ArticlePatterns (New York, N.Y.)2025
S2S: A deep learning method for the radiological diagnosis of fine-grained diseases spanning screening to subtyping.
Article in Patterns (New York, N.Y.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- From screening to subtyping in a single glance.Patterns (New York, N.Y.) · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiographic images play a critical role in disease diagnosis, but accurately interpreting them requires considerable expertise and workload. Recent research has advanced artificial intelligence-based medical image analysis, but such advancements remain limited in real clinical practice where multistage diagnosis is required for fine-grained diseases. This study proposes a screening-to-subtyping (S2S) AI paradigm specifically designed for accurate radiological diagnosis of fine-grained diseases, encompassing the entire diagnostic process from initial screening to final subtyping. The S2S framework integrates information from multiple diagnostic phases, radiological viewpoints, lesion dimensions, and imaging modalities to address complex diagnostic challenges. Evaluation using a large-scale, multi-center radiography dataset of fine-grained thoracic cancer subtypes demonstrates the system's robust performance. Furthermore, this investigation offers novel insights into human-AI collaboration for diagnosing intricate fine-grained pathologies. Our results highlight the substantial clinical potential of S2S AI across varied healthcare environments and disease entities, facilitating deeper integration of artificial intelligence in radiological diagnostics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.