Advancements in the diagnosis of biliopancreatic diseases: A comparative review and study on future insights

Abstract

Owing to the complex and often asymptomatic presentations, the diagnosis of biliopancreatic diseases, including pancreatic and biliary malignancies, remains challenging. Recent technological advancements have remarkably improved the diagnostic accuracy and patient outcomes in these diseases. This review explores key advancements in diagnostic modalities, including biomarkers, imaging techniques, and artificial intelligence (AI)-based technologies. Biomarkers, such as cancer antigen 19-9, KRAS mutations, and inflammatory markers, provide crucial insights into disease progression and treatment responses. Advanced imaging modalities include enhanced computed tomography (CT), positron emission tomography-CT, magnetic resonance cholangiopancreatography, and endoscopic ultrasound. AI integration in imaging and pathology has enhanced diagnostic precision through deep learning algorithms that analyze medical images, automate routine diagnostic tasks, and provide predictive analytics for personalized treatment strategies. The applications of these technologies are diverse, ranging from early cancer detection to therapeutic guidance and real-time imaging. Biomarker-based liquid biopsies and AI-assisted imaging tools are essential for non-invasive diagnostics and individualized patient management. Furthermore, AI-driven models are transforming disease stratification, thus enhancing risk assessment and decision-making. Future studies should explore standardizing biomarker validation, improving AI-driven diagnostics, and expanding the accessibility of advanced imaging technologies in resource-limited settings. The continued development of non-invasive diagnostic techniques and precision medicine approaches is crucial for optimizing the detection and management of biliopancreatic diseases. Collaborative efforts between clinicians, researchers, and industry stakeholders will be pivotal in applying these advancements in clinical practice.

Key Words: Biliopancreatic diseases; Endoscopic ultrasound; Endoscopic retrograde cholangiopancreatography; Magnetic resonance cholangiopancreatography; Peroral cholangiopancreatoscopy; Diagnostic advancements; Biomarkers in biliopancreatic diseases; Artificial intelligence in gastroenterology

Core Tip: Recent advancements in the diagnosis of biliopancreatic disease have significantly transformed clinical practice. Enhanced imaging techniques such as endoscopic ultrasound and computed tomography can provide detailed anatomical insights for accurate diagnosis. Additionally, the integration of biomarkers and artificial intelligence technologies can improve early disease detection and diagnostic precision. These innovations facilitate targeted treatment strategies tailored to individual patient needs, ultimately enhancing patient outcomes and quality of life. As the field continues to evolve, ongoing research and collaboration among healthcare professionals will be essential to further refine the diagnostic tools and approaches for biliopancreatic diseases.

INTRODUCTION

Biliopancreatic diseases include a wide array of disorders of the bile ducts, gall bladder, and pancreas and are considered to be some of the primary antagonists in gastrointestinal pathology. Gastroenterological studies and analyses performed over the years have identified and described biliopancreatic diseases such as gallstones, cholecystitis, pancreatitis, and biliopancreatic malignancies[1]. According to Villari et al[1], elderly patients, particularly those aged > 70 years, have significantly elevated susceptibility to acute biliopancreatic diseases. Gallstones, inflammatory diseases of the biliary tree, and biliary malignancies are associated with the highest comorbidity and mortality rates in this cohort. Furthermore, results from a recent analysis by the Spanish Society of Pathology and the Spanish Society of Medical Oncology showed that patients diagnosed with biliopancreatic malignancies have a significantly poor prognosis[2]. However, diagnosing these diseases is a complex undertaking mainly attributed to their often subtle and nonspecific clinical presentations, which complicate the timely and accurate diagnosis of these diseases[3].

Within the past few decades, there has been remarkable advancement in diagnostic modalities for biliopancreatic diseases, with the enhancement of conventional imaging techniques such as ultrasonography and computed tomography (CT) into more advanced and intricate modalities such as magnetic resonance cholangiopancreatography (MRCP) and endoscopic ultrasound (EUS)[3]. These inventions have evolved the resolution and sensitivity landscape and offered an invaluable tool in the earlier and more precise detection of pathological changes in the biliary tree and pancreas[4-6].

Concurrently, significant strides made in the molecular diagnostic field for identifying substantial biomarkers, including genetic, epigenetic, and protein markers, have remarkably revolutionized and enhanced the specificity and sensitivity of diagnostic protocols[7]. These biomarkers provide significant insights into the underlying pathophysiology of biliopancreatic diseases and have significant potential for guiding personalized therapeutic strategies[7]. Endoscopic techniques have evolved considerably in recent years. For example, endoscopic retrograde cholangiopancreatography (ERCP) is complemented by less invasive procedures such as EUS-guided fine-needle aspiration (EUS-FNA). These advancements have led to accurate diagnosis and have been important in offering therapeutic interventions, thereby reducing the need for invasive surgical procedures[8].

The 21^st century has been mainly characterized by significant technological innovations, which have had tremendous strides in the medical diagnostic field. In particular, artificial intelligence (AI) and machine learning (ML) technologies have emerged as transformative tools in the diagnostic landscape[9-11]. These technologies can enhance image analysis, predict disease progression, and personalize patient management[12]. AI-driven algorithms assist clinicians in interpreting complex imaging studies and integrating diverse diagnostic data, leading to more informed decision-making[13].

Despite these advancements, challenges remain in the early detection and differentiation of biliopancreatic diseases, particularly in distinguishing benign from malignant conditions. Notably, global disparities in the accessibility of diagnostic technology substantially impact the diagnosis and management of biliopancreatic disease. Saeed and Masters[14] describe these disparities as “the digital divide,” which is largely associated with poor health outcomes despite medical technological improvements. Given the severity of hepatocellular diseases and their poor prognoses, the need for highly specialized and complex diagnostic interventions, such as magnetic resonance imaging (MRI), CT, and molecular diagnostics, is highlighted[15]. Khaing et al[16] noted that access to advanced diagnostic tools is heavily skewed toward high-income countries (HICs), whereas low- and middle-income countries (LMICs) often experience severe shortages.

In HICs, advanced diagnostic tools routinely identify biliopancreatic diseases during the early disease stages-when treatment is most effective. For instance, EUS and MRCP are standard methods of evaluating biliary and pancreatic structures that enable the precise diagnosis and staging of cancers. Furthermore, biomarker tests, such as cancer antigen (CA) 19-9 for pancreatic cancer, complement imaging assessments by providing molecular insights guiding personalized treatment strategies. In contrast, LMICs often lack access to these technologies because of economic constraints, inadequate healthcare infrastructure, and shortages of trained specialists. Consequently, healthcare providers in these regions frequently rely on less accurate methods, such as basic abdominal ultrasound, which can overlook early symptoms of the disease. This diagnostic gap leads to delayed diagnosis, with several patients only obtaining a diagnosis at advanced stages, when treatment options are limited and prognoses are poor.

