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Decision Aids in Imaging: Tools for Better Patient Involvement

Daniel Lorenz

• 2 min read
Decision Aids in Imaging: Tools for Better Patient Involvement
Photo by Andrea De Santis / Unsplash

AI in Healthcare: Transforming Patient Engagement and Clinical Outcomes

Speaking to any radiology colleague these days the discussions always comes to AI at one point. And I am under impression that in this discussions our focus often orbits around enhancing the efficiency of:

a) Image production

b) Image analysis

c) Reporting

The quest is clear: to generate more images, analyze them deeply, and accelerate high-quality report generation. However, beyond meeting economic KPIs and ensuring healthcare delivery, there's a pivotal goal – improving patient clinical outcomes.

But how about the 'last mile' in healthcare? The crucial step of amplifying patient engagement and understanding (Davenport & Kalakota, 2019). AI is poised to be a game-changer here.

How AI Enhances Patient-Doctor Dynamics:

  • Empowering Interactions: AI as a first-line support, enhancing the patient-doctor relationship.
  • Personalized Education: Tailoring medical information to diverse patient backgrounds, promoting understanding and informed decisions.
  • Virtual Health Assistants: Boosting patient involvement with instant information, query responses, and integrative therapy advice (Ng et al., 2024; Sng et al., 2023).

Practical AI Applications for Patient Empowerment:

  • AI-Powered Chatbots: Assisting in initial patient support and triage (Marchiori et al., 2020).
  • Medical Language Translation: Customizing complex medical terms into layman's language, bridging the gap between diverse patient literacy levels.

Patient-Empowerment as the Top Target for the Future:

Imagine a healthcare environment where information isn't a doctor's monopoly but a shared treasure with patients. This shift towards equal partnership paves the way for more profound shared decision-making. It's a chance to alleviate some burden off healthcare professionals' shoulders, fostering a collaborative risk assessment and decision process.

The ongoing debate on how informed and empowered patients can potentially reduce healthcare costs is likely to gain more traction with AI advancements (Holmström & Röing, 2010; Chatzimarkakis, 2010). Despite current AI limitations (Sng et al., 2023), let's not forget that AI's mainstream emergence in healthcare is still in its infancy.

Davenport, T H., & Kalakota, R. (2019, June 1). The potential for artificial intelligence in healthcare. , 6(2), 94-98. https://www.rcpjournals.org/lookup/doi/10.7861/futurehosp.6-2-94

Ng, J Y., Cramer, H., Lee, M S., & Moher, D. (2024, February 1). Traditional, complementary, and integrative medicine and artificial intelligence: Novel opportunities in healthcare. Elsevier BV, 101024-101024. https://doi.org/https://doi.org/10.1016/j.imr.2024.101024

Sng, G G R., Tung, J Y M., Lim, D Y Z., & Bee, Y M. (2023, March 15). Potential and Pitfalls of ChatGPT and Natural-Language Artificial Intelligence Models for Diabetes Education. American Diabetes Association, 46(5), e103-e105. https://doi.org/https://doi.org/10.2337/dc23-0197

Marchiori, C., Dykeman, D D., Girardi, I., Ivankay, A., Thandiackal, K., Zusag, M., Giovannini, A., Karpati, D., & Saenz, H. (2020, November 9). Artificial Intelligence Decision Support for Medical Triage. arXiv (Cornell University). https://doi.org/10.48550/arXiv.2011.04548v1

Holmström, I., & Röing, M. (2010, May 1). The relation between patient-centeredness and patient empowerment: A discussion on concepts. Patient Education and Counseling, 79(2), 167-172. https://doi.org/10.1016/j.pec.2009.08.008

Chatzimarkakis, J. (2010, November 1). Why Patients Should Be More Empowered: A European Perspective on Lessons Learned in the Management of Diabetes. Journal of Diabetes Science and Technology, 4(6), 1570-1573. https://doi.org/10.1177/193229681000400634