radiology-frontier

radiology-frontier is an Academic writing & papers skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-frontier C (71/100) and it passed the safety scan; the rating is based on static analysis only.

Find publishable frontier directions and innovation points for imaging-AI / radiomics / radiogenomics research, grounded in the publication patterns of high-impact journals (Radiology, Radiology: AI, Lancet Digital Health, Lancet Oncology, Nature Medicine, Nature Communications, npj Digital Medicine, npj Precision Oncology, eClinicalMedicine, Cell Reports Medicine). Use when the user asks for frontier directions, innovation points, hot vs suitable topics, "近三年前沿方向", "创新点", "what's novel in imaging AI", or wants to know the evidence/publication-pattern basis behind a recommendation ("有什么文献依据", "证据"). Translates trends (foundation models, self-supervised, vision-language, multimodal fusion, longitudinal, weak/federated learning, radiogenomics) into executable research questions matched to the user's actual data. Encodes publication-pattern heuristics, not a fabricated citation list — routes live verification to radiology-search and never invents PMIDs/DOIs.

Grade

C

Quality

71 / 100

Safety

Passed

GitHub stars

819

Unproven: rated by static analysis only, not yet compared head-to-head.

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