THE AI SAFETY GAP: A SCENARIO-BASED STUDY OF RISKY HEALTH DECISIONS AND MISPLACED TRUST

KRASNIQI, Adriatik and HUSEINI, Amira and SAITI, Rihan (2026) THE AI SAFETY GAP: A SCENARIO-BASED STUDY OF RISKY HEALTH DECISIONS AND MISPLACED TRUST. In: INTERNATIONAL CONFERENCE “FROM RESEARCH TO APPLICATION”, 20 May, 2026.

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Abstract

Artificial intelligence (AI) is becoming increasingly integrated into healthcare and everyday life, offering rapid and accessible medical information to the public. Despite its benefits, concerns remain regarding the accuracy of AI-generated medical advice and the potential risks associated with excessive reliance on these technologies without professional medical consultation. Understanding how individuals perceive and use AI in healthcare is important for promoting safer digital health practices. This study aimed to evaluate public trust in AI-generated medical advice and explore attitudes and behaviors related to AI use in healthcare decision-making. A cross-sectional survey was conducted among 150 participants from the general population. The questionnaire assessed demographic characteristics, frequency of AI use, trust in AI-generated health information, and healthcare-related decision-making behaviors through hypothetical clinical scenarios. The results demonstrated moderate overall trust in AI-generated medical advice, with frequent use of AI tools reported among participants. While most individuals preferred physician guidance when presented with conflicting medical opinions, a proportion of participants still demonstrated uncertainty or willingness to rely on AI recommendations in potentially serious situations. Participants also expressed strong support for clearer safety measures, physician consultation recommendations, and greater public education regarding the responsible use of AI in healthcare. These findings highlight both the growing role of AI in healthcare decision-making and the importance of ensuring safe and responsible use of these technologies. Strengthening public awareness and implementing safety-oriented AI guidance may help reduce inappropriate reliance on AI-generated medical advice and improve patient safety.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Law, Arts and Social Sciences > School of Social Sciences
Depositing User: Unnamed user with email zshi@unite.edu.mk
Date Deposited: 10 Sep 2026 12:32
Last Modified: 10 Sep 2026 12:32
URI: http://eprints.unite.edu.mk/id/eprint/2392

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