@article{scholars17786,
            note = {cited By 6},
          volume = {29},
          number = {1},
             doi = {10.2174/0929867328666210405114938},
           title = {Recent Applications of Artificial Intelligence in the Detection of Gastrointestinal, Hepatic and Pancreatic Diseases},
            year = {2022},
         journal = {Current Medicinal Chemistry},
       publisher = {Bentham Science Publishers},
           pages = {66--85},
            issn = {09298673},
          author = {Kumar, R. and Khan, F. U. and Sharma, A. and Aziz, I. B. A. and Poddar, N. K.},
        keywords = {artificial intelligence; capsule endoscopy; clustering algorithm; decision support system; electronic medical record; gastrointestinal disease; human; intestinal bleeding; lesions and defects; liver disease; pancreas disease; predictive model; prognosis; Review; risk assessment; supervised machine learning; algorithm; artificial intelligence; gastroenterology, Algorithms; Artificial Intelligence; Gastroenterology; Gastrointestinal Diseases; Humans; Pancreatic Diseases},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122367202&doi=10.2174\%2f0929867328666210405114938&partnerID=40&md5=5d90a6275dd81a9884a0bdb682082b86},
        abstract = {There has been substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remote health monitoring using sensors and smartphones. A variety of AI-based prediction models are available for gastrointestinal, inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, hepatitis-associated fibrosis using electronic medical records, and pancreatic carcinoma utilizing endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patients{\^a}?? treatment employing multiple factors. Enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI-based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitations of AI techniques in such diseases{\^a}?? prognosis, risk assessment, and decision support are discussed. {\^A}{\copyright} 2022 Bentham Science Publishers.}
}