The consequences of such disparities are severe. Biliopancreatic diseases, particularly pancreatic cancer, are associated with high mortality rates worldwide; however, the burden is disproportionately borne by LMICs. Late-stage diagnoses in LMICs contribute to poor survival outcomes and place a remarkable economic strain on fragile healthcare systems. Moreover, the lack of advanced diagnostic technologies exacerbates health inequities, considering patients in LMICs are often cannot afford basic diagnostic services, let alone specialized tests. This leads to delayed care, increased morbidity, and high healthcare costs, which further widens the gap between HICs and LMICs.

Although efforts to address these disparities are underway, systemic and other challenges remain, such as funding shortages, political instability, and inequitable resource distribution across the globe. Thus, continuous research and innovation are required to refine the existing diagnostic tools and develop novel approaches to improve patient outcomes[3]. Therefore, this review aims to provide a comprehensive overview of recent advancements in the diagnosis of biliopancreatic diseases. By examining the latest developments in imaging techniques, molecular diagnostics, endoscopic procedures, and AI applications, we seek to highlight the progress and identify areas for future research and clinical practice improvements.

Our review included all available data in the Cochrane Library, Web of Science, PubMed, and Google Scholar databases until June 2024.

ADVANCEMENTS IN IMAGING MODALITIES FOR THE DIAGNOSIS OF BILIOPANCREATIC DISEASES

Imaging as a diagnostic modality in biliopancreatic diseases

As conventional radiographs can only detect a small proportion of biliopancreatic anomalies, imaging as a diagnostic modality for biliopancreatic diseases has significantly evolved over the years, with optical choledochoscopy being the earliest imaging modality reported in 1941[17]. Since the development of the optical choledochoscope, there has been significant progress in visualizing biliopancreatic anatomical structures over the 20^th and 21^st centuries, thereby significantly enhancing the ability of modern imaging modalities to detect, characterize, and monitor biliopancreatic diseases.

US and EUS

According to Novitch et al[18], the use of diagnostic ultrasonography dates back to the 1940s, based on the work of Dr. Tussik in 1942[19]. Thereafter, this technique has been widely adopted in the medical field because of its extensive availability and training by more clinicians[18]. Moreover, diagnostic ultrasonography has become an invaluable addition to the medical landscape because of its lack of ionizing radiation, which allows repetitive use, its non-invasive nature unlike other surgical alternatives, and its smooth learning curve by facilitating real-time, high-quality image resolution that allows real-time anatomical and functional learning opportunities[18].

The mechanisms underlying diagnostic ultrasonography are based on the transmission of extremely high-frequency sound waves produced by a transducer, which are then reflected to the transducer. These waves are reflected by different acoustic properties, through which images can be generated. Thus, EUS refers to the application of ultrasonographic images to diagnose and treat pathologies by a trained endoscopist during endoscopic examination[20]. After the development of medical diagnostic ultrasonography in 1940, the works of DiMagno and DiMagno[21] in the 1978s have provided key pioneering insights into endoscopic ultrasonography. DiMagno and DiMagno[21] was part of a team sponsored by the Development of Ultrasonic Endoscopic Probes for Cancer Diagnosis from 1978 to 1981, during which the first endoscopic probe was tested on an animal subject, a dog. Based on these investigations, DiMagno and DiMagno[21] hypothesized that EUS can visualize the gastrointestinal lumen while simultaneously providing high-resolution scans of adjacent anatomical structures. DiMagno and DiMagno[21] and the Mayo group, which comprised six co-investigators in 1979, were responsible for the first EUS test conducted on human subjects.

The initial EUS probe designed by DiMagno and DiMagno[21] comprised a 13-mm diameter American cystoscope, an fx-5 side-viewing endoscope paired with an 80-mm rigid tip comprised 10 megahertz, 64-element real-time image array (with a 30-frame capacity), and a 3 × 4 US probe[18]. Moreover, the design comprises a flag handle for tip maneuvering, which has now been rendered obsolete.

Since the 1980s, EUS technology has undergone significant advancements primarily based on its popularity in diagnostic imaging, which has introduced two types of echoendoscopes (linear echoendoscope and radial EUS)[22]. Radial EUS, which provides a 360º plane perpendicular to the field of view to the scope, first produces an image similar to a CT scan image[20]. The linear EUS model provides oblique images parallel to the scope, thereby facilitating therapeutic intervention using the endoscope, as shown in Figure 1.

Figure 1 Curved linear array.

In particular, EUS-FNA is advantageous for the diagnosis of pancreaticobiliary diseases because it can perform biopsies on extraluminal targets. This feature makes it invaluable to access and sample lesions located outside the gastrointestinal tract. These lesions include those in the pancreas, bile ducts, and surrounding lymph nodes, which are often difficult to reach using other modalities. The precision of EUS-FNA, facilitated by real-time imaging with a linear array endoscope, allows targeted tissue acquisition from deep-seated or small lesions, without the need for invasive surgery. This capability is important for diagnosing malignancies, cystic lesions, and lymphadenopathy in the pancreaticobiliary region, particularly in cases in which traditional endoscopic or imaging techniques may fall short. The ability of EUS-FNA to bypass anatomical barriers and accurately sample tissue contributes to the earlier diagnosis and more effective management of pancreaticobiliary diseases.

EUS can facilitate examination of the gallbladder in the stomach and duodenum. In particular, linear EUS can assess the gallbladder from four locations: the fundus, antrum, bulb, and descending duodenum, as depicted in Figure 2.

Figure 2 Curved linear array endoscopic utrasound technique in the gallbladder from the stomach and duodenal bulb[148]. Citation: Okasha HH, Gadour E, Atalla H, AbdEl-Hameed OA, Ezzat R, Alzamzamy AE, Ghoneem E, Matar RA, Hassan Z, Miutescu B, Qawasmi A, Pawlak KM, Elmeligui A. Practical approach to linear endoscopic ultrasound examination of the gallbladder. World J Radiol 2024; 16: 184-195. Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc.

