2020. Artificial intelligence in medicine: where are we now? Academic radiology 27, 62–70.
  17. Lekadir, K., Quaglio, G., Garmendia, A.T., Gallin, C., 2022. Artificial intelligence in healthcare-applications, risks, and ethical and societal impacts. European Parliament.
  18. Li, B., de Mestral, C., Mamdani, M., & Al-Omran, M. (2022). Perceptions of Canadian vascular surgeons toward artificial intelligence and machine learning. Journal of Vascular Surgery Cases, Innovations and Techniques, 8(3), 466-472.
  19. Malik, P., Pathania, M., Rathaur, V.K., et al., 2019. Overview of artificial intelligence in medicine. Journal of family medicine and primary care 8, 2328.
  20. Mazza, R., Berre, A., 2007. Focus group methodology for evaluating information visualization techniques and tools, in: 2007 11th International Conference Information Visualization (IV’07), IEEE. pp. 74–80.
  21. Mousa, A. H., Maria, N. T., Almuntashiri, F. S., Alsaywid, B. S., & Lytras, M. D. (2023). The potential of artificial intelligence in healthcare: Perceptions of healthcare practitioners and current adoption. In Digital Transformation in Healthcare in Post-Covid-19 Times (pp. 27-41). Academic Press.
  22. Onwuegbuzie, A.J., Dickinson, W.B., Leech, N.L., Zoran, A.G., 2009. A qualitative framework for collecting and analyzing data in focus group research. International journal of qualitative methods 8, 1–21.
  23. Rabiee, F., 2004. Focus-group interview and data analysis. Proceedings of the nutrition society 63, 655–660.
  24. Sauerbrei, A., Kerasidou, A., Lucivero, F., Hallowell, N., 2023. The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions. BMC Medical Informatics and Decision Making 23, 1–14.
  25. Schaarup, J. F., Aggarwal, R., Dalsgaard, E. M., Norman, K., Dollerup, O. L., Ashrafian, H., ... & Hulman, A. (2023). Perception of artificial intelligence-based solutions in healthcare among people with and without diabetes: A cross-sectional survey from the health in Central Denmark cohort. Diabetes Epidemiology and Management, 9, 100114.
  26. Secchi, F., Interlenghi, M., Alì, M., Schiavon, E., Monti, C.B., Capra, D., Salvatore, C., Castiglioni, I., Papa, S., Sardanelli, F., et al., 2022. A combined deep learning system for automatic detection of “bovine” aortic arch on computed tomography scans. Applied Sciences 12, 2056.
  27. European Society of Radiology (ESR) communications@ myesr. org Neri Emanuele de Souza Nandita Brady Adrian Bayarri Angel Alberich Becker Christoph D. Coppola Francesca Visser Jacob. (2019). What the radiologist should know about artificial intelligence–an ESR white paper. Insights into imaging, 10(1), 44.
  28. Topff, L., Ranschaert, E.R., Bartels-Rutten, A., Negoita, A., Menezes, R., Beets-Tan, R.G., Visser, J.J., 2023. Artificial intelligence tool for detection and worklist prioritization reduces time to diagnosis of incidental pulmonary embolism at ct. Radiology: Cardiothoracic Imaging 5, e220163.
  29. Topol, E., et al., 2019. The topol review. Preparing the healthcare workforce to deliver the digital future, 1–48.
  30. Vo, V., Chen, G., Aquino, Y. S. J., Carter, S., Do, Q., & Woode, M. E. (2023). Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis. Social Science & Medicine, 116357.
  31. Vorm, E.S., 2018. Assessing demand for transparency in intelligent systems using machine learning, in: 2018 Innovations in Intelligent Systems and Applications (INISTA), IEEE. pp. 1–7.
  32. Wilkinson, S., 1998. Focus group methodology: a review. International journal of social research methodology 1, 181–203.
  33. Yu, K.H., Beam, A.L., Kohane, I.S., 2018. Artificial intelligence in healthcare. Nature biomedical engineering 2, 719–731.
  34. Zhang, Z., Citardi, D., Wang, D., Genc, Y., Shan, J., Fan, X., 2021. Patients’ perceptions of using artificial intelligence (ai)-based technology to comprehend radiology imaging data. Health Informatics Journal 27, 14604582211011215.
  35. Zhu, B., Liu, J.Z., Cauley, S.F., Rosen, B.R., Rosen, M.S., 2018. Image reconstruction by domain-transform manifold learning. Nature 555, 487–492.

How to Cite

Tancredi, C., Ibba, S., Fantesini, A., Cellina, M., Presta, R., Mancuso, L., Alì, M., Papa, S., & Montanari, R. (2024). Capturing patient voices: A focus group-based study unveiling the potential of AI in medical diagnosis. Human Technology, 20(3), 541–557. https://doi.org/10.14254/1795-6889.2024.20-3.6