Page 78 - ITPS-8-1

INNOSC Theranostics and
Pharmacological Sciences AI-driven innovations in endoscopy

focusing on its role in enhancing diagnostic accuracy, facilitate earlier interventions and improved outcomes.
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standardizing care, addressing implementation challenges, In addition, AI-driven lesion characterization simplifies
and navigating ethical and practical concerns. treatment planning, allowing clinicians to adopt
personalized approaches tailored to individual patient
2. Enhancing polyp detection to reduce needs.
colorectal cancer mortality Emerging research also highlights the potential of AI
Colorectal cancer is the second leading cause of cancer- to predict treatment outcomes based on lesion features.
related deaths worldwide, emphasizing the critical need for For example, certain AI models have demonstrated the
early detection and intervention. The adenoma detection ability to assess the likelihood of recurrence or progression
rate (ADR) serves as a key quality indicator in colonoscopy, in specific gastrointestinal conditions, further advancing
with higher ADRs directly correlating to reduced colorectal precision medicine initiatives in gastroenterology. 7
cancer mortality. Despite advancements, traditional polyp
detection methods remain susceptible to human error, 4. Reducing variability in endoscopic
particularly for smaller, flat, or subtle lesions that are performance
frequently overlooked. One of the most significant challenges in endoscopy is the

AI-assisted endoscopy has demonstrated remarkable variability in diagnostic performance among practitioners.
potential to overcome these limitations. Convolutional Factors such as experience level, fatigue, and workload
neural networks, a type of AI model trained on vast can significantly influence the accuracy of lesion detection
datasets of endoscopic images and videos, have shown the and characterization. Less experienced endoscopists,
ability to identify polyps in real time with high sensitivity for example, often achieve lower ADRs than their more
1,2
and specificity. Clinical trials have reported significant experienced counterparts, resulting in inconsistencies in
improvements in ADR when AI tools are integrated patient care. 8
into colonoscopy procedures. These systems provide AI systems offer a powerful solution to this issue by
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visual markers that highlight suspected polyps, enabling providing real-time decision support during procedures.
endoscopists to detect lesions that might otherwise go Acting as a “second pair of eyes,” AI reduces reliance on
unnoticed. individual operator skill, ensuring a consistent standard of
The consistent improvement in polyp detection offered care. This is particularly valuable in regions with limited
by AI is particularly valuable in high-risk populations and access to highly trained endoscopists, where AI can help
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resource-limited settings, where access to experienced democratize expertise and improve health-care equity.
endoscopists may be constrained. By reducing missed Moreover, AI tools serve as effective training resources
diagnoses, AI has the potential to lower the incidence for endoscopists. Real-time feedback from AI systems
of interval colorectal cancers-those that arise between enables practitioners to identify areas for improvement
scheduled screenings. 4 and refine their techniques. Over time, this feedback loop
can contribute to significant advancements in the overall
3. Advanced lesion characterization for quality of endoscopic care across health-care systems.
personalized care

Beyond polyp detection, AI algorithms have demonstrated 5. Challenges in AI implementation
exceptional accuracy in lesion characterization, effectively 5.1. Validation and generalizability
distinguishing between benign and malignant growths. Despite its promise, the implementation of AI in
This capability stems from AI’s ability to identify complex endoscopy faces several challenges. A key concern is
image patterns that are often imperceptible to the human the validation and generalizability of AI systems across
eye. During real-time endoscopy, AI tools provide diverse clinical settings. AI models are typically trained on
immediate feedback on lesion type, aiding clinicians in specific datasets that may not fully represent the variability
decisions such as whether to perform a biopsy or proceed in patient populations, imaging equipment, and clinical
with therapeutic intervention. 5 environments. Ensuring the reliable performance of AI
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This advanced diagnostic capacity carries several algorithms in real-world scenarios necessitates rigorous,
important implications for clinical practice. By reducing large-scale, multicenter clinical trials. 11
unnecessary biopsies and follow-up procedures, AI In addition, the performance of AI systems may vary
enhances the efficiency and cost-effectiveness of care. based on factors such as image quality, bowel preparation,
Patients benefit from timely, accurate diagnoses that and the presence of comorbidities. Addressing these

Volume 8 Issue 1 (2025) 72 doi: 10.36922/itps.5143