TRANSFORMING TUMOR BOARDS WITH ADVANCED UX AND ARTIFICIAL INTELLIGENCE
Keywords:
Precision Tumor Board, Artificial Intelligence In Oncology, User Experience Design In Healthcare, Personalized Cancer, Treatment, Clinical Decision Support Systems, Precision User Experience, Precision UX, Oncology UX, Cancer Care UXAbstract
This article explores the transformative impact of integrating advanced artificial intelligence (AI) and user experience (UX) design in Precision Tumor Board (PTB) platforms for cancer treatment. It examines how these cutting-edge technologies revolutionize the traditional tumor board process, enabling more accurate, personalized, and efficient treatment recommendations. The article delves into the key components of PTB platforms, including AI-driven data analysis capabilities, predictive analytics, and intelligent treatment recommendations. It also discusses the crucial role of effective UX design in presenting complex AI-generated insights and ensuring intuitive navigation for healthcare professionals. The synergy between AI and UX design is explored, highlighting strategies for seamless integration and balancing automation with user control. Furthermore, the article addresses important challenges and considerations, such as data privacy, integration with existing healthcare systems, and ethical implications of AI-assisted decision-making. Looking to the future, it outlines potential advancements in AI and UX technologies, possible expansions beyond cancer treatment, and integration with emerging technologies like virtual reality and wearables. By examining the intersection of AI, UX design, and oncology, this article provides valuable insights into how innovative digital tools can advance precision medicine and support oncologists in delivering tailored, effective cancer care.
References
Tamborero, D., Dienstmann, R., Rachid, M.H. et al. The Molecular Tumor Board Portal supports clinical decisions and automated reporting for precision oncology. Nat Cancer 3, 251–261 (2022). https://doi.org/10.1038/s43018-022-00332-x
Boos, L., Wicki, A. The molecular tumor board—a key element of precision oncology. memo 17, 190–193 (2024). https://doi.org/10.1007/s12254-024-00977-7
[3] G. Litjens et al., "A survey on deep learning in medical image analysis," Med. Image Anal., vol. 42, pp. 60-88, Dec. 2017. [Online]. Available: https://pubmed.ncbi.nlm.nih.gov/28778026/
A. Esteva et al., "A guide to deep learning in healthcare," Nat. Med., vol. 25, no. 1, pp. 24-29, Jan. 2019. [Online]. Available: https://www.nature.com/articles/s41591-018-0316-z
A. Holzinger et al., "Causability and explainability of artificial intelligence in medicine," WIREs Data Mining Knowl. Discov., vol. 9, no. 4, p. e1312, 2019. [Online]. Available: https://wires.onlinelibrary.wiley.com/doi/full/10.1002/widm.1312
D. Gunning and D. Aha, "DARPA's Explainable Artificial Intelligence (XAI) Program," AI Mag., vol. 40, no. 2, pp. 44-58, Jun. 2019. [Online]. Available: https://ojs.aaai.org/index.php/aimagazine/article/view/2850
M. A. Musen, B. Middleton, and R. A. Greenes, "Clinical Decision-Support Systems," in Biomedical Informatics, E. H. Shortliffe and J. J. Cimino, Eds. London: Springer, 2014, pp. 643-674. [Online]. Available: https://link.springer.com/chapter/10.1007/978-1-4471-4474-8_22
P. Voigt and A. von dem Bussche, "The EU General Data Protection Regulation (GDPR): A Practical Guide," Cham, Switzerland: Springer International Publishing, 2017. [Online]. Available: https://link.springer.com/book/10.1007/978-3-319-57959-7
C. J. Cai et al., "Human-centered tools for coping with imperfect algorithms during medical decision-making," in Proc. CHI Conf. Human Factors Comput. Syst., 2019, pp. 1-14. [Online]. Available: https://dl.acm.org/doi/10.1145/3290605.3300234
E. J. Topol, "High-performance medicine: the convergence of human and artificial intelligence," Nat. Med., vol. 25, no. 1, pp. 44-56, Jan. 2019. [Online]. Available: https://www.nature.com/articles/s41591-018-0300-7