Dtsch Arztebl Int
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Increasing digitalization enables the use of artificial intelligence (AI) and machine learning in pathology. However, these technologies have only just begun to be implemented, and no randomized prospective trials have yet shown a benefit of AI-based diagnosis. In this review, we present current concepts, illustrate them with examples from representative publications, and discuss the possibilities and limitations of their use. ⋯ Initial proof-of-concept studies for AI in pathology are now available. Randomized, prospective studies are now needed so that these early findings can be confirmed or falsified.
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Approximately half of all patients with tumors need radiotherapy. Long-term survivors may suffer from late sequelae of the treatment. The existing radiotherapeutic techniques are being refined so that radiation can be applied more precisely, with the goal of limiting the radiation exposure of normal tissue and reducing late sequelae. ⋯ Special challenges for research in this field arise from the long latency of radiation sequelae and the need for largescale, well-documented patient collectives in order to discern dose-effect relationships, and take account of cofactors, when the overall number of events is small. It is hoped that further technical and conceptual advances will be made in the areas of adaptive radiotherapy, proton and heavy-ion therapy, and personalized therapy.