Experimental Acupuncture and Moxibustion, as an interdisciplinary course, integrates traditional acupuncture and moxibustion with modern science. It is an important way for cultivating students' scientific thinking and innovation. The teaching group of Experimental Acupuncture and Moxibustion in Shandong university of TCM developed the content of courses under the guidance of talent construction of "emerging medicine discipline", aiming to cultivate the talents for the upright and innovation of acupuncture and moxibustion, and being abide to the advanced teaching concepts of students' cognitive law and outcome based education. Base on teaching design, teaching mode, curriculum ideological and political development, and teaching evaluation, a series of new measures have been implemented, e.g. "two-line, three-stage and eight-step measures", "six dimensions and five combinations" and "five combinations and three measure gauges". Through practice and exploration, the students' satisfaction, the innovation and creativity, and teaching ability of teachers have been enhanced. These reforms could provide the new approach to the development of the first-class courses related to traditional Chinese medicine.
Moxibustion/standards* ; Humans ; Acupuncture/education* ; Curriculum ; Acupuncture Therapy/methods* ; Medicine, Chinese Traditional ; China ; Teaching

Artificial intelligence (AI) is an emerging science and technology that studies and develops theories, methods, technologies, and application systems for simulating and expanding human intelligence. AI has made great breakthroughs in the field of intelligent medicine, and has shown great potential in the diagnosis and treatment of diabetic retinopathy (DR), retinopathy of prematurity, and other fundus diseases. A number of clinical trials on the application of AI technologies to DR screening have been carried out in the domestic and overseas, which not only have a high accuracy rate, but also save doctors' reading time and reduce the burden of society, medical work and patients. However, due to the lack of evaluation system for DR intelligent diagnosis technology, the accuracy of AI system still lacks of big data verification. Secondly, most of the color fundus photographs are taken in the posterior 45°, which only show the most vulnerable areas, making some lesions undetectable. In addition, the current DR screening system has not yet been applied to the clinic, most of which are in the stage of prospective research and trials. There are still many obstacles from the environment to the hospital or the clinic. Doctors cannot use real patient data to evaluate the AI system, so it is not popular in clinical practice. In the future, DR screening algorithms and diagnostic models can be further improved and established to make DR AI screening more accurate.