Role of artificial intelligence in hepatobiliary and pancreatic surgery

Deep learning HRNet FCN for blood vessel identification in laparoscopic pancreatic surgery

Laparoscopic pancreatic surgery remains highly challenging due to the complexity of the pancreas and surrounding vascular structures, with risk of injuring critical blood vessels such as the Superior Mesenteric Vein (SMV)-Portal Vein (PV) axis and splenic vein. Here, we evaluated the High Resolution Network (HRNet)-Full Convolutional Network (FCN) model for its ability to accurately identify vascular contours and improve surgical safety. Using 12,694 images from 126 laparoscopic distal pancreatectomy (LDP) videos and 35,986 images from 138 Whipple procedure videos, the model demonstrated robust performance, achieving a mean Dice coefficient of 0.754, a recall of 85.00%, and a precision of 91.10%. By combining datasets from LDP and Whipple procedures, the model showed strong generalization across different surgical contexts and achieved real-time processing speeds of 11 frames per second during surgery process. These findings highlight HRNet-FCN's potential to recognize anatomical landmarks, enhance surgical precision, reduce complications, and improve laparoscopic pancreatic outcomes.

Ethics and trustworthiness of artificial intelligence in Hepato-Pancreato-Biliary surgery: a snapshot of insights from the European-African Hepato-Pancreato-Biliary Association (E-AHPBA) survey

BACKGROUND

Hepato-Pancreato-Biliary (HPB) surgery is a complex specialty and Artificial Intelligence (AI) applications have the potential to improve pre- intra- and postoperative outcomes of HPB surgery. While ethics guidelines have been developed for the use of AI in clinical surgery, the ethical implications and reliability of AI in HPB surgery remain specifically unexplored.

METHODS

An online survey was developed by the Innovation Committee of the E-AHPBA to investigate the current perspectives on the ethical principles and trustworthiness of AI in HPB Surgery among E-AHPBA membership. The survey consisted of 22 questions, based on guidelines outlined by the Artificial Intelligence Surgery Journal Task Force on AI Ethics in clinical surgery and was disseminated via email to all E-AHPBA members.

RESULTS

A total of 84 members of the E-AHPBA participated in the survey. Seventeen out of 22 questions achieved more than 80 % agreement, with nine of those exceeding 90 %. Five questions had agreement levels between 70 % and 80 %.

CONCLUSION

While HPB surgeons are aware of the need to regulate the use of AI devices, robots, and to protect patient data, consensus appears to be heterogeneous regarding AI's role in mitigating gender-related and minority biases, as well as ensuring fairness and equity.

Comprehensive overview of artificial intelligence in surgery: a systematic review and perspectives

The rapid integration of artificial intelligence (AI) into surgical practice necessitates a comprehensive evaluation of its applications, challenges, and physiological impact. This systematic review synthesizes current AI applications in surgery, with a particular focus on machine learning (ML) and its role in optimizing preoperative planning, intraoperative decision-making, and postoperative patient management. Using PRISMA guidelines and PICO criteria, we analyzed key studies addressing AI's contributions to surgical precision, outcome prediction, and real-time physiological monitoring. While AI has demonstrated significant promise-from enhancing diagnostics to improving intraoperative safety-many surgeons remain skeptical due to concerns over algorithmic unpredictability, surgeon autonomy, and ethical transparency. This review explores AI's physiological integration into surgery, discussing its role in real-time hemodynamic assessments, AI-guided tissue characterization, and intraoperative physiological modeling. Ethical concerns, including algorithmic opacity and liability in high-stakes scenarios, are critically examined alongside AI's potential to augment surgical expertise. We conclude that longitudinal validation, improved AI explainability, and adaptive regulatory frameworks are essential to ensure safe, effective, and ethically sound integration of AI into surgical decision-making. Future research should focus on bridging AI-driven analytics with real-time physiological feedback to refine precision surgery and patient safety strategies.

