ReviewInnere Medizin (Heidelberg, Germany)2026
[Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].
Review in Innere Medizin (Heidelberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
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Abstract
backgroundArtificial intelligence (AI) is increasingly being implemented in digital pathology to support the tissue classification, cell detection, biomarker quantification, the grading and prediction of clinically relevant molecular alterations.
objectiveCurrent applications of AI in the pathology of internal and oncological diseases are summarized with a focus on the most important algorithm approaches, representative cases of diagnostic applications and current limits of clinical implementation. MATERIAL AND
methodsThis narrative overview of the most recent advances describes the essential model forms, including tissue segmentation systems, algorithms for recognition of individual cells, unsupervised learning and foundation models, tools for the evaluation of immunohistochemistry and multimodal or language-based applications. Representative studies on renal, liver, pulmonary, gastrointestinal, hematological and thyroid gland pathologies are discussed.
resultsIn many situations AI improves the reproducibility, objectiveness and efficiency. Some systems achieve an accuracy that is comparable to that of experts. Most AI tools are still in the validation stage and only a few have been transferred to routine clinical use. DISCUSSION: Artificial intelligence could further optimize the diagnostic and predictive histopathology. Their role remains assistive. A broad implementation is limited due to various hurdles and bottlenecks. The future progress depends on prospective validation and the integration into routine digital workflows.
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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.