Transabdominal ultrasonography is non-invasive, can facilitate real-time imaging, and has a low overall cost. Therefore, it is the first-line and most commonly applied imaging modality for diagnostic workups in patients with biliopancreatic diseases[23]. However, pancreatic EUS is quite challenging to perform owing to the retroperitoneal location, overlying structures, and small size of the pancreas[23]. Advancements in the field in terms of radiologist training and the introduction of high-resolution scanners have remarkably improved imaging quality with the use of modern EUS to examine the whole pancreas, except in cases where patients present with impassable duodenal stenosis or nonamendable postsurgical anatomies[24].

Challenges and limitations associated with EUS

EUS is a valuable diagnostic tool that combines endoscopy and ultrasonography to produce detailed images of the gastrointestinal tract and the surrounding organs. However, their use is associated with several challenges and limitations. One significant challenge is the dependence on operator expertise[25-27]. Inexperienced practitioners may struggle with the complexity of the procedure leading to suboptimal results. Thus, the efficacy of EUS is highly reliant on the skill and experience of the operator[26]. In addition, the considerable learning curve can affect the widespread adoption and availability of EUS.

Moreover, interpreting EUS images can be subjective and vary between operators, potentially leading to inconsistent diagnostic outcomes and misdiagnosis. Therefore, extensive training is required to accurately interpret images, particularly when distinguishing benign from malignant lesions. Furthermore, although EUS is less invasive than other surgical procedures, it is associated with some risks. Complications, such as bleeding, infection, and perforation, are rare. However, they can also occur and require prompt management[28]. Patients may also experience discomfort or adverse reactions to sedatives used during the procedure. EUS-FNA has additional risks, including puncture site infection and bleeding, and is associated with a low risk of tumor cells spreading along the needle tract[29]. In addition, anatomical constraints and patient variability can affect EUS feasibility and efficacy. Specific anatomical locations may be difficult to access, thereby limiting the ability to evaluate and sample the lesions in these areas. Moreover, variations in patient anatomy, such as the presence of scar tissue, and previous surgical history can further complicate the procedure[29,30]. Despite these challenges, EUS remains an essential tool in the diagnostic arsenal, and addressing these limitations requires ongoing advancements in technology, training, and patient selection.

Summary of the current clinical trials on the use of EUS in biliopancreatic disease diagnosis are available in (Supplementary Table 1).

ERCP

ERCP is an imaging technique that combines endoscopy and fluoroscopy for the diagnosis and treatment of biliary and pancreatic ductal systems[31]. This imaging modality was introduced in the 1960s and has become valuable in gastroenterology with improvements in scope design and imaging quality. ERCP allows the visualization and injection of high-contrast medium into the pancreatic and biliary ducts, which allows a more straightforward interpretation of radiographic images and has been primarily used to diagnose and treat biliopancreatic diseases, including gallstones, inflammatory strictures, leaks, and malignancies[31].

Based on the study by Meseeha and Attia[31], the mechanisms behind ERCP involve the use of endoscopic papillotomy, sphincter of Oddi manometry, endoscopic papillary balloon dilation, tissue sampling, stone removal, placement of biliary and pancreatic stents, cholangiopancreatoscopy, and/or biliary and pancreatic drainage. The same study also presented a detailed procedure for the effective use of ERCP. Despite advancements associated with ERCP, several complications have been reported, reaching as high as 6.8% in all cases[31]. These complications are often associated with blood transfusion (> 4 units) and hospitalization for > 10 days[31].

The significant complications associated with ERCP include post-ERCP pancreatitis (mild to moderate severity), gastrointestinal bleeding, and duodenal and biliary perforations, ranked in order of frequency, with post-ERCP pancreatitis having the highest susceptibility to ERCP-related mortality[31-33]. Some rare or less frequent complications associated with ERCP include cardiovascular events, pneumothorax, and hepatic hematoma.

FURTHER ADVANCEMENTS IN THE IMAGING DIAGNOSTICS OF PANCREATOBILIARY DISEASES

Peroral cholangiopancreatoscopy

Peroral cholangiopancreatoscopy (POC) is an advanced endoscopic technique that has transformed the diagnosis and treatment of biliary and pancreatic disorders[32]. This procedure combines the principles of endoscopy and fluoroscopy to visualize and manage bile and pancreatic ducts. Therefore, it has diagnostic and therapeutic capabilities[32]. POC can be performed using two primary technologies: An ultraslim endoscope and disposable POC, such as the SpyGlass system (Boston Scientific, United States). The ultraslim endoscope is directly inserted into the bile or pancreatic ducts after preliminary endoscopic papillotomy, thereby facilitating high-resolution imaging and providing therapeutic intervention capabilities via its working channel[32]. The Spy Glass system, which is inserted through the instrumental channel of a duodenoscope, also requires preliminary papillotomy and offers real-time, high-definition imaging that can perform targeted biopsies and other procedures[33]. Both technologies have similar advantages. For example, they can perform direct visualization and access the ductal system. However, they are technically demanding and carry risks such as bleeding and pancreatitis, which are associated with papillotomy. These methods were selected based on the clinical situation, patient anatomy, and available expertise and equipment. Despite this innovation, POC has several challenges and limitations that must be considered in clinical practice.

One of the primary advantages of POC is its ability to provide real-time visualization of the bile and pancreatic ducts, thereby enabling accurate diagnosis and intervention, which is particularly beneficial in identifying and treating conditions such as bile duct stones, strictures, and tumors[33]. The procedure involves insertion of an endoscope via the mouth and into the duodenum, followed by placement of a catheter in the bile or pancreatic duct. A contrast dye was injected and radiographic imaging was used to visualize the ducts[34]. This technique allows for detailed mapping of ductal anatomy, thereby facilitating the removal of stones, placement of stents, and dilation of strictures[34].

Nonetheless, POC faces certain drawbacks. One major hurdle is the intricate nature of this process. The success of POC is significantly based on the operator’s skill and experience, with a steep learning curve that can affect the procedural success rate[32]. Inexperienced operators may encounter difficulties in navigating the endoscope and catheter, potentially leading to incomplete examinations or complications. In addition, the procedure requires high dexterity and familiarity with the equipment, which further underscores the need for specialized training and experience[32].

