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publications
Evaluation of the performance of GPT-3.5 and GPT-4 on the Polish Medical Final Examination
Published in Scientific Reports, 2023
The study aimed to evaluate the performance of two Large Language Models (LLMs): ChatGPT (based on GPT-3.5) and GPT-4 with two temperature parameter values, on the Polish Medical Final Examination (MFE).
Recommended citation: Rosoł, M., Gąsior, J.S., Łaba, J. et al. Evaluation of the performance of GPT-3.5 and GPT-4 on the Polish Medical Final Examination. Sci Rep 13, 20512 (2023). https://doi.org/10.1038/s41598-023-46995-z
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Determination of anal sphincter injury location using impedance spectroscopy in obstetric patients
Published in IEEE, 2024
This study aimed to evaluate the of machine learning models in lolaziation of obstetric anal sphincter injuries.
Recommended citation: Młyńczak M, Rosoł M, Korzeniewski K, i in. Determination of anal sphincter injury location using impedance spectroscopy in obstetric patients. W: Proceedings of the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2024). Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Institute of Electrical and Electronics Engineers; 2024:1–4. doi:10.1109/embc53108.2024.10782955
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Prediction of the duration of maximal exercise test in professional adolescent football players based on the cardiorespiratory signals – a pilot study
Published in IEEE, 2024
This pilot study aimed to evaluate the potential of machine learning models utilizing parameters estimated from cardiorespiratory signals obtained during rest in predicting the duration of maximal cardiopulmonary exercise tests (CPET) in professional adolescent football players
Recommended citation: Rosoł, M., Gąsior, J. S., Korzeniewski, K., Łaba, J., Makuch, R., & Młyńczak, M. (2024, July). Prediction of the duration of maximal exercise test in professional adolescent football players based on the cardiorespiratory signals–a pilot study. In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1-4). IEEE.
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Exploration of Heart Rate Variability for the Prediction of Performance in Youth Footballers”
Published in Computing in Cardiology conference, 2024
The study was to explore prediction quality of resting short-term heart rate variability (HRV) parameters with endurance performance in youth footballers.
Recommended citation: Korzeniewski, K., Gąsior, J. S., Mikielewicz, M., Rosoł, M., Makuch, R., & Młyńczak, M. (2024). Exploration of Heart Rate Variability for the Prediction of Performance in Youth Footballers. Age, 13, 2-3.
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Correlation of Early Postpartum Anal/Faecal Incontinence with Endoanal Ultrasound, Digital Rectal Examination, Machine Learning-supported Electric Impedance Spectroscopy, and High-Resolution Anorectal Manometry Results: Exploratory Results from a Prospective, Comparative, Multicentre Clinical Study
Published in ICS 2024 Madrid Abstracts, 2024
The study aimed to evaluate the clinical performance and safety of the ONIRY system for obstetric anal sphincter injuries (OASI) detection versus three-dimensional endoanal ultrasound (EAUS).
Recommended citation: FERNANDO R, HERMAN H, GONZALEZ DIAZ E, i in. 307 - Correlation of Early Postpartum Anal/Faecal Incontinence with Endoanal Ultrasound, Digital Rectal Examination, Machine Learning-supported Electric Impedance Spectroscopy, and High-Resolution Anorectal Manometry Results: Exploratory Results from a Prospective, Comparative, Multicentre Clinical Study. Continence. 2024;12:101649. doi:10.1016/j.cont.2024.101649
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Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied — An Exploratory Study
Published in JCM, 2024
This study aimed to evaluate the accuracy of machine learning (ML) techniques in classifying pediatric individuals—cardiological patients, healthy participants, and athletes—based on cardiorespiratory features from short-term static measurements
Recommended citation: Rosoł M, Gąsior J, Korzeniewski K, i in. Machine Learning Classification of Pediatric Health Status Based on Cardiorespiratory Signals with Causal and Information Domain Features Applied—An Exploratory Study. Journal of Clinical Medicine. 2024;13:1–28. doi:10.3390/jcm13237353
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Detection of obstetric anal sphincter injuries using machine learning-assisted impedance spectroscopy: a prospective, comparative, multicentre clinical study
Published in Scientific Reports, 2025
The study aimed to evaluate the clinical performance and safety of the ONIRY system for obstetric anal sphincter injuries (OASI) detection versus three-dimensional endoanal ultrasound (EAUS).
Recommended citation: Borycka, K., Młyńczak, M., Rosoł, M. et al. Detection of obstetric anal sphincter injuries using machine learning-assisted impedance spectroscopy: a prospective, comparative, multicentre clinical study. Sci Rep 15, 7522 (2025). https://doi.org/10.1038/s41598-025-92392-z
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talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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