Virtual 3D models, augmented reality systems and virtual laparoscopic simulations in complicated pancreatic surgeries: state of art, future perspectives, and challenges

Pancreatic surgery is considered one of the most challenging interventions by many surgeons, mainly due to retroperitoneal location and proximity to key and delicate vascular structures. These factors make pancreatic resection a demanding procedure, with successful rates far from optimal and frequent postoperative complications. Surgical planning is essential to improve patient outcomes, and in this regard, many technological advances made in the last few years have proven to be extremely useful in medical fields. This review aims to outline the potential and limitations of 3D digital and 3D printed models in pancreatic surgical planning, as well as the impact and challenges of novel technologies such as augmented/virtual reality systems or artificial intelligence to improve medical training and surgical outcomes.

Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study

Background: Preoperative planning of patients diagnosed with pancreatic head cancer is difficult and requires specific expertise. This pilot study assesses the added value of three-dimensional (3D) patient models and computer-aided detection (CAD) algorithms in determining the resectability of pancreatic head tumors. Methods: This study included 14 hepatopancreatobiliary experts from eight hospitals. The participants assessed three radiologically resectable and three radiologically borderline resectable cases in a simulated setting via crossover design. Groups were divided in controls (using a CT scan), a 3D group (using a CT scan and 3D models), and a CAD group (using a CT scan, 3D and CAD). For the perceived fulfillment of preoperative needs, the quality and confidence of clinical decision-making were evaluated. Results: A higher perceived ability to determine degrees and the length of tumor-vessel contact was reported in the CAD group compared to controls (p = 0.022 and p = 0.003, respectively). Lower degrees of tumor-vessel contact were predicted for radiologically borderline resectable tumors in the CAD group compared to controls (p = 0.037). Higher confidence levels were observed in predicting the need for vascular resection in the 3D group compared to controls (p = 0.033) for all cases combined. Conclusions: "CAD (including 3D) improved experts' perceived ability to accurately assess vessel involvement and supports the development of evolving techniques that may enhance the diagnosis and treatment of pancreatic cancer".

Prevalence of physical frailty and its associated factors among elderly patients undergoing hepatobiliary pancreatic surgery in China

Frailty is a geriatric syndrome characterized by a multisystem physiological decline, increased vulnerability to stressors, and adverse clinical outcomes. However, there is a knowledge gap regarding the association between frailty and its influencing factors. This study aimed to understand the current status of preoperative frailty in elderly patients with hepatobiliary pancreatic disease (HBP) and analyze debilitation-related factors. We enrolled 220 participants aged ≥ 65 years who underwent HBP surgery at two hospitals in China between December 2023 and February 2024. The physical frailty of elderly participants in communities with different characteristics was compared using Kruskal-Wallis and chi-square tests. Ordinal logistic regression analysis was used to analyze the factors influencing preoperative frailty. A total of 212 patients were included in the analysis based on the inclusion and exclusion criteria, with an overall prevalence of frailty at 53 (25%). Ordinal logistic regression analysis results showed that current smoking (odds ratio [OR] = 2.584, p = 0.006) was an independent risk factor for preoperative frailty in elderly participants with HBP. In contrast, exercise habits (OR = 0.323, p < 0.001), two or more multimorbidity statuses (OR = 0.495, p = 0.033), and independent status (OR = 0.216, p < 0.001) were protective factors. Our results suggest that having good exercise habits, not smoking, and independent status can prevent frailty progression in older adults who require HBP surgery. Interventions for frail elderly patients should be supported preoperatively by strengthening exercises to improve tolerance to surgery.

Enhancing Tonsillectomy Recovery with AI: A Comparative Study on Postoperative Care Outcomes