The risk of complications is another challenge associated with POC[33]. Although the procedure is generally safe, it is associated with a risk of adverse events, such as pancreatitis, bleeding, infection, and perforation. The incidence of postprocedural pancreatitis is particularly concerning, occurring in a small but significant number of cases[35]. This risk requires careful patient selection and prophylactic measures to prevent potential complications. Patients with a history of pancreatitis, complicated anatomy, or previous abdominal surgeries were at a higher risk. This may require additional precautions or alternative diagnostic approaches[36]. Moreover, technological limitations can affect the efficacy of POC. Imaging quality and procedure success are significantly dependent on the technological capabilities of the endoscopic equipment used[32]. Variations in the resolution of imaging systems and the performance of fluoroscopic units can affect the clarity of images, potentially compromising diagnostic accuracy. Moreover, the high cost of advanced endoscopic and imaging equipment can limit the availability of POC in some healthcare settings, particularly in resource-constrained environment[36].

Furthermore, although POC is a powerful diagnostic tool, its therapeutic role is limited by anatomical and physiological challenges. The accessibility of certain ductal segments can be challenging, particularly in patients with complex anatomy or those who have undergone previous surgeries that have altered the ductal system[37]. In addition, the procedure may be less effective in patients with extensive ductal strictures or severe inflammation who are at a higher risk of complications and have a lower likelihood of successful intervention[38]. This limitation often requires adjunctive procedures or alternative imaging modalities for a comprehensive diagnosis and treatment plan. Despite these challenges, POC continues to evolve, with technological advancements and techniques enhancing its capabilities. Table 1 presents a comparative review of the POC, ERCP, and EUS.

Table 1 Comparative overview of proral colangioscopy, endoscopic retrograde colangiopancreatography, and edoscopic utrasound.

Aspect	Endoscopic utrasound	Endoscopic retrograde cholangiopancreatography	Peroral colangioscopy
Technique involved	Combines endoscopy and ultrasonography	Combines endoscopy and fluoroscopy; use of contrast dye and radiography	Insertion of an endoscope via the mouth using advanced imaging
Purpose	Primarily diagnostic	Diagnostic and therapeutic	Detailed diagnostic imaging and therapeutic interventions
Procedure	Use of an endoscope with an ultrasound probe for internal imaging	Injection of contrast dye into the ducts, with radiographic images taken with real-time guidance	High-resolution visualization of the bile and pancreatic ducts
Imaging quality	High-resolution ultrasound imaging	Real-time fluoroscopic guidance	High-resolution; detailed visualization
Technology	Ultrasound-guided fine-needle aspiration biopsy	Fluoroscopy for real-time imaging	Often incorporates digital and high-resolution imaging systems
Primary clinical uses	Pancreatic cancer detection and staging	Diagnosing and treating bile duct obstructions	High-resolution imaging of the bile and pancreatic ducts
	Chronic pancreatitis and biliary disease evaluation	Gallstone removal, stent placement, and stricture dilation	Identifying small lesions and ductal changes
	Evaluation and sampling of submucosal lesions	Stricture and tumor management	Stone removal, stent placement, and dilation of strictures
Advantages	Minimally invasive with high-resolution imaging	Combined diagnostic and therapeutic capabilities	Enhanced imaging quality
	Guided biopsies, including extraluminal targets	Immediate symptom relief and treatment	Reduced radiation exposure
	Ability to reach and biopsy beyond the GI tract	Proven efficacy with a high success rate	Improved diagnostic accuracy via digital innovations
Risks and limitations	Procedure-related risks (e.g., bleeding, infection, and perforation)	Higher rates of complications (e.g., pancreatitis, infection, and bleeding)	Technically demanding; requiring specialized training
	Complementary to ERCP in therapeutic procedures	Radiation exposure from fluoroscopy	Operator dependency affecting outcomes
	Technically demanding	Technological limitations based on the equipment	Anatomical challenges in accessing the ducts
Patient selection	Excellent for staging, lesion assessment, and biopsies	Ideal for immediate therapeutic intervention during diagnosis	Useful for detailed diagnostic evaluations
	Complementary to ERCP in addressing limitations	Suitable for several biliary and pancreatic conditions	Challenges with a complex anatomy
Therapeutic role	Complementary to ERCP in therapeutic procedures	Notable therapeutic capabilities (stone removal, stenting)	Stone removal, stent placement, and dilation
Biopsy capability	Combines endoscopy with ultrasonography	Can collect small tissue samples (biopsies)	Can be performed under direct visualization
Invasiveness	Primarily diagnostic	More invasive with a higher risk of complications	Less invasive than surgery
Imaging vs therapeutics	Endoscope with an ultrasound probe for internal imaging	Balanced diagnostic and therapeutic functions	Useful for high-resolution imaging of small lesions and ducts
Complications	High-resolution ultrasound imaging	Higher risk of pancreatitis, infection, and perforation	Risk of infection, bleeding, and perforation

ERCP: Endoscopic retrograde cholangiopancreatography.

Balloon enteroscopy

Since its clinical introduction in 2003, balloon enteroscopy or balloon-assisted enteroscopy has become an essential technique for managing patients with surgically altered anatomy, such as those with a Roux-en-Y loop or an incomplete colon, owing to incomplete conventional colonoscopy[39,40]. Traditional endoscopy often has limitations in these cases owing to the length and complexity of the altered intestinal tract, which makes it difficult to reach the bile ducts or pancreatic ducts. Balloon enteroscopy, including single- and double-balloon enteroscopy, addresses this challenge by using balloons to anchor the enteroscope and allowing it to advance through complex anatomy[41]. This method is effective for diagnostic and therapeutic interventions such as stone removal, placement, and biopsy. Balloon enteroscopy complements small-bowel imaging modalities such as capsule endoscopy, abdominal ultrasonography, MRI, and CT scan[42]. The ability to navigate through an altered anatomy with balloon enteroscopy has significantly expanded the reach and utility of endoscopic procedures in patients with complex surgical histories, thereby improving the diagnostic capabilities and treatment outcomes[43,44].

Balloon enteroscopy is an effective tool for deep small-bowel exploration and intervention. However, this procedure has several limitations and challenges. The procedure is technically demanding and requires specialized training, as the insertion and manipulation of the endoscope via the small intestine can be complex, particularly in patients with an altered anatomy or adhesions[45]. It is also time-consuming and often requires prolonged procedures to reach deeper bowel sections. Patient discomfort and the need for sedation or anesthesia can be significant in addition to procedural risks. In addition, balloon enteroscopy may result in complications such as bowel perforation, bleeding, or pancreatitis, particularly during therapeutic interventions[46]. Despite these limitations, its diagnostic yield and therapeutic potential for small-bowel disease make it a valuable endoscopic procedure.

