{"title":"\"Electronic Pediatrician\", a non-machine learning prototype artificial intelligence software for pediatric computer-assisted pathophysiologic diagnosis - general presentation.","authors":"Andrei-Lucian Drăgoi, Roxana-Maria Nemeș","doi":"10.5662/wjm.v15.i3.100903","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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 <i>etc.</i>).</p><p><strong>Aim: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":94271,"journal":{"name":"World journal of methodology","volume":"15 3","pages":"100903"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11948191/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"World journal of methodology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5662/wjm.v15.i3.100903","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.