Abstract In a scientific and implementation consortium, we developed an adaptive AI platform that enables doctors to create accurate and comprehensive Electronic Health Records (EHRs) through advanced speech recognition and context analysis tailored to Polish medical language. This system ensures stability with consistent performance across real-world clinical settings, achieving expected values for speech and context recognition during extensive testing. Its robustness is demonstrated by handling diverse inputs—such as regional accents, complex terminology, and noisy environments—supported by error-correction mechanisms and a specialized acoustic probe. Sustainability is achieved through seamless integration with existing healthcare infrastructures, scalable design, and ongoing updates to medical dictionaries, facilitating long-term use and adaptation. Structured data from electronic health records (EHRs) supports scientific research based on Real-World Data (RWD), verified by medical specialists using evidence-based medicine (EBM). The platform covers 10 clinical situations. The applied method was illustrated using one situation—a breast X-ray examination—employing clinically approved structures and real-world validation. Approved by the Bioethics Committee, the system is currently being tested at the hospital, marking a significant step toward efficient, reliable, and sustainable healthcare documentation.
Szplit et al. (Mon,) studied this question.