Optical biopsy and enhanced imaging techniques

Advancements in endoscopic techniques have significantly improved the diagnosis and management of pancreaticobiliary diseases, which facilitates more precise and real-time tissue evaluation. Enhanced imaging technologies, such as laser confocal endomicroscopy, can promote in vivo microscopic imaging of cellular architecture and vascular patterns during the procedure, thereby aiding in the early detection of malignancies and other pathologies[47-49]. Narrow band imaging enhances the visualization of mucosal and vascular structures, thereby improving the identification of neoplastic changes, particularly in the bile and pancreatic ducts[50-52]. Other digital enhancement technologies, such as flexible spectral imaging color enhancement and I-scan, further improve contrast and clarity, thereby allowing for better detection of subtle lesions[53].

High magnification techniques, such as ZOOM endoscopy[54-58] and autofluorescence imaging[59-62], which highlight abnormal tissue fluorescence, also improve the diagnostic accuracy. Optical coherence tomography provides cross-sectional imaging to assess deeper tissue layers. Meanwhile, endocytoscopy obtains ultra-high magnification images for real-time cellular analysis[59,60].

ADVANCEMENTS IN COMPUTER TOMOGRAPHY SCAN FOR THE DIAGNOSIS OF BILIOPANCREATIC DISEASES

CT technology has significantly advanced and revolutionized medical diagnostic imaging. Dual-energy CT (DECT), which utilizes two energy levels to acquire images and offers enhanced tissue characterization and improved contrast resolution, is a notable innovation[61-63]. DECT technology has been valuable in detecting kidney stones, gout, and vascular abnormalities, and in differentiating various tissue types[64]. Similarly, Spectral CT simultaneously captures images at multiple energy levels, thereby providing detailed tissue composition information[65]. This innovation has improved lesion detection and differentiation and reduced artifacts from metal implants, enhancing diagnostic accuracy in complex cases[66].

The development of iterative reconstruction techniques is another significant advancement[67-71]. These advanced algorithms iteratively refine the image reconstruction process, reduce noise, and improve overall image quality. This has significantly reduced the radiation dose without compromising the image quality, which is particularly beneficial in pediatric imaging and follow-up scans[72-74]. In addition, integrating AI and ML algorithms further enhanced CT scan imaging. These technologies allow automated lesion detection, improved image quality, reduced scan times, and enhanced diagnostic accuracy via AI-assisted interpretation.

The development of AI has revolutionized several medical fields and its impact on imaging modalities for biliopancreatic diseases is particularly significant. Biliopancreatic diseases encompass various conditions affecting the biliary system, pancreas, and the surrounding structures. These conditions are often complex and require precise diagnostic tools to improve the patient outcomes. Owing to its ability to analyze large datasets and recognize patterns, AI has significantly enhanced the diagnostic accuracy, efficiency, and predictive capabilities of imaging techniques in this domain.

ADVANCEMENTS IN MRCP

MRCP is a non-invasive imaging technique used to visualize the biliary and pancreatic ducts[70]. Unlike traditional cholangiography, MRCP employs MRI to produce detailed images without contrast injection into the ducts. MRCP is particularly valuable in diagnosing conditions such as bile duct stones, strictures, tumors, and congenital abnormalities[71]. Furthermore, it is used to evaluate the pancreatic duct in conditions such as chronic pancreatitis or pancreatic tumors. The technique works by identifying the difference in fluid content between the bile and pancreatic ducts and surrounding tissues, thereby making these ducts appear bright on MRI images and the surrounding tissues remain darker.

One of the main advantages of MRCP is its non-invasive nature, which prevents the risks associated with invasive procedures such as ERCP. Moreover, it is free of ionizing radiation. Thus, it is safer for patients who require repeated imaging or pregnant women. MRCP provides high-resolution images that can help in the detailed assessment of the ductal anatomy and pathology, and it can be used in conjunction with other imaging modalities to enhance diagnostic accuracy. However, MRCP has certain limitations. It may not detect extremely small stones or early-stage tumors, and the image quality can be affected by patient movement or the presence of metallic implants, which can cause artifacts. Despite these limitations, MRCP is still a highly useful tool for non-invasive evaluation of the biliary and pancreatic ductal systems, thereby providing essential information for the diagnosis and management of various conditions.

High-resolution MRCP represents a significant advancement in imaging technology as it provides more detailed and precise images of the biliary and pancreatic ducts. This technique utilizes high magnetic field strengths, typically 3T MRI machines, along with advanced imaging protocols to achieve a greater spatial resolution[71]. High-resolution MRCP enables the visualization of finer details within the ductal system, which allows for the detection of small stones, subtle strictures, early-stage tumors, and other minute abnormalities that may be missed by standard MRCP. This level of detail is particularly valuable in preoperative planning and in evaluating complex cases in which precise anatomical information is crucial[72].

Functional MRCP is an emerging technique that combines traditional MRCP with dynamic imaging sequences to assess the physiological function of biliary and pancreatic ducts[73]. Unlike standard MRCP, which provides static images, functional MRCP captures the movement of bile and pancreatic fluids over time, thereby offering insight into the functional status of these ducts[74,75]. This technique is particularly useful for diagnosing functional disorders such as biliary dyskinesia and sphincter of Oddi dysfunction, where the flow of bile or pancreatic juice is abnormal. Secretin-enhanced MRCP is a common approach in functional MRCP, where the administration of secretin (a hormone that stimulates pancreatic secretion) increases the volume of pancreatic fluid, distends the ducts, and allows for dynamic assessment of their function[76]. Functional MRCP can provide valuable information about ductal motility and fluid dynamics, which are essential for diagnosing conditions that are not associated with structural abnormalities[77,78].

Diffusion-weighted imaging (DWI) is an advanced MRI technique that measures the diffusion of water molecules within the tissues. When used in combination with MRCP, DWI offers additional diagnostic information by evaluating the cellular environment and microstructure of the bile ducts, pancreatic ducts, and surrounding tissues[79]. DWI is particularly useful in distinguishing benign from malignant lesions, as malignant tissues typically exhibit restricted diffusion owing to their higher cellularity and altered tissue architecture[80,81]. This ability of DWI-MRCP to assess tissues at the molecular level makes it a valuable tool for non-invasive characterization of biliary and pancreatic strictures, masses, and other abnormalities. Moreover, DWI can help detect early-stage tumors and assess the extent of disease spread, thereby potentially reducing the need for invasive diagnostic procedures, such as biopsies[82].