Introduction: Tonsillectomy is commonly associated with significant postoperative challenges such as pain management, complication monitoring, and patient recovery. Traditional care methods, while effective, often do not adequately address these issues, particularly in personalized care and remote monitoring. This study assesses the impact of Artificial Intelligence (AI)-assisted postoperative care on recovery outcomes in tonsillectomy patients compared to conventional care methods. Methods: Conducted at a tertiary care hospital's Otolaryngology Department from January to December 2023, this observational cohort study involved 100 elective tonsillectomy patients. Participants were divided into two cohorts: one receiving traditional care and the other AI-assisted care, which utilized machine learning for pain management, continuous symptom monitoring through wearable devices, and virtual assistance. Results: AI-assisted care significantly improved early postoperative pain management, reducing pain scores to 5.2 ± 1.1 from 6.5 ± 1.2 in traditional care (p = 0.01). Dehydration rates decreased from 6 to 1% (p = 0.05), and the average hospital stay was reduced to 2.8 ± 1.1 days from 3.5 ± 1.2 days. While no significant differences were found in readmission rates for haemorrhage and infection, patient satisfaction notably increased, with pain management improving to 4.4 ± 0.7 and overall satisfaction to 4.1 ± 0.6 (p = 0.03). Conclusion: AI-assisted care offers significant advantages over traditional methods in managing tonsillectomy recovery, optimizing surgical outcomes, and enhancing patient satisfaction. This study supports further exploration into AI's long-term outcomes and its application across various surgical fields.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12070-024-05103-x.

Intra-Operative Tumour Detection and Staging in Pancreatic Cancer Surgery: An Integrative Review of Current Standards and Future Directions

BACKGROUND

Surgical resection for pancreatic ductal adenocarcinoma (PDAC) entails the excision of the primary tumour and regional lymphadenectomy. This traditional strategy is challenged by the high rate of early recurrence, suggesting inadequate disease staging. Novel methods of intra-operative staging are needed to allow surgical resection to be tailored to the disease's biology.

METHODS

A search of published articles on the PubMed and Embase databases was performed using the terms 'pancreas' OR 'pancreatic' AND 'intra-operative staging/detection' OR 'guided surgery'. Articles published between January 2000 and June 2023 were included. Technologies that offered intra-operative staging and tailored treatment were curated and summarised in the following integrative review.

RESULTS

lymph node (LN) mapping and radioimmunoguided surgery have shown promising results but lacked practicality to facilitate real-time intra-operative staging for PDAC. Fluorescence-guided surgery (FGS) offers high contrast and sensitivity, enabling the identification of cancerous tissue and positive LNs with improved precision following intravenous administration of a fluorescent agent. The unique properties of optical coherence tomography and ultrasound elastography lend themselves to be platforms for virtual biopsy intra-operatively.

CONCLUSIONS

Accurate intra-operative staging of PDAC, localisation of metastatic LNs, and identification of extra-pancreatic disease remain clinically unmet needs under current detection methods and staging standards. Tumour-specific FGS combined with other diagnostic and therapeutic modalities could improve tumour detection and staging in patients with PDAC.

Robotic pancreas surgery for pancreatic cancer

Since the introduction of robotic pancreas surgery in the early 2000s, there has been significant increase in the adoption of the robot to perform complex pancreatic resections. However, utilization of the robot for pancreatic cancer has lagged behind due to concern for inferior oncologic outcomes. Furthermore, research in this field has previously been limited to small, single institution observational studies. Recent and ongoing randomized controlled trials in robotic distal pancreatectomy and robotic pancreatoduodenectomy have aimed to address concerns regarding the use of robotic techniques in pancreatic cancer. Together, these studies suggest similar, if not improved, outcomes with a robotic approach, including shorter hospital stays, expedited recovery with less postoperative complications, and equivalent resection rates, when compared to the standard open approaches. Additionally, surgical training in robotic pancreas surgery is of equal importance for patient safety. This review summarizes the available literature on the efficacy and safety of robotic pancreas surgery for pancreatic cancer, with specific focus on robotic distal pancreatectomy and robotic pancreatoduodenectomy.

The utility of axial imaging among selected patients in the early postoperative period after pancreatectomy

BACKGROUND

Postoperative computed tomography imaging has been shown to play an important role in avoiding failure-to-rescue. We sought to examine the impact of the timing of such imaging studies on outcomes after pancreatectomy.

