Investigating Algorithm Awareness in Medical AI: A Multidimensional Literacy Approach

As artificial intelligence (AI) becomes increasingly embedded in healthcare decision-making, understanding medical professionals' algorithm awareness is essential for fostering informed adoption and mitigating biases. This pilot study explores how medical doctors (n=101) across different levels of seniority perceive, engage with, and critically assess AI-driven decision-support systems. By integrating mixed methods—including quantitative surveys, qualitative insights, and scenario-based assessments—this study examines attitudes, experiences, and perceived limitations of AI in medical practice. Findings contribute to the development of a Multidimensional AI Literacy Framework (MALF), categorizing algorithmic literacy into six constructs: Technical, Applied, Critical, Collaborative, Ethical-Normative, and Non-Proficient Literacy. This framework serves as a methodological tool to assess professionals' engagement with AI and highlights the need for targeted educational interventions to enhance algorithm awareness. Furthermore, the study explores how interactive learning techniques, such as Explainable AI (XAI), participatory design, and cross-disciplinary training, can improve algorithmic transparency and professional trust. By employing scenario-based methods, the research also elicits reflections on AI’s role in clinical decision-making and its broader societal implications. These findings align with the panel's focus on methodological advancements in algorithm awareness research, emphasizing how educational strategies—including interdisciplinary teaching, human-computer interaction, and real-world case studies—can bridge algorithmic literacy gaps. Addressing these challenges is crucial to ensuring fairness, transparency, and informed decision-making in algorithmic governance across professional fields.