AI IN ULTRASOUND IMAGING AND EUS

Integrating AI into ultrasound imaging has significantly improved the detection and characterization of biliopancreatic diseases[83]. AI algorithms can analyze ultrasound images to identify subtle changes that indicate early-stage disease. For example, AI-powered software can differentiate benign from malignant lesions in the pancreas by analyzing texture patterns and echogenicity that are not easily discernible to the human eye. Thus, it enhances the accuracy of ultrasonography in diagnosing pancreatic cancer and potentially leading to earlier detection and improved survival rates[84-86]. In addition, AI can assist in real-time image acquisition, guiding the operator to obtain optimal images, and reducing operator variability[86].

The diagnostic capabilities of AI integration into EUS have been enhanced by assisting in interpreting EUS images by automatically identifying and characterizing lesions[87]. For example, AI algorithms can differentiate benign from malignant pancreatic lesions with high accuracy, thereby improving endoscopists’ diagnostic confidence[84]. In addition, AI can guide FNA and FNB tissue acquisition procedures, optimize the sampling of lesions, and increase diagnostic yield[88]. Moreover, AI can be used to develop predictive models based on the EUS findings. By analyzing large datasets of EUS images and associated clinical outcomes, AI can identify the patterns and predictors of disease progression, which helps in risk stratification and individualized treatment planning.

AI in CT scan and MRI

CT is a cornerstone in the imaging of biliopancreatic diseases owing to its high spatial resolution and ability to provide detailed cross-sectional images. AI applications in CT scans have focused on automating image analysis and improving the diagnostic accuracy. Deep learning algorithms can be trained to recognize and precisely segment pancreatic tumors, cysts, and other abnormalities[89]. Furthermore, these algorithms can analyze vascular involvement in pancreatic tumors, which is crucial for surgical planning. Moreover, AI can help detect incidental findings such as small pancreatic cysts and gallstones, which might be overlooked during routine scans[90,91]. AI-enhanced CT imaging also reduces radiation exposure. By optimizing image acquisition protocols and enhancing image reconstruction, AI can maintain high image quality while lowering the radiation dose, thereby decreasing patient risk[91]. MRI provides excellent soft tissue contrast, thereby making it a valuable tool for evaluating biliopancreatic diseases. AI applications in MRI have focused on improving image acquisition, enhancing image quality, and automating image interpretation[91]. AI algorithms can enhance MRI images by reducing noise and artifacts, thereby making the images more precise and accurate. This is particularly important for detecting small lesions or subtle changes in the biliary and pancreatic ducts. In addition, AI can accelerate MRI acquisition times, making the procedure more comfortable for patients and increasing the throughput in clinical settings. Furthermore, AI plays an important role in the interpretation of MRI findings. For example, ML models can analyze MRI sequences to identify and classify pancreatic cysts based on imaging characteristics. This aids in differentiating benign cysts from those with malignant potential, guiding clinical management, and reducing unnecessary interventions. Several clinical trials at various stages have emphasized the role and application of AI in imaging and diagnostic modalities. Tables 2 and 3[92-114] present a summaries of clinical trials and their statuses and stages, as well as the AI modalities used in the diagnosis of biliopancreatic diseases. The current clinical trials on the application of AI in the imaging of biliopancreatic disease are provided in (Supplementary Table 2).

Table 2 Summary of artificial intelligence-based prediction models for computed tomography scan in clinical studies.

Clinical data availability	AI agorithm	Equipment	Reference sandard	Outcome masured	AUC	Ref.
With clinical data	Boruta, gradient-boosting classifier	Siemens, GE	Surgical resection	Residual ALN metastasis	0.866
	Lasso regression	Philips	Surgical resection	SLN metastasis	0.95	[93,94]
	CNN-fast and CNN	GE, Philips	Surgical resection	SLN metastasis	0.817
Without or insufficient clinical data	DCNNs	18FDG-PET/CT (Philips, GE)	Surgical resection	ALN metastasis	0.868
	DA-VGG19	GE, Philips	Surgical resection	ALN metastasis	0.9694
	DT, RF, NB, SVM, ANN	Philips	Surgical resection	ALN metastasis	0.86
	XGBoost	18FDG-PET/CT (GE)	Surgical resection	ALN metastasis	0.89

AI: Artificial intelligence; ALN: Axillary lymph node; ANN: Artificial neural network; AUC: Area under the curve CT: Computed tomography; DA: Deformable attention; DCNNs: Deep convolutional neural networks; DT: Decision tree; RF: Random forest; NB: Naïve Bayes; SVM: Support vector machine; 18FDG-PET: Fluorodeoxyglucose positron emission tomography.

Table 3 AI-Assisted based prediction models for magnetic resonance imaging models.

Clinical data availability	AI algorithm	Equipment	Reference standard	Outcome measured	AUC	Ref.
With clinical data	SVM	1.5 T GE	Surgical resection	ALN metastasis	0.87
	SVM	3.0 T GE	Surgical resection	ALN metastasis	0.810
	RF	N/A	Surgical resection	ALN metastasis	0.91
Without or with insufficient clinical data	LDA, RF, NB, KNN, SVM	3.0 T Siemens	FNA or surgical resection	ALN metastasis	0.82
	SVM, KNN, and LDA	3.0 T Siemens	FNA or surgical resection	ALN metastasis	0.8615
	LDA	1.5 T Aurora	Surgical resection	ALN metastasis	0.812
	SVM, XGBoost	3.0 T GE	Surgical resection	ALN metastasis	0.83
	SVM	1.5 T Philips	Surgical resection	SLN metastasis	0.852
	CNN	1.5 T GE	18FDG-PET	ALN metastasis	0.91
	RF	1.5 T Philips	Surgical resection	SLN metastasis	0.868
	Lasso regression	1.5 T Siemens	Surgical resection	ALN metastatic burden	0.81

AI: Artificial intelligence; ALN: Axillary lymph node; ANN: Artificial neural network; AUC: Area under the curve; CNN: Convolutional neural network; DA: Deformable attention; DCNNs: Deep convolutional neural networks; DT: Decision tree; FNA: Fine-needle aspiration; KNN: k-nearest neighbors; LDA: Linear discriminant analysis; NB: Naïve Bayes; RF: Random forest; SLN: Sentinel lymph node; SVM: Support vector machine; 18FDG-PET: Fluorodeoxyglucose positron emission tomography.

