Artificial intelligence in pancreatitis: A narrative review on advancing precision diagnosis, prognosis, and therapeutic strategies

Abstract

Pancreatitis poses persistent diagnostic and therapeutic challenges due to its heterogeneous clinical presentation, variable disease course, and lack of targeted interventions. Conventional tools, such as serum enzymes, cross-sectional imaging and clinical scoring systems, often exhibit limited sensitivity and prognostic value, especially during early or atypical stages. Moreover, therapeutic development remains slow, with limited progress toward personalized or mechanism-based strategies. These limitations highlight a critical need for integrative data-driven approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance clinical decision-making in pancreatitis. This narrative review synthesizes recent progress in AI applications across three domains. First, AI-enabled diagnostic platforms incorporating radiomics, deep learning-based imaging analysis, and biomarker optimization have improved early detection and differentiation of pancreatic diseases. Second, AI-driven prognostic models now allow real-time severity prediction, complication forecasting, and recurrence risk assessment, some of which have been deployed in hospital information systems for intensive care units and mortality risk triage. Third, AI-assisted drug discovery and network pharmacology, particularly in combination with traditional Chinese medicine, have revealed novel therapeutic opportunities. Despite encouraging developments, challenges remain in data standardization, model transparency and clinical validation. A multidisciplinary strategy integrating omics data, longitudinal monitoring and pharmacological modeling may help bridge current gaps and advance precision medicine in pancreatitis care.

Key Words: Pancreatitis; Artificial intelligence; Machine learning; Deep learning; Diagnosis; Prognosis management; Therapeutics; Network pharmacology; Traditional Chinese medicine

Core Tip: This narrative review summarizes recent advances in artificial intelligence (AI) applications for pancreatitis. It covers AI-enhanced diagnosis through imaging and biomarker analysis, real-time prognostication using machine learning models, and AI-assisted therapeutic innovation, including integration with network pharmacology and traditional Chinese medicine. The review also highlights clinically implemented models and discusses current limitations and future directions for AI deployment in pancreatitis care.

INTRODUCTION

Pancreatitis is an inflammatory disorder of the pancreas. It encompasses a broad clinical spectrum ranging from acute, self-limiting episodes to chronic, progressive forms that lead to irreversible structural damage and functional decline[1,2]. Acute pancreatitis (AP) is among the most common gastrointestinal emergencies worldwide, with rising incidence and significant morbidity and mortality, particularly in severe cases[3]. On the other hand, chronic pancreatitis (CP) poses a substantial burden due to persistent abdominal pain, exocrine and endocrine insufficiency, and increased malignancy risk[4]. The diverse etiologies, overlapping clinical features, and unpredictable disease trajectories make timely diagnosis and optimal management challenging in both acute and chronic forms[5].

Traditional clinical tools, including serum enzyme assays (e.g., amylase, lipase), imaging modalities [contrast-enhanced computed tomography (CT), magnetic resonance imaging (MRI), endoscopic ultrasound (EUS)], and scoring systems such as Ranson, Bedside Index of Severity in Acute Pancreatitis, and Accuracy of Acute Physiology and Chronic Health Evaluation (APACHE) II, have provided foundational frameworks for diagnosis and risk stratification[6-8]. However, these conventional approaches have well-known limitations. Enzyme elevations can be nonspecific (for example, other abdominal issues can raise amylase), and mild or very early pancreatitis can be missed[9]. Imaging may not always distinguish inflammation from other processes, and factors like inter-observer variability or the need for radiation in CT can hinder accuracy[10,11]. While useful, scoring systems are based on a few static parameters and often fail to capture the full complexity of each patient’s condition[12,13]. In short, the sensitivity and specificity of these methods in early or atypical cases are imperfect, and patient subgroups remain hard to identify. Beyond supportive care, therapeutic options remain suboptimal, especially in severe or recurrent pancreatitis, and drug development in this field has been notably slow[14].

