In recent years, the incidence of thyroid cancer has risen significantly, making it the third most common malignant tumor among women in China. With the rapid development of artificial intelligence technology, the diagnosis and treatment models of thyroid nodules are undergoing profound changes. By efficiently processing massive amounts of data, providing precise analysis and personalized suggestions, artificial intelligence has significantly promoted the integrated development of thyroid cancer in preoperative diagnosis, surgical navigation, surgical methods and postoperative management, thus facilitating the realization of “precision medicine and smart medicine” in thyroid surgery. This article focuses on the current development status of the “internet+thyroid” model and looks forward to its future development.
Against the backdrop of the National Health Commission incorporating the “smart medical emergency care system construction” into the Guidelines for the Construction and Management of Critical Maternal and Fetal Care Centers, leveraging smart medical technologies such as artificial intelligence and 5G to advance the “pre-hospital to in-hospital integrated” emergency care system for critical cases has become a trend. Based on this, a technical specification for smart healthcare in pre-hospital emergency of emergency obstetrics and gynecology, including 5G and artificial intelligence, has been developed to clarify the standardized process, risk prevention and control, and multidisciplinary collaboration mechanism for pre-hospital emergency care and transportation of critically ill obstetrics and gynecology patients. This will shorten emergency response time, improve rescue efficiency, reduce maternal and infant mortality rates, and improve prognosis, which is crucial for enhancing regional rescue capabilities and ensuring maternal and infant safety.
This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.
ObjectiveTo explore the teaching model of the "LungSmart" smart healthcare system in clinical pulmonology teaching and its effectiveness in enhancing the clinical reasoning skills of postgraduate students. MethodsA single-center, single-group pretest-posttest educational intervention study was conducted among 30 postgraduate students who participated in respiratory medicine-related teaching activities and enrolled in the "LungSmart" smart healthcare course at Shanghai Pulmonary Hospital from 2024 to 2025 academic year. The course was structured around three core components, namely an AI case repository, dynamic simulation, and immediate feedback, and was delivered over 16 weeks with a total of 64 class hours. Teaching effectiveness was assessed using pretest and posttest clinical reasoning ability scores, while students’ acceptance of the course was evaluated using a 5-point Likert questionnaire. ResultsAll 30 students completed the teaching activities and were included in the final analysis. The pretest score was (78.83±6.25) points, and the posttest score increased to (93.50±4.18) points, with a mean improvement of (14.67±7.06) points (95%CI, 12.03 to 17.30), indicating a statistically significant improvement after the intervention (t=11.37, P<0.001). A total of 30 valid questionnaires were collected at the end of the course, with a response rate of 100.0%. The overall satisfaction score was (4.67±0.15) points, and the mean scores for content satisfaction, practical value, interest stimulation, and professional competence were (4.72±0.23) points, (4.53±0.35) points, (4.72±0.39) points, and (4.75±0.43) points, respectively. ConclusionThese findings suggest that the clinical teaching model based on the "LungSmart" smart healthcare system is feasible and well accepted, and may help improve postgraduate students’ clinical reasoning ability.
