Mucinous cysts of the pancreas represent the most common identifiable precursor to pancreatic cancer. Evidence-based guidelines for screening and surveillance exist, but many patients are either not properly identified or lost to follow-up. Artificial Intelligence, specifically computational linguistics models, can dramatically improve patient identification and mitigate risk through modernizing pancreatic cyst longitudinal surveillance. Herein we discuss the risk associated with mucinous cysts of the pancreas and modern approaches to patient identification and follow-up.

Aim: As a preliminary stage in the development of an artificial intelligence (AI) algorithm for surgery, this work aimed to study the inter- and intra-individual variability of phase annotations in videos of minimally invasive plate osteosynthesis of distal radius fractures (MIPO). The main hypothesis was that the inter-individual variability was almost perfect if Cohen's kappa coefficient (k) was ≥ 81% overall; the secondary hypothesis was that the intra-individual variability was almost perfect if the F₁-score (F₁) was ≥ 81%.

Methods: The material comprised 9 annotators and three annotated MIPO videos with 5 phases and 4 sub-phases. Each video was presented 3 times to each annotator. The method involved analysing the inter-individual variability of annotations by computing k and F₁ from a reference annotator. The intra-individual variability of annotations was analysed by computing F₁.

Results: Annotation anomalies were noticed: either absences or differences in phase and sub-phase annotations. Regarding the inter-individual variability, an almost perfect agreement between annotators was observed because k ≥ 81% for the three videos. Regarding the intra-individual variability, F₁ ≥ 81% for most phases and sub-phases with the nine annotators.

Conclusion: The homogeneity of annotations must be as high as possible to develop an AI algorithm in surgery. Therefore, it is necessary to identify the least efficient annotators (measurement of the intra-individual variability) to provide them with individual training and a personalised annotation rhythm. It is also important to optimise the definition of the phases, improve the annotation protocol and choose suitable training videos.

Radiomics is an advanced computational analysis of biomedical images that aims to obtain a detailed, objective, and multidimensional characterization of biological tissues. Radiomics features ultimately represent the physiopathology of the tissue under study and can be used to characterize and quantify the spatial distribution and interactions between the voxels that compose a biomedical image. The aim of this paper was to review the current role of radiomics in hepato-bilio-pancreatic surgery by analyzing systematic reviews, meta-analyses and the most relevant published series. Literature data revealed that radiomics is a promising tool in improving the non-invasive characterization and preoperative staging of hepato-bilio-pancreatic neoplasms. Nevertheless, there are major limitations in this approach, mainly linked to the lack of standardization in image acquisition, that result in a significant translational gap between research and clinical practice.

The journal Artificial Intelligence Surgery was established to explore the integration of artificial intelligence (AI) in surgery. It originated from the desire to understand the potential of true robotic surgery, as existing robotic systems are tele-manipulators rather than autonomous robots. AI’s role in surgery involves levels of autonomy and a balance between human expertise and technological advancements. In this regard, a new field of Surgiomics emerges, integrating patient data such as genomics, radiomics, and pathomics to enhance surgical decision-making. Overcoming limitations in surgical data analysis, AI processes vast amounts of data, detects subtle patterns, and explores complex relationships. As Surgiomics continues to evolve, it holds the potential to reshape surgical patient management. Initiatives like the Artificial intelligence, Radiomics, Genomics, Oncopathomics and Surgomics (AiRGOS) Project aim to develop AI algorithms for precision therapeutic treatments in cancer patients using radiologic imaging, genomic sequencing, and clinical data. In this commentary, we envision a future where AI technologies revolutionize surgical decision-making and create personalized treatment plans based on comprehensive patient data.

The medical technological revolution has transformed the nature with which we deliver care. Adjuncts such as artificial intelligence and machine learning have underpinned this. The applications to the field of endoscopy are numerous. Malignant polyps represent a significant diagnostic dilemma as they lie in an area in which mischaracterisation may mean the difference between an endoscopic procedure and a formal bowel resection. This has implications for patients’ oncological outcomes, morbidity and mortality, especially if post-procedure histopathology upstages disease. We have made significant strides with the applications of artificial intelligence to colonoscopic detection. Deep learning algorithms are able to be created from video and image databases. These have been applied to traditional, human-derived, classification methods, such as Paris or Kudo, with up to 93% accuracy. Furthermore, multimodal characterisation systems have been developed, which also factor in patient demographics and colonic location to provide an estimation of invasion and endoscopic resectability with over 90% accuracy. Although the technology is still evolving, and the lack of high-quality randomised controlled trials limits clinical usability, there is an exciting horizon upon us for artificial intelligence-augmented endoscopy.