METHODS

Patients who underwent pancreatic resection at our institution from 2017 to 2022 were reviewed retrospectively to identify those undergoing computed tomography for any indication before discharge. Patients were subdivided by the postoperative day that the first computed tomography scan was obtained: immediate (postoperative day <3), early (postoperative day 3-7), and delayed (postoperative day >7).

RESULTS

Of 370 patients, 110 (30%) had a computed tomography during the initial surgical stay. The 3 timing groups were similar in age, comorbidities, pathology, operative time, and number of scans. When comparing the early with the delayed group, we found that antibiotic usage, percutaneous drainage, and overall invasive interventions during surgical stay were all similar. However, those patients who were scanned in the early period had significantly shorter length of stay (17.05 vs 22.82, P = .0008) and fewer composite days hospitalized (20.1 vs 24.9, P = .01) relative to the delayed group. Importantly, early computed tomography imaging was found to be the only independent predictor of a postoperative length of stay ≤15 days on multivariate analysis. Surgical stay mortality rates were significantly lower in the early compared with delayed group (0% vs 11%, P = .02). A change in treatment was observed in 59% after computed tomography, with 15% undergoing invasive interventions, 27% treated medically, and 16% with expectant management.

CONCLUSION

In our cohort, patients imaged early after pancreatectomy experienced shorter hospital stays and lower inpatient mortality relative to those scanned after the first postoperative week.

The integration of artificial intelligence in robotic surgery: A narrative review

BACKGROUND

The rise of high-definition imaging and robotic surgery has independently been associated with improved postoperative outcomes. However, steep learning curves and finite human cognitive ability limit the facility in imaging interpretation and interaction with the robotic surgery console interfaces. This review presents innovative ways in which artificial intelligence integrates preoperative imaging and surgery to help overcome these limitations and to further advance robotic operations.

METHODS

PubMed was queried for "artificial intelligence," "machine learning," and "robotic surgery." From the 182 publications in English, a further in-depth review of the cited literature was performed.

RESULTS

Artificial intelligence boasts efficiency and proclivity for large amounts of unwieldy and unstructured data. Its wide adoption has significant practice-changing implications throughout the perioperative period. Assessment of preoperative imaging can augment preoperative surgeon knowledge by accessing pathology data that have been traditionally only available postoperatively through analysis of preoperative imaging. Intraoperatively, the interaction of artificial intelligence with augmented reality through the dynamic overlay of preoperative anatomical knowledge atop the robotic operative field can outline safe dissection planes, helping surgeons make critical real-time intraoperative decisions. Finally, semi-independent artificial intelligence-assisted robotic operations may one day be performed by artificial intelligence with limited human intervention.

CONCLUSION

As artificial intelligence has allowed machines to think and problem-solve like humans, it promises further advancement of existing technologies and a revolution of individualized patient care. Further research and ethical precautions are necessary before the full implementation of artificial intelligence in robotic surgery.

The Future of Artificial Intelligence in Surgery

Until recently, innovations in surgery were largely represented by extensions or augmentations of the surgeon's perception. This includes advancements such as the operating microscope, tumor fluorescence, intraoperative ultrasound, and minimally invasive surgical instrumentation. However, introducing artificial intelligence (AI) into the surgical disciplines represents a transformational event. Not only does AI contribute substantively to enhancing a surgeon's perception with such methodologies as three-dimensional anatomic overlays with augmented reality, AI-improved visualization for tumor resection, and AI-formatted endoscopic and robotic surgery guidance. What truly makes AI so different is that it also provides ways to augment the surgeon's cognition. By analyzing enormous databases, AI can offer new insights that can transform the operative environment in several ways. It can enable preoperative risk assessment and allow a better selection of candidates for procedures such as organ transplantation. AI can also increase the efficiency and throughput of operating rooms and staff and coordinate the utilization of critical resources such as intensive care unit beds and ventilators. Furthermore, AI is revolutionizing intraoperative guidance, improving the detection of cancers, permitting endovascular navigation, and ensuring the reduction in collateral damage to adjacent tissues during surgery (e.g., identification of parathyroid glands during thyroidectomy). AI is also transforming how we evaluate and assess surgical proficiency and trainees in postgraduate programs. It offers the potential for multiple, serial evaluations, using various scoring systems while remaining free from the biases that can plague human supervisors. The future of AI-driven surgery holds promising trends, including the globalization of surgical education, the miniaturization of instrumentation, and the increasing success of autonomous surgical robots. These advancements raise the prospect of deploying fully autonomous surgical robots in the near future into challenging environments such as the battlefield, disaster areas, and even extraplanetary exploration. In light of these transformative developments, it is clear that the future of surgery will belong to those who can most readily embrace and harness the power of AI.

