AI-Based Diagnostic Tools: Perception, Trust, and Implementation Barriers in Clinical Practice

Abstract

Artificial Intelligence (AI) is transforming the diagnostic landscape by improving accuracy, improving efficiency, and providing decision support in diagnostic healthcare. But clinical uptake has been slow, hobbled by mistrust, ethical quandaries and systemic road blocks.Objective The current study examines perceptions of AI-based diagnostic tools by healthcare professionals, and assesses levels of trust and coordination through identifying barriers affecting integration of such tools into clinical practice.Methods: Systematic review of the literature complemented by a survey of 100 clinicians across multiple specialties. Perceived benefits, concerns, and implementation challenges were analyzed using thematic analysis and descriptive statistics.While clinicians are eager to adopt AI to enhance the diagnostic process, they expressed concerns over AI explainability, legal accountability, data privacy and integration into existing systems. However, trust in AI tools is not consistent across specialties.:While AI diagnostic tools have the potential to bring transformative change, they also face significant barriers to trust and ethics. Widespread clinical deployment will only happen with human-centered design, explainable AI and sound regulation.Artificial Intelligence Clinical Diagnostics Trust Perception of AI Healthcare Technology Barriers to Implementation