Medical University of Białystok

The Role of 3D Printing in Planning Complex Medical Procedures and Training of Medical Professionals—Cross-Sectional Multispecialty Review

Jarosław Meyer-Szary, Marlon Souza Luis, Szymon Mikulski et al.

2022 · 197 Zit.

Pathological changes or technical artefacts? The problem of the heterogenous databases in COVID-19 CXR image analysis

Marek Socha, Wojciech Prażuch, Aleksandra Suwalska et al.

2023 · 6 Zit.

A multi-center study of a machine learning algorithm for identifying undiagnosed patients with myelodysplastic syndrome based on complete blood count data

Anna Lason, Arkadiusz Sycz, Karol Lis et al.

2025 · 1 Zit.

Digital Twins for Predictive Modelling of Thrombosis and Stroke Risk: Current Approaches and Future Directions

Adelaide de Vecchi, Oscar Camara, R Cavarra et al.

2026 · 1 Zit.

Large Language Models as Patient Education Tools in Hypothyroidism: A Cross-Sectional Analysis of Dietary, Pharmacological, and Safety Recommendations

Patryk Hebda, Mateusz Kubicki, Andrii Bilyk et al.

2026 · 0 Zit.

Prediction models for incident stroke in the community: a systematic review and meta-analysis of predictive performance

Mohammad Haris, Elizabeth Romer, Tanina Younsi et al.

2025 · 0 Zit.

AI-powered precision: Unmasking hidden treatment optimizations for lower-risk myelodysplastic syndrome patients based on real-world, multi center EHR data.

Marek Dudziński, Anna Lason, Arkadiusz Sycz et al.

2025 · 0 Zit.

Artificial intelligence in forensic medicine and related sciences – selected issues = Sztuczna inteligencja w medycynie sądowej i naukach pokrewnych – wybrane zagadnienia

Michał Szeremeta, Julia Janica, Anna Niemcunowicz‐Janica

2024 · 0 Zit.

CIRCA: comprehensible online system in support of chest X-rays-based screening by COVID-19 example

Wojciech Prażuch, Aleksandra Suwalska, Marek Socha et al.

2025 · 0 Zit.

Clinically Actionable Explainable AI in Pulmonary Arterial Hypertension: Endpoints, Calibration, and External Validation. Reply to Pagnoni et al. Toward Clinically Actionable Explainable AI in Pulmonary Arterial Hypertension: Endpoints, Calibration, and External Validation. Comment on “Ledziński et al. Personalized Medicine in Pulmonary Arterial Hypertension: Utilizing Artificial Intelligence for Death Prevention. J. Clin. Med. 2025, 14, 8325”

Łukasz Ledziński, Grzegorz Grześk, Michał Ziołkowski et al.

2026 · 0 Zit.

Prediction of major outcomes in patients with malignant hypertension using machine learning: A report from the West Birmingham malignant hypertension registry

Antonis Argyris, Hironori Ishiguchi, Yang Chen et al.

2025 · 0 Zit.