Every year, over 1.5 million scientific articles are added to a global collection of 40 million references. That’s more than 4,000 new articles each day, with a new one published roughly every 21 seconds. Keeping up with this volume is nearly impossible for doctors and healthcare professionals. A study in the Journal of the Royal Society of Medicine suggests that it takes about 17 years for new medical knowledge to be fully integrated into clinical practice. This delay means that patients might receive outdated or incomplete information, which can affect diagnosis, treatment, and overall care quality. The gap between the creation of medical knowledge and its practical use has both human and economic consequences. Delayed or poorly integrated information can lead to longer diagnostic periods, unnecessary tests, and treatment decisions that are not fully informed. In a healthcare system already under pressure, the slow flow of knowledge is a critical issue. This is where artificial intelligence (AI) is increasingly seen as a valuable tool. However, the public debate often focuses on a misleading question: will AI replace doctors? A more relevant question is how to make the latest medical knowledge accessible to healthcare professionals exactly when they need it—within seconds, not years. AI’s role in healthcare is growing rapidly. In the United States, 81% of doctors reported using AI in their professional work in 2026, up from 38% just three years earlier. In France, AI tools are often used on the fringes of existing systems, with many designed for the general public rather than clinical settings. While refusing these tools might seem like a way to avoid risks, it often makes their use invisible and harder to regulate. The real challenge is not just to allow or prohibit AI, but to organize its use effectively. To do this, four key requirements have been identified. First, traceability: medical AI must provide verifiable sources for its information, with clear references to studies, dates, and levels of evidence. Second, responsibility: AI should support, not replace, doctors. The final decision must remain with the practitioner, ensuring trust and accountability. Third, evaluation: AI systems should be judged based on real-world clinical outcomes, such as reduced diagnostic delays and improved efficiency, rather than academic benchmarks. Finally, making uncertainty clear: AI must recognize and communicate the limitations of available evidence, helping doctors make informed choices. With these principles, AI can become a powerful, transparent, and reliable tool in healthcare.