Summary of Key Points
Artificial Intelligence (AI) has reached a mature stage in the clinical healthcare sector. Nearly half (49%) of healthcare professionals worldwide are already using AI tools, with even higher adoption rates in China (56%), particularly among doctors (72%). The industry's focus has shifted from questioning whether to use AI to how to create credible and verifiable clinical value. Trust is currently the biggest barrier; only 37% of healthcare professionals globally trust AI, while this figure is slightly higher at 49% in China. Most healthcare professionals believe that AI will not replace them but rather serve as a valuable assistant in clinical decision-making. To be successfully implemented, AI must overcome four key challenges: regulatory compliance, performance evaluation, validation, and practical application, with an emphasis on both scientific research value and real-world effectiveness.
Detailed Analysis
1. AI Clinical Applications Have Matured: From “Whether to Use” to “How to Use It Effectively”
AI is no longer a novelty but has become an integral part of healthcare professionals' daily work. Globally, 49% of healthcare professionals are using AI, and China leads the way with 56% usage, including 72% of doctors (compared to an average of 57% globally).
More importantly, the adoption of specialized AI tools for clinical decision-making and case analysis is accelerating. In 2025, only 22% of users would regularly use such tools; now that figure has risen to 34%. These specialized tools are more reliable than general-purpose AI (like ChatGPT) because they can be directly applied in critical scenarios such as providing second opinions on complex cases and patient monitoring, earning them higher ratings for practicality and safety.
The industry's focus has shifted from the question of whether to use AI to how to utilize it to generate genuine clinical value—specifically, whether the AI’s recommendations are traceable and in line with medical standards.
2. Trust as a Barrier to AI Clinical Adoption
Despite the increasing use of AI, trust levels remain low. Only 37% of healthcare professionals globally trust current AI tools, although this figure is slightly higher at 49% in China. The main concerns include ease of use, the completeness and reliability of information, safety, transparency of decision-making processes, and the quality of data.
China has made significant progress in building an AI ecosystem: 57% of healthcare professionals are satisfied with their institution's AI governance (e.g., policies for responsible use), 54% with the availability of digital tools, and 48% with training programs (global averages are 40%, 41%, and 32%, respectively). However, Chinese professionals place particular emphasis on the reliability of data, indicating a growing demand for evidence-based medical support for AI-generated information.
3. AI as an Assistant, Not a Replacement
80% of healthcare professionals believe that AI will not replace them in the next 5-10 years but rather serve as a valuable aid in clinical decision-making and patient care. For example, AI can help analyze images and organize medical records, freeing doctors to focus on patient communication. However, final diagnoses and treatment plans will still be made by humans.
Moreover, AI skills are expected to become essential for future healthcare professionals. 79% believe that medical professionals must master AI tools, and 58% think AI can improve the quality of care. Although expectations for time-saving and faster diagnosis have declined compared to 2025, there is a greater emphasis on practical benefits rather than merely theoretical potential.
4. The Key to Successful AI Implementation in Clinics: Overcoming Four Challenges
For AI to be integrated into hospital workflows, four issues need to be addressed: regulatory compliance, performance evaluation, validation of clinical effectiveness, and practical application. Hospitals often choose AI projects based on either cutting-edge research (to lead industry trends) or practical problem-solving (to improve efficiency and revenue).
For instance, a hospital used AI for patient screening, helping to match patients with the right specialists or departments, resulting in benefits for over 10,000 patients and an increase in revenue of about 100 million yuan—this demonstrates the practical value of AI.
In Conclusion
AI in healthcare has moved from a experimental phase to a practical one. To gain doctors' trust and provide real benefits to patients, it is crucial to address trust concerns and ensure that technology is applied in contexts that create tangible value. AI is not meant to replace jobs but to enhance the work of healthcare professionals by providing valuable assistance.