Aliado - A design concept of AI for decision support in oncological liver surgery
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Background: The interest in artificial intelligence (AI) is increasing. Systematic reviews suggest that there are many machine learning algorithms in surgery, however, only a minority of the studies integrate AI applications in clinical workflows. Our objective was to design and evaluate a concept to use different kinds of AI for decision support in oncological liver surgery along the treatment path. Methods: In an exploratory co-creation between design experts, surgeons, and data scientists, pain points along the treatment path were identified. Potential designs for AI-assisted solutions were developed and iteratively refined. Finally, an evaluation of the design concept was performed with n = 20 surgeons to get feedback on the different functionalities and evaluate the usability with the System Usability Scale (SUS). Participating surgeons had a mean of 14.0 ± 5.0 years of experience after graduation. Results: The design concept was named “Aliado”. Five different scenarios were identified where AI could support surgeons. Mean score of SUS was 68.2 ( ± 13.6 SD). The highest valued functionalities were “individualized prediction of survival, short-term mortality and morbidity”, and “individualized recommendation of surgical strategy”. Conclusion: Aliado is a design prototype that shows how AI could be integrated into the clinical workflow. Even without a fleshed out user interface, the SUS already yielded borderline good results. Expert surgeons rated the functionalities favorably, and most of them expressed their willingness to work with a similar application in the future. Thus, Aliado can serve as a surgical vision of how an ideal AI-based assistance could look like.
Details
Original language | English |
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Article number | 108669 |
Journal | European journal of surgical oncology |
Publication status | E-pub ahead of print - 29 Sept 2024 |
Peer-reviewed | Yes |
External IDs
ORCID | /0000-0002-4590-1908/work/172572826 |
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Keywords
DFG Classification of Subject Areas according to Review Boards
Subject groups, research areas, subject areas according to Destatis
Keywords
- Artificial intelligence, Decision support, Design, Liver cancer, Machine learning, Surgery, Surgical data science