Allied Journal of Medical Research

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Rapid Communication - Allied Journal of Medical Research (2022) Volume 6, Issue 11

Artificial intelligence's use in gastroenterology

Mohammed Jahangir*

Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, South Korea

*Corresponding Author:
Mohammed Jahangir
Department of Internal Medicine,
Hallym University College of Medicine,
Chuncheon, South Korea

Received: 26-Oct-2022, Manuscript No. AAAJMR-22-82525; Editor assigned: 28-Oct-2022, PreQC No. AAAJMR-22-82525(PQ); Reviewed: 14-Nov-2022, QC No. AAAJMR-22-82525; Revised: 16-Nov-2022, Manuscript No. AAAJMR-22-82525(R); Published: 23-Nov-2022, DOI:10.35841/ aaajmr-6.11.153

Citation: Jahangir M. Artificial intelligence's use in Gastroenterology. Allied J Med Res. 2022;6(11):153

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Artificial intelligence utilizing profound learning has arisen as a cutting edge PC innovation. By the time of large information, the gathering of a tremendous number of computerized pictures and clinical records drove the requirement for the usage of simulated intelligence to effectively manage this information, which has become key assets for a machine to advance without help from anyone else. Among a few DL models, the convolutional brain network showed exceptional execution in picture examination. Nonetheless, potential innate determination predisposition can't be rejected in that frame of mind of review study. Since over fitting and range predisposition have the chance of misjudging the precision, outside approval utilizing unused datasets for model turn of events, gathered in a way that limits the range predisposition, is obligatory. For powerful confirmation, planned investigations with satisfactory consideration/prohibition rules, which address the objective populaces, are required. DL has its own absence of interpretability. Since interpretability is significant in that it can give wellbeing measures, help to distinguish predisposition, and make social acknowledgment, further examinations ought to be performed.


Artificial intelligence, Convolutional neural network, Deep-learning, Gastroenterology, Endoscopy.


Recently, Artificial Intelligence (AI) using Deep-Learning (DL) has emerged as a breakthrough computer technology, and numerous research studies, using AI applications to identify or differentiate images in various medical fields including radiology, neurology, orthopedics, pathology, ophthalmology, and gastroenterology, have been published. However, AI, the display of intelligent behavior indistinguishable from that of a human being, was already mentioned in the 1950s. Although AI has waxed and waned over the past six decades with seemingly little improvement, it was constantly applied to the medical field using various models of Machine Learning (ML) including Bayesian inferences, decision trees, linear discriminants and Support Vector Machines (SVM), logistic regression, and artificial neural networks [1]. In the area of gastroenterology, doctors handle a lot of clinical information and different sorts of picture gadgets, for example, esophagogastroduodenoscopy, colonoscopy, container endoscopy, and ultrasound gear. Simulated intelligence has been applied in the area of gastroenterology while making a determination, foreseeing a guess, and examining pictures. Past examinations revealed surprising aftereffects of artificial intelligence in gastroenterology. The fast movement of simulated intelligence requests that gastroenterologists get familiar with the utility, qualities, and entanglements of simulated intelligence. Likewise, doctors ought to plan for the progressions and impacts of computer based intelligence on genuine clinical practice sooner rather than later. Subsequently, in this audit, we plan to momentarily present a ML innovation; sum up a computer based intelligence application in the area of gastroenterology, which is separated into two classes; and talk about the difficulties for the application and future headings of man-made intelligence [2].

Artificial intelligence in gastroenterology

Despite the fact that artificial intelligence in the area of gastroenterology as of late centred on picture examination, a few ML models have shown promising outcomes in the acknowledgment of determination and expectation of guess. The Artificial Neural Networks [ANN] are suitable for managing complex datasets to beat the disadvantages of conventional straight insights. Moreover, the ANN can represent the refined connections between segment, ecological, and clinical qualities [3].

Application of AI

Although many researchers have investigated the utility of AI and have shown promising results, most studies were designed in retrospective manner: as a case-control study from a single center, or by using endoscopic images that were chosen from specific endoscopic modalities unavailable from many institutions. Potential inherent bias such as selection bias cannot be excluded in this situation. Therefore, it is crucial to meticulously validate the performance of AI before the application of AI in real clinical practice. To properly verify the accuracy of AI, physicians should understand the effects of over fitting and spectrum bias (class imbalance) on the performance of AI, and try to evaluate the performance by avoiding these biases. In addition, datasets that were collected by case-control design are particularly vulnerable to spectrum bias. Spectrum bias occurs when the dataset used for model development does not adequately represent the range of patients who will be applied in clinical practice [4].


Since AI was introduced in the 1950s, it has been persistently challenged in terms of statistical or image analyses in the field of gastroenterology. Recent evaluation of big data and computer science enabled the dramatic development of AI technology, particularly DL, which showed promising potential. Now, there is no doubt that the implementation of AI in the gastroenterology field will progress in various healthcare services. To utilize AI wisely, physicians should make great effort to understand its feasibility and ameliorate the drawbacks through further investigation.


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