KI-gestützte klinische Entscheidungsunterstützungssysteme in der (gynäkologischen) Präzisionsonkologie

Over the past two decades, oncological treatment approaches have evolved from a generalized treatment paradigm to an individualized treatment concept. Despite substantial progress, major challenges persist particularly in managing rare tumor entities (24% of all cancers) and heavily pretreated patients with complex resistance mechanisms. Increasingly more patients benefit from extended molecular pathological diagnostics. These data are interpreted by molecular tumor boards (MTB) and individually tailored treatment plans are developed. The process of translating genomic data into clinical treatment plans is complicated. The implementation necessitates substantial effort: approximately 80% are implemented in off-label use. Fragmented data and manual curation of data collectively hinder the broader implementation of MTBs in clinical practice. Artificial intelligence (AI)-assisted decision support systems can analyze large datasets and identify clinically relevant patterns. While AI is able to access annotated medical images in dermatology, pathology and radiology, unstructured clinical text data from personalized medicine are difficult to process. Moreover, the lack of standardized precision oncological recommendations has further constrained the integration of AI technologies by machine learning into the MTB workflow. The domain-specific AI system MEREDITH, which employs a retrieval-augmented generation architecture, seeks to address these limitations. A proof of concept study showed that MEREDITH exhibits strong concordance with expert clinical recommendations but further evaluation studies are needed to validate the clinical utility of MEREDITH in real-world MTB practice. The Model Project Genome Sequencing is anticipated to further increase the complexity of oncological care, emphasizing the need for the integration of innovative technologies.