Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Methodol. Sep 20, 2025; 15(3): 100903
Published online Sep 20, 2025. doi: 10.5662/wjm.v15.i3.100903
“Electronic Pediatrician”, a non-machine learning prototype artificial intelligence software for pediatric computer-assisted pathophysiologic diagnosis ― general presentation
Andrei-Lucian Drăgoi, Roxana-Maria Nemeș
Andrei-Lucian Drăgoi, Roxana-Maria Nemeș, Medical Doctoral School of University "Titu Maiorescu", Bucharest 040051, Romania
Andrei-Lucian Drăgoi, The Emergency County Hospital Târgoviște (SJUT), Dambovita 130095, Târgoviște, Romania
Author contributions: Drăgoi AL designed, analyzed, interpreted, and prepared this paper; Nemeș RM has initially reviewed this paper and suggested important improvements in its form.
Institutional review board statement: It is not the case for this paper.
Informed consent statement: Patients were not additionally required to give informed consent to this study of the EPed software, because the analysis accomplished by EPed used anonymous data that were obtained after each parent of each admitted child-patient (diagnosed and treated in the Pediatric Infectious Diseases Ward of our hospital) agreed to all labs, imagistic and treatment interventions on his/her child by written consent (which consent was part of each medical folder in part).
Conflict-of-interest statement: The authors of this paper declare no existing competing interests.
Data sharing statement: No additional data.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Andrei-Lucian Drăgoi, MD, Doctor, Researcher, Medical Doctoral School of University "Titu Maiorescu", Str. General I. E. Florescu nr. 9, Bl. D11, Sc. B, Ap. 9, Bucharest 040051, Romania. dr.dragoi@yahoo.com
Received: September 6, 2024
Revised: October 24, 2024
Accepted: November 25, 2024
Published online: September 20, 2025
Processing time: 181 Days and 14.4 Hours
Abstract
BACKGROUND

Knowledge-based systems (KBS) are software applications based on a knowledge database and an inference engine. Various experimental KBS for computer-assisted medical diagnosis and treatment were started to be used since 70s (VisualDx, GIDEON, DXPlain, CADUCEUS, Internist-I, Mycin etc.).

AIM

To present in detail the “Electronic Pediatrician (EPed)”, a medical non-machine learning artificial intelligence (nml-AI) KBS in its prototype version created by the corresponding author (with database written in Romanian) that offers a physiopathology-based differential and positive diagnosis and treatment of ill children.

METHODS

EPed specifically focuses on the physiopathological reasoning of pediatric clinical cases. EPed has currently reached its prototype version 2.0, being able to diagnose 302 physiopathological macro-links (briefly named “clusters”) and 269 pediatric diseases: Some examples of diagnosis and a previous testing of EPed on a group of 34 patients are also presented in this paper.

RESULTS

The prototype EPed can currently diagnose 269 pediatric infectious and non-infectious diseases (based on 302 clusters), including the most frequent respiratory/digestive/renal/central nervous system infections, but also many other non-infectious pediatric diseases like autoimmune, oncological, genetical diseases and even intoxications, plus some important surgical pathologies.

CONCLUSION

EPed is the first and only physiopathology-based nml-AI KBS focused on general pediatrics and is the first and only pediatric Romanian KBS addressed to medical professionals. Furthermore, EPed is the first and only nml-AI KBS that offers not only both a physiopathology-based differential and positive disease diagnosis, but also identifies possible physiopathological “clusters” that may explain the signs and symptoms of any child-patient and may help treating that patient physiopathologically (until a final diagnosis is found), thus encouraging and developing the physiopathological reasoning of any clinician.

Keywords: Knowledge-based systems; Computer-assisted medical diagnosis; Non-machine learning artificial intelligence; DXPlain; General pediatrics; “Electronic Pediatrician” software

Core Tip: Electronic Pediatrician (EPed) is the first and only physiopathology-based non-machine learning artificial intelligence (nml-AI) knowledge-based system (KBS) focused on general pediatrics and is the first and only Romanian pediatric KBS addressed to medical professionals. Furthermore, EPed is the first and only nml-AI KBS that offers not only both a physiopathology-based differential and positive disease diagnosis, but also identifies possible physiopathological “clusters” that may explain the signs and symptoms of any child-patient and may help treating that patient physiopathologically (until a final diagnosis is found), thus encouraging and developing the physiopathological reasoning of any clinician.