With the rapid advancement of deep learning, computer vision and clinical large language models, artificial intelligence-assisted diagnosis has evolved from research exploration to clinical implementation. It serves as a vital tool to improve the efficiency of early disease screening, balance medical resources and optimize clinical workflows. Essentially, AI-assisted diagnosis acts as a clinical decision support tool for physicians. All outputs generated by the system are for reference only, and licensed practitioners bear the final responsibility for disease diagnosis and medical treatment.
Medical imaging represents the most mature application field of AI-assisted diagnosis. Leveraging computer vision algorithms, AI can automatically detect lesions, perform segmentation, quantitative measurement and risk stratification on CT scans, X-rays, MRI, fundus photographs and digital pathological slices. In chest CT screening, AI rapidly identifies pulmonary nodules, automatically measures their size and density, and assesses the probability of malignancy. It greatly reduces radiologists’ reading workload and improves the detection rate of tiny nodules. For acute stroke, head CT AI can recognize intracranial hemorrhage within seconds, enabling emergency physicians to seize the golden time window for thrombolysis and thrombectomy. Fundus imaging AI is widely deployed in population screening for diabetic retinopathy and glaucoma, suitable for large-scale physical examinations at primary healthcare institutions. Digital pathology AI analyzes whole-slide images to assist tumor classification and Gleason scoring, minimizing subjective discrepancies in manual slide review. AI systems for mammography, fracture X-ray, dermoscopy and endoscopic imaging have also obtained regulatory approvals, delivering value in early tumor screening and rapid emergency evaluation.
Beyond imaging, clinical decision support systems (CDSS) based on natural language processing are gradually integrated into electronic medical records. AI automatically extracts chief complaints, present illness, past medical history and laboratory results to structure medical records. It supports pre-consultation and differential diagnosis recommendation, and realizes pre-prescription review with real-time alerts for drug allergies, incompatibility and excessive dosage. ECG AI interprets electrocardiograms automatically to identify acute myocardial infarction and various arrhythmias. Multimodal large models further integrate imaging, laboratory tests, medical records and genetic data to provide comprehensive diagnostic clues for complex and rare diseases, compensating for insufficient experience among primary care physicians. In intensive care units, time-series prediction models continuously monitor vital signs to warn of clinical deterioration such as sepsis and acute organ injury, facilitating early intervention.
AI-assisted diagnosis still has notable limitations in clinical practice. Deep learning models suffer from the black-box problem, making it difficult to clearly interpret the reasoning behind diagnostic conclusions and hindering clinical traceability. Model performance heavily relies on training datasets. False positives and false negatives tend to occur when encountering rare diseases, atypical cases and images acquired with different equipment parameters. Medical large models also carry risks of medical hallucinations. AI cannot conduct physical examinations or fully evaluate patients by incorporating social background and psychological status. Meanwhile, data privacy, distribution shift across multi-center datasets, limited generalization of models among different hospitals, together with product registration, quality control and definition of clinical liability, restrict large-scale promotion. Domestic regulations stipulate that AI-assisted imaging products for clinical use must obtain Class III medical device registration certificates; uncertified systems are only permitted for research purposes.
Overall, AI-assisted diagnosis is not intended to replace physicians. It focuses on repetitive, standardized screening tasks and amplifies clinicians’ diagnostic capacity. Future directions include improving model interpretability, adopting federated learning to protect patient privacy, building multimodal fusion models, and perfecting clinical quality control systems and evaluation criteria. Within the tiered medical service system, AI will continue to extend its reach to primary care, narrow gaps in diagnosis and treatment capacity across regions, promote early detection and intervention of diseases, and facilitate precision medicine and health management.
