Expanding the applications of artificial intelligence in emergency radiology: Advancing precision medicine and resource efficiency

Table 1 Challenges and relevant systematic solutions

Challenge dimensions	Core issues	Strategic response plan
Data privacy and security	Patient imaging and medical record data are highly sensitive	By adopting federated learning technology, we achieve collaborative training with the principle of ‘data stays local while models move’; hospital-based servers are utilized to ensure data remains within the hospital
Model interpretability and clinical trust	“Black box” decision-making is difficult for physicians to accept	The system needs to provide visual evidence (e.g., heat map) and a confidence score to establish a human-machine collaborative golden veto mechanism
Workflow integration and ethical responsibilities	The responsibility for adverse consequences caused by algorithmic errors is ambiguously defined	The legal framework must clearly define the role of AI as an auxiliary tool, with all diagnostic reports ultimately requiring review and signature by licensed physicians; all AI decision pathways must be blockchain-verified for traceability and auditing purposes

AI: Artificial intelligence.