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Summary: Artificial intelligence is transforming clinical practice, but its greatest impact is not in replacing healthcare professionals. By taking on tasks related to data processing, pattern recognition, and risk prediction, AI enhances the analytical capabilities of care teams while reinforcing the importance of uniquely human skills such as clinical judgment, communication, leadership, and decision-making. In

Summary: Artificial intelligence in healthcare depends on much more than advanced algorithms. To deliver meaningful impact in clinical practice, it requires structured data, scientific validation, integration into clinical workflows, and strong clinical governance. These pillars enable data to be transformed into actionable intelligence that supports decision-making, anticipates risks, and improves the quality and safety

Summary: Patient safety is entering a new stage of evolution. Over the past decades, hospitals and healthcare institutions worldwide have strengthened their reporting, investigation and learning processes based on adverse events. Artificial intelligence is now expanding that capability by enabling increasingly preventive approaches. In the seventh article of the series "AI in Healthcare: Credibility, Safety,

Summary: Healthcare is entering a new era. After years focused on digitizing records and generating reports, healthcare organizations are moving toward operational intelligence, where data is transformed into insights, predictions, recommendations, and actions that support patient care in real time. In the sixth article of the series “AI in Healthcare: Credibility, Safety, and Impact on

Summary: Artificial intelligence is already part of clinical practice, supporting diagnoses, identifying risks, and contributing to care-related decision-making. As these technologies take on a more active role in patient care, a growing need has emerged: establishing oversight, transparency, and accountability mechanisms that ensure their safe and responsible use. In the fifth article of the series

Summary: Although artificial intelligence in medicine has advanced rapidly, a global survey reveals that only 16% of healthcare professionals use AI tools to support clinical decision-making. This finding suggests that the greatest challenge facing AI today is no longer algorithm accuracy, but usability at the point of care. The fourth article in the editorial series

Summary: Artificial intelligence is advancing rapidly in medicine, but a statistic from MIT reveals that approximately 95% of AI pilot projects fail when implemented in real-world settings. The reason is simple: outstanding technical accuracy in controlled environments does not necessarily translate into safety at the bedside. The third article in the editorial series “AI in

Summary: In today’s rapidly expanding healthcare AI landscape, the greatest challenge to widespread adoption is not technological sophistication but the quality of the data that powers it. Data volume alone does not necessarily translate into clinical value. The second article in the editorial series “AI in Healthcare: Credibility, Safety, and Impact on Clinical Practice” demonstrates

Summary: Artificial intelligence is already a reality in healthcare. However, for it to be sustainably integrated into clinical practice, technological sophistication alone is not enough—it must also be trustworthy. In a field where inaccurate data can directly affect patient outcomes, credible clinical AI depends on factors that go far beyond algorithms. This article opens Epimed