Evidence map›Paper›PMID 42115645›Full record

ArticleScientific reports2026

Developing an AI platform for voice-input medical records in Polish: a digital application for optimisation of physicians' workload.

Dariusz Szplit, Andrzej Czyżewski, Beata Graff, Anna Szyndler, Piotr Pałczyński, Mariusz Siemiński, Natalia Glaner, Tomasz Stefaniak, Julia Bogdan, Kinga Słomińska and 7 more

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In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Dariusz SzplitDepartment of Health Care Quality, Medical University of Gdansk, M. Skłodowskiej-Curie 3a, 80-210, Gdańsk, Poland. dariusz.szplit@gumed.edu.pl.
Andrzej CzyżewskiFaculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Gdańsk, Poland.
Beata GraffDepartment of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland.
Anna SzyndlerDepartment of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland.
Piotr PałczyńskiDepartment of Emergency Medicine, Medical University of Gdansk, Gdańsk, Poland.
Mariusz SiemińskiDepartment of Emergency Medicine, Medical University of Gdansk, Gdańsk, Poland.
Natalia GlanerStudent Scientific Circle of Emergency Medicine, Medical University of Gdansk, Gdańsk, Poland.
Tomasz StefaniakDepartment of Health Care Quality, Medical University of Gdansk, M. Skłodowskiej-Curie 3a, 80-210, Gdańsk, Poland.
Julia BogdanDepartment of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland.
Kinga SłomińskaDepartment of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland.
Anna DąbkowskaDepartment of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland.
Mariusz BudziszUniversity Clinical Center, Gdańsk, Poland.
Marta ZielonkaFaculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Gdańsk, Poland.
Daniel CieślakFaculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Gdańsk, Poland.
Szymon ZaporowskiFaculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Gdańsk, Poland.
Józef KotusFaculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Gdańsk, Poland.
Krzysztof NarkiewiczDepartment of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland.

Funding

Narodowe Centrum Badań i Rozwoju INFOSTRATEG4/0003/2022
6 · The paper itself

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.

Indexed as

Artificial IntelligenceElectronic Health RecordsPhysiciansSpeech Recognition SoftwareWorkloadHumansIntelligent SystemsPolandAI for real world data collectionAI in healthcareAI towards quality and safety of health careEHR improvement by AIMedical speech recognitionMedical text annotationPolish medical language

Identifiers

PMID42115645
PMCPMC13350868

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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.