Virtual Reality for Preoperative Planning in Complex Surgical Oncology: A Single-Center Experience

INTRODUCTION

Virtual reality models (VRM) are three-dimensional (3D) simulations of two-dimensional (2D) images, creating a more accurate mental representation of patient-specific anatomy.

METHODS

Patients were retrospectively identified who underwent complex oncologic resections whose operations differed from preoperative plans between April 2018 and April 2019. Virtual reality modeling was performed based on preoperative 2D images to assess feasibility of use of this technology to create models. Preoperative plans made based upon 2D imaging versus VRM were compared to the final operations performed. Once the use of VRM to create preoperative plans was deemed feasible, individuals undergoing complex oncologic resections whose operative plans were difficult to define preoperatively were enrolled prospectively from July 2019 to December 2021. Preoperative plans made based upon 2D imaging and VRM by both the operating surgeon and a consulting surgeon were compared to the operation performed. Confidence in each operative plan was also measured.

RESULTS

Twenty patients were identified, seven retrospective and 13 prospective, with tumors of the liver, pancreas, retroperitoneum, stomach, and soft tissue. Retrospectively, VRM were unable to be created in one patient due to a poor quality 2D image; the remainder (86%) were successfully able to be created and examined. Virtual reality modeling more clearly defined the extent of resection in 50% of successful cases. Prospectively, all VRM were successfully performed. The concordance of the operative plan with VRM was higher than with 2D imaging (92% versus 54% for the operating surgeon and 69% versus 23% for the consulting surgeon). Confidence in the operative plan after VRM compared to 2D imaging also increased for both surgeons (by 15% and 8% for the operating and consulting surgeons, respectively).

CONCLUSIONS

Virtual reality modeling is feasible and may improve preoperative planning compared to 2D imaging. Further investigation is warranted.

Advancements and Challenges in the Application of Artificial Intelligence in Surgical Arena: A Literature Review

This literature review delves into the transformative potential of artificial intelligence (AI) in the field of surgery, exploring its evolution, applications, and technological advancements. AI, with its ability to mimic human intelligence, presents a paradigm shift in surgical practices. The review critically analyzes a broad range of research, encompassing machine learning, deep learning, natural language processing, and their diverse applications in preoperative planning, surgical simulation, intraoperative guidance, and postoperative analysis. Ethical, legal, and regulatory considerations, as well as challenges and future directions, are also explored. The study underscores AI's ability to revolutionize surgical visualization and its role in shaping the future of healthcare.

Augmented reality-assisted navigation system contributes to better intraoperative and short-time outcomes of laparoscopic pancreaticoduodenectomy: a retrospective cohort study

BACKGROUND

Augmented reality (AR)-assisted navigation system are currently good techniques for hepatectomy; however, its application and efficacy for laparoscopic pancreatoduodenectomy have not been reported. This study sought to focus on and evaluate the advantages of laparoscopic pancreatoduodenectomy guided by the AR-assisted navigation system in intraoperative and short-time outcomes.

METHODS

Eighty-two patients who underwent laparoscopic pancreatoduodenectomy from January 2018 to May 2022 were enrolled and divided into the AR and non-AR groups. Clinical baseline features, operation time, intraoperative blood loss, blood transfusion rate, perioperative complications, and mortality were analyzed.

