Advancing Gastrointestinal Disease Detection through Artificial Intelligence: A Comprehensive Analysis

Authors

  • Rakesh Sharma Manipal University Jaipur, India
  • C. S. Lamba Manipal University Jaipur, India

Keywords:

gastrointestinal, exploration, revolutionizing, paramount, efficacy

Abstract

In the quest to enhance the precision of gastrointestinal disease detection, Artificial Intelligence (AI) emerges as a beacon of hope, offering new perspectives in a field where accuracy can mean the difference between life and death. This study delves into the transformative role of AI in diagnosing gastrointestinal ailments, a domain where traditional methods often grapple with challenges of accuracy and early detection. With gastrointestinal disorders affecting a significant portion of the global population and being a leading cause of mortality and morbidity, the urgency for more efficient diagnostic tools is paramount. Recent advancements in AI, particularly in deep learning, have shown promising results in interpreting complex medical images, a task that has historically been reliant on the subjective expertise of clinicians. Our research navigates through these advancements, critically analyzing the efficacy of AI in identifying a range of gastrointestinal diseases from various imaging modalities. We meticulously examine case studies and current applications where AI has successfully aided in disease detection, contrasting these AI-driven methods with traditional diagnostic approaches. The findings reveal a remarkable potential of AI in enhancing diagnostic accuracy, while also highlighting some of the current limitations and areas needing further exploration. This study, grounded in recent real-world applications and data, aims to shed light on the potential of AI as a tool not just for augmenting medical diagnostics but also for revolutionizing patient outcomes in gastrointestinal healthcare.

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Published

23.02.2024

How to Cite

Sharma, R. ., & Lamba, C. S. . (2024). Advancing Gastrointestinal Disease Detection through Artificial Intelligence: A Comprehensive Analysis. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 514–518. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4910

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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