USE OF BIOMARKERS IN THE DIAGNOSIS OF BILIOPANCREATIC DISEASES

CA 19-9 is among the most widely used biomarkers for pancreatic diseases, particularly pancreatic adenocarcinoma[115]. High CA 19-9 levels indicate pancreatic cancer. However, this marker has no specificity, as it can also be elevated in other conditions such as cholangiocarcinoma, pancreatitis, and benign biliary obstructions. Carcinoembryonic antigen, another marker often used in combination with CA 19-9, can also be elevated in pancreatic cancer. However, it is primarily used for colorectal cancer treatment[116,117]. For acute pancreatitis, high amylase and lipase levels are key indicators, with lipase being more specific and remaining elevated for a longer duration than amylase. In addition to these more commonly used markers, hormones such as insulin, C-peptide, and glucagon are valuable in diagnosing pancreatic neuroendocrine tumors, which can cause conditions such as hypoglycemia and hyperglycemia, attributed to excessive hormone secretion. The potential role of emerging biomarkers, such as microRNAs (miRNAs), in pancreatic cancer diagnosis is also being evaluated. Specific miRNAs, such as miR-21, miR-155, and miR-196a, are promising because of their stability in the blood and their involvement in cancer pathogenesis[118,119]. Furthermore, the detection of KRAS mutations, particularly via circulating tumor DNA (ctDNA), is becoming increasingly important for understanding the genetic profile of pancreatic tumors and facilitating their diagnosis and monitoring[120].

CA 19-9 is also a commonly used biomarker for biliary diseases, particularly cholangiocarcinoma. Nevertheless, its diagnostic utility is limited by its elevation in benign conditions such as cholangitis and biliary obstruction[121]. Alpha-fetoprotein is primarily used in hepatocellular carcinoma. However, it can also be elevated in combined hepatocellular cholangiocarcinoma and gallbladder carcinoma[122,123]. High bilirubin levels often indicate bile duct obstruction, which can occur in conditions such as gallstones, cholangitis, and bile duct tumors. Enzymes such as alkaline phosphatase and gamma-glutamyl transferase are useful for the diagnosis of biliary obstruction and cholestatic liver diseases, including primary biliary cholangitis and primary sclerosing cholangitis.

Other markers such as IgG4 are associated with autoimmune pancreatitis and IgG4-related sclerosing cholangitis, which are conditions that can mimic malignancy but respond well to steroid therapy[124,125]. In addition, mucin proteins, such as MUC1 and MUC5AC, are associated with biliary tract cancers, including cholangiocarcinoma and gallbladder cancer. High MUC1 and MUC5AC levels indicate the presence of malignancy. Fibroblast growth factor 19 is an emerging biomarker, and its role in cholangiocarcinoma is being evaluated, considering its involvement in bile acid metabolism and potential link to tumor growth[126]. These biomarkers play an important role in the early detection, diagnosis, and management of biliopancreatic diseases, thereby providing essential insights into their presence and progression.

Research on miRNAs has also progressed significantly, with specific miRNA profiles being identified as potential markers for the early detection and prognosis of pancreatic cancer and other biliary diseases. The stability of miRNAs in the blood and their role in gene regulation make them promising non-invasive biomarkers. Another notable advancement is the study of glycan structures, such as those found in MUC1 and MUC5AC, which are overexpressed in biliary and pancreatic cancers[127,128]. These glycan alterations can be detected in serum or tissue samples, and their potential for early detection and use as indicators of prognosis is being explored[129].

Exosomal biomarkers have emerged as a promising area of research. In pancreatic cancer, specific exosomal markers, such as glypican-1 and certain miRNAs, can distinguish patients with cancer from those with benign conditions or healthy controls, thereby offering a novel avenue for early detection and treatment response monitoring[130-132]. Advancements in proteomic and metabolomic technologies have enabled the identification of novel proteins and metabolic biomarkers associated with biliopancreatic diseases. These approaches can identify complex biomarker patterns that may be correlated with disease presence, stage, and response to treatment, thereby offering a more individualized approach for diagnosis and management.

The integration of digital polymerase chain reaction and next-generation sequencing technologies into biomarker research has also improved the detection sensitivity of low-abundance biomarkers, such as ctDNA and miRNAs. These technologies allow the quantification of minute genetic changes, thereby enabling the detection of early-stage cancers and minimal residual disease after treatment. In addition, there have been advancements in the detection of autoantibodies against tumor-associated antigens, which can be early markers of pancreatic cancer and can facilitate earlier diagnosis before the disease becomes clinically apparent.

Genetic and epigenetic biomarkers have also undergone significant advancements, particularly in the understanding of epigenetic changes, such as DNA methylation and histone modification, in pancreatic and biliary cancers. These biomarkers can predict disease susceptibility, prognosis, and response to therapy, thereby offering valuable insights into individualized treatment strategies. With the increased use of immunotherapy in cancer treatment, research on biomarkers that can predict the response to immunotherapy, such as PD-L1 expression and tumor mutational burden, is ongoing, with the goal of identifying patients who could benefit from immune checkpoint inhibitors[133,134]. These advancements in biliopancreatic disease biomarkers are improving the precision of diagnosis, promoting earlier detection, and offering new avenues for personalized treatment approaches, ultimately improving patient outcomes and providing more targeted therapeutic options.

Challenges in biomarker research include the standardization and validation of biomarkers. Large-scale prospective studies should be performed to validate the clinical utility of biomarkers and establish standardized protocols for their use in clinical practice[135-138]. Despite these challenges, the diagnosis and treatment of biliopancreatic diseases can potentially be transformed by advancements in biomarkers. Biomarkers can improve patient outcomes and quality of life by improving early detection, risk assessment, and treatment monitoring (Supplementary Table 3). The stratification of clinical trials assessing various key biomarkers in the diagnosis of biliopancreatic diseases are provided in Table 4.

Table 4 Summary of key biomarkers and their diagnostic performance.

Biomarker	Primary use	Sensitivity	Specificity	Detection method	Clinical applications	Limitations
CA 19-9	Pancreatic cancer	80%-90%	70%-80%	Enzyme-linked immunosorbent assay (ELISA)	Used in monitoring disease progression and treatment response	Elevated in benign conditions; lacks specificity
KRAS mutations	Pancreatic cancer	High	High	Polymerase chain reaction (PCR); next-generation sequencing (NGS)	Identifies high-risk patients, guides targeted therapies	Limited sensitivity in early-stage cancer
Amylase/lipase	Acute pancreatitis	> 90%	70%-80%	Serum biochemical assays	First-line test for diagnosing acute pancreatitis	Cannot distinguish between acute and chronic cases
Alpha-fetoprotein	Hepatocellular and biliary carcinoma	60%-70%	80%-90%	ELISA, chemiluminescent immunoassay	Used in screening for hepatocellular carcinoma	Limited specificity in biliary malignancies
MicroRNAs (miR-21, miR-196a)	Early detection of pancreatic cancer	85%	90%	Reverse transcription PCR (RT-PCR); RNA sequencing	Potentially noninvasive biomarker for early detection	Requires further validation and standardization

CA 19-9: Cancer antigen 19-9.