RESULTS

AR-guided laparoscopic pancreaticoduodenectomy was performed in the AR group ( n =41), whereas laparoscopic pancreatoduodenectomy was carried out routinely in the non-AR group ( n =41). There was no significant difference in baseline data between the two groups ( P >0.05); Although the operation time of the AR group was longer than that of the non-AR group (420.15±94.38 vs. 348.98±76.15, P <0.001), the AR group had a less intraoperative blood loss (219.51±167.03 vs. 312.20±195.51, P =0.023), lower blood transfusion rate (24.4 vs. 65.9%, P <0.001), lower occurrence rates of postoperative pancreatic fistula (12.2 vs. 46.3%, P =0.002) and bile leakage (0 vs. 14.6%, P =0.026), and shorter postoperative hospital stay (11.29±2.78 vs. 20.04±11.22, P <0.001) compared with the non-AR group.

CONCLUSION

AR-guided laparoscopic pancreatoduodenectomy has significant advantages in identifying important vascular structures, minimizing intraoperative damage, and reducing postoperative complications, suggesting that it is a safe, feasible method with a bright future in the clinical setting.

Applicability and reproducibility of the validated intraoperative bleeding severity scale (VIBe scale) in liver surgery: A multicenter study

BACKGROUND

Bleeding is an intraoperative and postoperative complication of liver surgery of concern, and yet evidence to support utility and reproducibility of bleeding scales for liver surgery is limited. We determined the reproducibility of the clinician-reported validated intraoperative bleeding severity scale and its clinical value of implementation in liver surgery.

METHODS

In this descriptive and observational multicenter study, we assessed the performance of liver surgeons instructed on the clinician-reported intraoperative bleeding severity scale using training videos that covered all 5 grades of bleeding severity. Surgeons were stratified according to years of surgical experience and number of surgeries performed per year based on a median split in low and high values. Intraobserver and interobserver agreement was assessed using Kendall's coefficient of concordance (Kendall's W).

RESULTS

Forty-seven surgeons from 10 hospitals in Spain participated in the study. The overall intraobserver concordance was 0.985, and the overall interobserver concordance was 0.929. For "high experience" surgeons, the intraobserver and interobserver agreement values were 0.990 and 0.941, respectively. For "low experience" surgeons, the intraobserver and interobserver agreement was 0.981 and 0.922, respectively. Regarding the annual number of surgeries, intraobserver and interobserver agreement values were 0.995 and 0.940, respectively, for surgeons performing >35 surgeries per year, with 0.979 and 0.923, respectively, for surgeons who perform ≤35 surgeries year.

CONCLUSION

The clinician-reported intraoperative bleeding severity scale shows high interobserver and intraobserver concordance, suggesting it is a useful tool for assessing severity of bleeding during liver surgery; years of surgical experience and number of annual procedures performed did not affect the applicability of the clinician-reported intraoperative bleeding severity scale.

Three-dimensional visualization and virtual reality simulation role in hepatic surgery: Further research warranted

Artificial intelligence (AI) is the study of algorithms that enable machines to analyze and execute cognitive activities including problem solving, object and word recognition, reduce the inevitable errors to improve the diagnostic accuracy, and decision-making. Hepatobiliary procedures are technically complex and the use of AI in perioperative management can improve patient outcomes as discussed below. Three-dimensional (3D) reconstruction of images obtained via ultrasound, computed tomography scan or magnetic resonance imaging, can help surgeons better visualize the surgical sites with added depth perception. Pre-operative 3D planning is associated with lesser operative time and intraoperative complications. Also, a more accurate assessment is noted, which leads to fewer operative complications. Images can be converted into physical models with 3D printing technology, which can be of educational value to students and trainees. 3D images can be combined to provide 3D visualization, which is used for preoperative navigation, allowing for more precise localization of tumors and vessels. Nevertheless, AI enables surgeons to provide better, personalized care for each patient.

Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery

Background and Objectives: The advancement of artificial intelligence (AI) based technologies in medicine is progressing rapidly, but the majority of its real-world applications has not been implemented. The establishment of an accurate diagnosis with treatment has now transitioned into an artificial intelligence era, which has continued to provide an amplified understanding of liver cancer as a disease and helped to proceed better with the method of procurement. This article focuses on reviewing the AI in liver-associated diseases and surgical procedures, highlighting its development, use, and related counterparts. Materials and Methods: We searched for articles regarding AI in liver-related ailments and surgery, using the keywords (mentioned below) on PubMed, Google Scholar, Scopus, MEDLINE, and Cochrane Library. Choosing only the common studies suggested by these libraries, we segregated the matter based on disease. Finally, we compiled the essence of these articles under the various sub-headings. Results: After thorough review of articles, it was observed that there was a surge in the occurrence of liver-related surgeries, diagnoses, and treatments. Parallelly, advanced computer technologies governed by AI continue to prove their efficacy in the accurate screening, analysis, prediction, treatment, and recuperation of liver-related cases. Conclusions: The continual developments and high-order precision of AI is expanding its roots in all directions of applications. Despite being novel and lacking research, AI has shown its intrinsic worth for procedures in liver surgery while providing enhanced healing opportunities and personalized treatment for liver surgery patients.

The role of artificial intelligence in pancreatic surgery: a systematic review

Artificial intelligence (AI), including machine learning (ML), is being slowly incorporated in medical practice, to provide a more precise and personalized approach. Pancreatic surgery is an evolving field, which offers the only curative option for patients with pancreatic cancer. Increasing amounts of data are available in medicine: AI and ML can help incorporate large amounts of information in clinical practice. We conducted a systematic review, based on PRISMA criteria, of studies that explored the use of AI or ML algorithms in pancreatic surgery. To our knowledge, this is the first systematic review on this topic. Twenty-five eligible studies were included in this review; 12 studies with implications in the preoperative diagnosis, while 13 studies had implications in patient evolution. Preoperative diagnosis, such as predicting the malignancy of IPMNs, differential diagnosis between pancreatic cystic lesions, classification of different pancreatic tumours, and establishment of the correct management for each of these lesions, can be facilitated through different AI or ML algorithms. Postoperative evolution can also be predicted, and some studies reported prediction models for complications, including postoperative pancreatic fistula, while other studies have analysed the implications for prognosis evaluation (from predicting a textbook outcome, the risk of metastasis or relapse, or the mortality rate and survival). One study discussed the possibility of predicting an intraoperative complication-massive intraoperative bleeding. Artificial intelligence and machine learning models have promising applications in pancreatic surgery, in the preoperative period (high-accuracy diagnosis) and postoperative setting (prognosis evaluation and complication prediction), and the intraoperative applications have been less explored.

Gallbladder cancer: Historical treatment and new management options

Gallbladder cancer is a rare, aggressive malignancy that has a poor overall prognosis. Effective treatment consists of early detection and surgical treatment. With the wide spread treatment of gallbladder disease with minimally invasive techniques, the rate of incidental gallbladder cancer has seen an equitable rise along with stage migration towards earlier disease. Although the treatment remains mostly surgical, newer modalities such as regional therapy as well as directed therapy based on molecular medicine has led to improved outcomes in patients with advanced disease. We aim to summarize the management of gallbladder cancer along with the newer developments in this formidable disease process.

Current trends in three-dimensional visualization and real-time navigation as well as robot-assisted technologies in hepatobiliary surgery

With the continuous development of digital medicine, minimally invasive precision and safety have become the primary development trends in hepatobiliary surgery. Due to the specificity and complexity of hepatobiliary surgery, traditional preoperative imaging techniques such as computed tomography and magnetic resonance imaging cannot meet the need for identification of fine anatomical regions. Imaging-based three-dimensional (3D) reconstruction, virtual simulation of surgery and 3D printing optimize the surgical plan through preoperative assessment, improving the controllability and safety of intraoperative operations, and in difficult-to-reach areas of the posterior and superior liver, assistive robots reproduce the surgeon's natural movements with stable cameras, reducing natural vibrations. Electromagnetic navigation in abdominal surgery solves the problem of conventional surgery still relying on direct visual observation or preoperative image assessment. We summarize and compare these recent trends in digital medical solutions for the future development and refinement of digital medicine in hepatobiliary surgery.