The extensive use of efficient technologies in fields like science, health, economy, and media has raised concerns about data transparency, reliability, confidentiality, and bias. These issues create social anxiety and affect the perception of security. This study aims to identify why AI-based medical diagnostic systems are mistrusted, explore how this impacts clinical outcomes, and suggest improvements to enhance system reliability and user understanding. The research involved reviewing open access studies from Google Scholar, IEEE Xplore, and Web of Science, focusing on AI-based medical diagnosis reliability issues. A total of 24 sources from both international and local academic journals were analyzed. Distrust in AI-based medical diagnostics stems from technology complexity, algorithm opacity, and data security concerns. These factors decrease patient adherence to treatment and hinder healthcare professionals' adaptation to new technologies. Proposed solutions include training for healthcare professionals and patients, improved user interfaces, and greater algorithm transparency. Ethically developed AI systems, prioritizing user needs, can enhance trust in the healthcare sector. To combat mistrust, the study suggests increasing AI literature through training programs, making algorithm decision-making transparent, developing user-friendly interfaces, strengthening data security, and updating legal regulations. Implementing these recommendations should make AI more comprehensible and acceptable, enhancing patient satisfaction and service quality.