Colonoscopy: more effective and sustainable with artificial intelligence

By Cristiano Antonino On Apr 22, 2022

Artificial intelligence has proven to be a valuable ally in the prevention of colon and rectal cancer, as its algorithms allow for greater accuracy in identifying polyps during colonoscopy

The benefits associated with the use of these technologies are not, however, only clinical, but also translate into terms of greater economic sustainability: these are the findings of a multi-centre study published in the journal The Lancet Digital Health, coordinated by Prof. Alessandro Repici, director of the Department of Gastroenterology at Humanitas and professor at Humanitas University and Prof. Cesare Hassan, professor at Humanitas University, in collaboration with the University of Oslo.

The aim of the project is to study the cost-effectiveness of Artificial Intelligence (AI) technologies in the field of colonoscopy

The use of this software, in fact, requires very high economic investments for hospitals (about $ 19 per patient).

The study signed by Humanitas has shown that, over a period of 30 years, these investments are not only fully amortised, but also generate savings when compared to medical expenses for patients with colon cancer.

AI tools improve the detection rate of colon lesions by 44%, reducing the risk of diagnostic errors.

Performing screening colonoscopies with the aid of AI is associated with an 8.4% reduction in the incidence of colon cancer and a cost saving of $57 per person due to the reduction in treatment costs associated with increased prevention.

All of this data demonstrates the importance of continuing to invest in Artificial Intelligence, both to ensure increasingly effective and personalised patient care and to promote more sustainable healthcare.

Colonoscopy: using AI to enhance diagnosis

Colon cancer is the second most common cause of cancer death in Italy and, to date, the best tool for its correct diagnosis is colonoscopy.

In the development of artificial intelligence algorithms, the hope is that they can support healthcare professionals in identifying even very small polyps, which are otherwise difficult to notice either because they are not well exposed, or because of their colour, which is often very similar to that of healthy mucosa.

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