Objective To summarize the classic and latest treatment techniques for localized knee cartilage lesions in clinical practice and create a new comprehensive clinical decision-making process. Methods The advantages and limitations of various treatment methods for localized knee cartilage lesions were summarized by extensive review of relevant literature at home and abroad in recent years. Results Currently, there are various surgical methods for treating localized knee cartilage injuries in clinical practice, each with its own pros and cons. For patients with cartilage injuries less than 2 cm2 and 2-4 cm2 with bone loss are recommended to undergo osteochondral autograft (OAT) and osteochondral allograft (OCA) surgeries. For patients with cartilage injuries less than 2 cm2 and 2-4 cm2 without bone loss had treatment options including bone marrow-based techniques (micro-fracture and ogous matrix induced chondrogenesis), autologous chondrocyte implantation (ACI)/matrix-induced ACI, particulated juvenile allograft cartilage (PJAC), OAT, and OCA. For patients with cartilage injuries larger than 4 cm2 with bone loss were recommended to undergo OCA. For patients with cartilage injuries larger than 4 cm2 without bone loss, treatment options included ACI/matrix-induced ACI, OAT, and PJAC. Conclusion There are many treatment techniques available for localized knee cartilage lesions. Treatment strategy selection should be based on the size and location of the lesion, the extent of involvement of the subchondral bone, and the level of evidence supporting each technique in the literature.
Artificial intelligence (AI) is reshaping evidence-based clinical decision-making. From the perspective of clinical decision-making, this paper explores the collaborative value of AI in life-cycle health management. While AI can enhance early disease screening efficiency (e.g., medical image analysis) and assist clinical decision-making through personalized health recommendations, its reliance on non-specialized data necessitates the development of dedicated AI systems grounded in high-quality, specialty-specific evidence. AI should serve as an auxiliary tool to evidence-based clinical decision-making, with physicians’ comprehensive judgment and humanistic care remaining central to medical decision-making. Clinicians must improve the reliability of decision making through refining prompt design and cross-validating AI outputs, while actively participate in AI tool optimization and ethical standard development. Future efforts should focus on creating specialty-specific AI tools based on high-quality evidence, establishing dynamic guideline update systems, and formulating medical ethical standards to position AI as a collaborative partner for physicians in implementing life-cycle health management.