AI-Driven Diagnostic Tools: A Survey of Adoption and Outcomes in Global Healthcare Practices

The roles of artificial intelligence in health care, especially in diagnosis, are evident as they function as a multiplier in diagnosing patients. This research explored how healthcare organizations incorporate decision support tools based on artificial intelligence technology, the problems healthcare professionals encounter during the integration process, and the results obtained from applying artificial intelligence technology at the system level. It therefore sought to identify the differences in the identified factors across regions and types of facilities. A quantitative survey with questions in a closed format was used; the participants were 260 healthcare professionals from different hospitals and clinics, doctors, healthcare managers and IT specialists. The findings showed that the more resources and specialist human labor are accessible, the more public and private hospitals have embraced AI. Compared to the smaller clinics, research institutions expressed considerable difficulties, especially in costs and training opportunities. Hence, the map reveals that North America and Europe have a higher overall rate of Broadband absorption than Africa and South America, where financial and infrastructural constraints are even higher. This study reveals that, though the application of AI in diagnosing amplified the diagnosis rate and benefitted distinct treatment plans, there are barriers, including prohibitive costs, regulation norms, and the requirement of training. The research showed that the greater the level of AI implementation, the higher the satisfaction among the healthcare organization staff; therefore, the approach should be adjusted depending on the healthcare facility setting. The study's findings suggest that approaches must be adjusted depending on the region and type of facility in mind. It also emphasizes the need to spend sums on training and investing in structures so that the global advantages of integrating Artificial Intelligence into diagnostic tools can be achieved, especially in developing countries. Future studies should identify ways of making AI more affordable in the health sector and conduct more extended research on the efficiency of AI in healthcare.