The work presented in this article is part of a broader research initiative whose focus revolves around the integration of Artificial Intelligence (AI) in diagnosing Mild Cognitive Impairment (MCI), in particular, investigating the reliability and stability of eXplainable AI (XAI) predictions concerning markers of MCI and Alzheimer's Disease. In order to foster neurologists' understanding, confidence and trust in the AI system results, the initial Machine Learning (ML) based analysis pipeline has been now extended to incorporate a graphical user interface (GUI) that would provide "neurologist-centred" explanations. In this article, the focus is on a preliminary study that involved neurology professionals to assess their understanding and confidence in AI-generated diagnoses presented through three alternative plots implemented in the system GUI.
Intuitiveness and Trustworthiness of AI-Powered Interfaces for Neurological Diagnosis - Preliminary Results / Lombardi, Angela; Marzo, Sofia; Di Sciascio, Eugenio; Di Noia, Tommaso; Ardito, Carmelo. - 14793 LNCS:(2024), pp. 273-280. [10.1007/978-3-031-64576-1_18]
Intuitiveness and Trustworthiness of AI-Powered Interfaces for Neurological Diagnosis - Preliminary Results
Lombardi, Angela;Marzo, Sofia;Di Sciascio, Eugenio;Di Noia, Tommaso;Ardito, Carmelo
2024-01-01
Abstract
The work presented in this article is part of a broader research initiative whose focus revolves around the integration of Artificial Intelligence (AI) in diagnosing Mild Cognitive Impairment (MCI), in particular, investigating the reliability and stability of eXplainable AI (XAI) predictions concerning markers of MCI and Alzheimer's Disease. In order to foster neurologists' understanding, confidence and trust in the AI system results, the initial Machine Learning (ML) based analysis pipeline has been now extended to incorporate a graphical user interface (GUI) that would provide "neurologist-centred" explanations. In this article, the focus is on a preliminary study that involved neurology professionals to assess their understanding and confidence in AI-generated diagnoses presented through three alternative plots implemented in the system GUI.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
