Exploring Human-Centered AI in Healthcare: Diagnosis, Explainability, and Trust
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AI has become an increasingly active area of research over the past few years in healthcare. Nevertheless, not all research advancements are applicable in the field as there are only a few AI solutions that are actually deployed in medical infrastructures or actively used by medical practitioners. This can be due to various reasons as the lack of a human-centered approach for the or non-incorporation of humans in the loop. In this workshop, we aim to address the questions relevant to human-centered AI solutions associated with healthcare by exploring different human-centered approaches for designing AI systems and using image-based datasets for medical diagnosis. We aim to bring together researchers and practitioners in AI, human-computer interaction, healthcare, etc., and expedite the discussions about making usable systems that will be more comprehensible and dependable. Findings from our workshop may serve as ‘terminus a quo’ to significantly improve AI solutions for medical diagnosis.
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Number of citations to item: 4
- Taiwo Kolajo, Olawande Daramola (2023): Human-centric and Semantics-based Explainable Event Detection: A Survey, doi:10.21203/rs.3.rs-2639603/v1
- Dimitra Dritsa, Loes van Renswouw, Sara Colombo, Kaisa Väänänen, Sander Bogers, Arian Martinez, Jess Holbrook, Aarnout Brombacher (2024): Designing (with) AI for Wellbeing, In: Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613905.3636282
- Taiwo Kolajo, Olawande Daramola (2023): Human-centric and semantics-based explainable event detection: a survey, In: Artificial Intelligence Review S1(56), doi:10.1007/s10462-023-10525-0
- Sheree May Saßmannshausen, Nazmun Nisat Ontika, Aparecido Fabiano Pinatti De Carvalho, Mark Rouncefield, Volkmar Pipek (2024): Amplifying Human Capabilities in Prostate Cancer Diagnosis: An Empirical Study of Current Practices and AI Potentials in Radiology, In: Proceedings of the CHI Conference on Human Factors in Computing Systems, doi:10.1145/3613904.3642362