ADVANCEMENTS IN THE USE OF AI IN THE DIAGNOSIS OF BILIOPANCREATIC DISEASES

AI involves the use of complex computer algorithms to analyze and manipulate vast amounts of data to examine patterns and make predictions[12,87]. Advancements in the use of AI in diagnosing biliopancreatic diseases have significant potential for enhancing diagnostic accuracy, efficiency, and personalized patient care. AI technologies, particularly ML algorithms, have been integrated into various aspects of medical imaging, pathology, and data analysis to improve the detection, characterization, and monitoring of biliopancreatic diseases[12].

Imaging is a key area in which AI has had substantial impact. AI algorithms can analyze complex imaging data with high precision using modalities such as CT, MRI, and EUS. These algorithms can detect subtle abnormalities that may be missed by the human eye, thereby improving the early detection rates. For example, AI-powered tools can automatically identify pancreatic lesions and classify them based on their malignant potential, which is important for early intervention and better patient outcomes.

In addition to enhancing image interpretation, AI has improved the efficiency of imaging workflows with AI algorithms used to automate routine tasks such as organ segmentation and quantification, which reduces the workload for radiologists and allows them to focus on more complex cases[139]. This accelerates the diagnostic process and ensures measurement consistency and accuracy. AI has also been used to analyze pathological data. For example, ML models can examine histopathological slides to identify malignant cells, differentiate various tumors, and predict disease progression[140]. These models can learn from vast datasets, which can continuously improve their accuracy and provide valuable insights into the pathological characteristics of biliopancreatic diseases[140].

Moreover, AI-driven liquid biopsy analysis is promising for non-invasive detection of biliopancreatic cancers. AI algorithms can detect genetic mutations and molecular alterations associated with cancer by analyzing ctDNA, RNA, and other biomarkers in blood samples. This approach offers a less invasive alternative to traditional biopsy, thereby enabling earlier diagnosis and real-time monitoring of disease progression. The predictive analytics capabilities of AI represent another area of advancement. AI can predict disease outcomes and treatment responses by analyzing large datasets that include the demographic characteristics of patients, medical history, imaging findings, and genetic information. This allows for more personalized treatment plans based on the specific needs of each patient, ultimately improving clinical outcomes.

Despite these advancements, the widespread adoption of AI for the diagnosis of biliopancreatic diseases still faces several challenges. Therefore, it is important to ensure the robustness and generalizability of AI models across diverse patient populations[141]. In addition, integrating AI tools into clinical workflows requires collaboration between technologists, clinicians, and regulatory bodies to address issues related to data privacy, ethical considerations, and clinical validation[142,143].

RECOMMENDATIONS

Further research must be performed to identify novel biomarkers and imaging techniques that require collaborative effort among researchers, clinicians, and industry partners. Standardized protocols for biomarker testing and imaging interpretation should be established to ensure consistency and reliability, with large-scale prospective studies validating the clinical utility of these advancements[144,145]. To improve the diagnostic accuracy and implement personalized treatment strategies, healthcare institutions should integrate biomarkers and advanced imaging modalities into their diagnostic algorithms for biliopancreatic diseases.

The need for optimal and efficient competency among healthcare professionals is essential. Therefore, healthcare professionals should undergo regular education and training regarding the latest advancements in biomarkers and imaging modalities to ensure their optimal utilization in clinical practice. A patient-centric approach should be adopted when selecting diagnostic modalities and treatment strategies, considering individual patient characteristics and preferences[146-148]. Integrating AI algorithms into biomarker analysis and imaging interpretation can enhance diagnostic accuracy and efficiency, which requires investment in AI technologies.

A multidisciplinary approach involving radiologists, gastroenterologists, surgeons, oncologists, and pathologists is essential for optimal management of biliopancreatic diseases. Collaboration among these professionals can improve the diagnostic and treatment outcomes. Cost-efficacy analyses should be conducted to evaluate the economic impact of integrating biomarkers and imaging modalities into clinical practice, thereby helping healthcare institutions to allocate resources efficiently. By implementing these recommendations, healthcare institutions can enhance the diagnosis and management of biliopancreatic disease, leading to better patient outcomes and quality of life.

CONCLUSION

Advancements in biomarkers, imaging modalities, and other diagnostic technologies have collectively revolutionized the diagnosis and management of biliopancreatic diseases. Biomarkers, such as CA 19-9, KRAS mutations, and inflammatory markers, offer valuable insights into disease progression and treatment response. Imaging modalities such as CT, MRI, EUS, and ERCP can provide detailed anatomical and functional information, thereby helping in the early detection and accurate staging of biliopancreatic diseases. Other advancements, including genetic testing, liquid biopsies, and AI, can further enhance the diagnostic accuracy and personalized treatment strategies.

Despite these promising developments, the implementation of AI technologies across different healthcare settings faces substantial challenges. High technological costs and the need for specialized infrastructure limit widespread adoption, particularly in resource-limited regions. Furthermore, the effectiveness of AI models is contingent on high-quality data, requiring the implementation of robust data governance policies for security and privacy. Addressing these hindrances needs investment in training programs to equip healthcare professionals with the required skills to effectively utilize AI-enhanced diagnostics.

Another critical issue is the standardization of biomarkers. Although biomarkers, such as CA 19-9 and KRAS mutations, have diagnostic utility, variability in laboratory methodologies and interpretation criteria hampers their widespread clinical application. Standardized protocols and validation frameworks are essential for enhancing reliability and comparability across different institutions. Furthermore, regulatory bodies must establish guidelines to ensure the clinical integration of AI-assisted biomarker analysis while maintaining transparency and accountability in AI-driven decision-making.

Future efforts should entail developing cost-effective AI solutions tailored to diverse healthcare settings, implementing standardized biomarker validation protocols, and fostering interdisciplinary collaboration to optimize the clinical utility of these technologies. By addressing these challenges, AI- and biomarker-based diagnostics can achieve their full potential in improving patient outcomes and advancing pancreaticobiliary disease management.