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Artificial intelligence (AI)-assisted colonoscopy has emerged as a tool to enhance adenoma detection rates (ADRs) and improve lesion characterization. However, its performance in real-world settings, especially in developing countries, remains uncertain.
The aim of this study was to evaluate the impact of AI on ADRs and its concordance with histopathological diagnosis.
A matched case–control study was conducted at a colorectal cancer (CRC) referral center, including 146 patients aged 45–75 years who underwent colonoscopy for CRC screening or surveillance. Patients were allocated into two groups: AI-assisted colonoscopy (n=74) and high-definition conventional colonoscopy (n=72). The primary outcome was ADR, and the secondary outcome was the agreement between AI-based lesion characterization and histopathology. Statistical analysis was performed with a significance level of p<0.05.
ADR was higher in the AI group (60%) than in the control group (50%), but this difference was not statistically significant (p>0.05). AI-assisted lesion characterization showed substantial agreement with histopathology (kappa=0.692). No significant difference was found in withdrawal time (29 min vs. 27 min; p>0.05), indicating that AI did not delay the procedure
Although AI did not significantly increase ADR compared to conventional colonoscopy, it demonstrated strong histopathological concordance, supporting its reliability in lesion characterization. AI may reduce interobserver variability and optimize real-time decision-making, reinforcing its clinical utility in CRC screening.
The introduction of chatbots has been one of the most intriguing advances in artificial intelligence. There are numerous potential uses for artificial intelligence in clinical research. However, there are also some issues that require attention. Everyone agrees that AI requires a more stable foundation and that a cutting-edge approach is necessary for AI to operate effectively.
The field of medicine has always been at the forefront of technological innovation, constantly seeking new strategies to diagnose, treat, and prevent diseases. Guidelines for clinical practice to orientate medical teams regarding diagnosis, treatment, and prevention measures have increased over the years. The purpose is to gather the most medical knowledge to construct an orientation for practice. Evidence-based guidelines follow several main characteristics of a systematic review, including systematic and unbiased search, selection, and extraction of the source of evidence. In recent years, the rapid advancement of artificial intelligence has provided clinicians and patients with access to personalized, data-driven insights, support and new opportunities for healthcare professionals to improve patient outcomes, increase efficiency, and reduce costs. One of the most exciting developments in Artificial Intelligence has been the emergence of chatbots. A chatbot is a computer program used to simulate conversations with human users. Recently, OpenAI, a research organization focused on machine learning, developed ChatGPT, a large language model that generates human-like text. ChatGPT uses a type of AI known as a deep learning model. ChatGPT can quickly search and select pieces of evidence through numerous databases to provide answers to complex questions, reducing the time and effort required to research a particular topic manually. Consequently, language models can accelerate the creation of clinical practice guidelines. While there is no doubt that ChatGPT has the potential to revolutionize the way healthcare is delivered, it is essential to note that it should not be used as a substitute for human healthcare professionals. Instead, ChatGPT should be considered a tool that can be used to augment and support the work of healthcare professionals, helping them to provide better care to their patients.
Developed by Surya MKT