Recent advances in artificial intelligence (AI), encompassing machine learning (ML), deep learning (DL), natural language processing, and network pharmacology, offer potential tools to address these limitations[15-17]. AI models can analyze large-scale, heterogeneous datasets, including clinical records, medical images, laboratory values, genomic data, and real-time physiological signals, to identify complex, nonlinear patterns that elude conventional statistical methods[18,19]. For instance, a well-trained model may be able to identify early signals of impending organ failure or highlight imaging features too subtle for the human eye[20-22]. In turn, these insights can enable earlier and more precise diagnosis, better risk prediction for each patient, and even suggest tailored treatment strategies. In essence, AI tools can act as advanced decision-support, augmenting clinical judgment with data-driven intelligence.

This review synthesizes the latest developments in AI applications for both acute and CP, structured around three core domains: (1) Precision diagnostics; (2) Dynamic prognostication; and (3) Integrative therapeutic innovation. We emphasize clinically relevant use cases, cutting-edge modeling techniques, and interdisciplinary approaches, including the incorporation of traditional Chinese medicine (TCM) and omics technologies to provide a comprehensive outlook on the emerging era of intelligent pancreatitis care.

LITERATURE SEARCH STRATEGY

We performed a broad search of Englishlanguage literature published from January 1, 2005 to June 30, 2025 using the following databases: PubMed, Web of Science, and ScienceDirect. The search combined Medical Subject Headings and free-text terms as follows: “Pancreatitis” AND (“artificial intelligence” OR “machine learning” OR “deep learning”) AND (“diagnosis” OR “prognosis” OR “drug discovery” OR “network pharmacology”). Reference lists of seminal articles were also reviewed to capture any additional relevant studies. After removing duplicates, 2066 unique records were screened by title and abstract to exclude non-English publications, conference abstracts, case reports, and studies unrelated to AI applications in pancreatitis. Full texts of 580 remaining articles were then assessed for relevance, yielding 72 final inclusions comprising original research papers and high-quality reviews focused on AI-driven diagnostics, prognostics, or therapeutics in pancreatitis.

In addition, we integrated the key information of 22 representative AI model studies with high accuracy [e.g., area under the curve (AUC) > 0.83] in diagnosis and prognosis into Table 1, including prediction target, patient population, AI method, input data type, performance indicator (e.g., AUC, accuracy), and clinical translation stage. While not a full systematic review, this approach follows essential PRISMA principles[23], such as clear search terms, predefined inclusion criteria, and transparent screening steps, to ensure rigor and reproducibility in our narrative synthesis.

Table 1 Application of high-accuracy artificial intelligence models in the diagnosis and prognosis management of pancreatitis.

Application	Prediction target	AI methodology	Patient cohort	Input data type	Performance metrics	Stage of clinical translation	Ref.
Diagnosis	AP early diagnosis and risk stratification	XGBoost model	61894	Vital signs and lab measurements	AUC: 0.921	Experimental	[47]
		Logistic regression model	275	28 important radiomics features, lipase levels	AUC: 0.933	Experimental	[32]
	Pancreatic lesions detection and segmentation	V-Net model	144	Pancreatic volume. The split volume ratios	AUC: 0.854	Experimental	[31]
		SegFormer model	165	EUS images	AUC: 0.95	Validation phase	[41]
	Pediatric pancreas segmentation	PanSegNet	84	Imaging parameters, biologic sex, age at imaging, clinical diagnosis	Dice coefficient: Approximately 0.88	Validation phase	[37]
	Non-invasive diagnosis of PDAC and CP	DLR model	558	CEUS images	Training cohort AUC: 0.986. Internal validation cohort AUC: 0.978. External validation cohorts 1 AUC: 0.967. External validation cohorts 2 AUC: 0.953	Validation phase	[39]
			491	EUS images	AUC: 0.936	Validation phase	[40]
	Distinguish AIP from PDAC	SVM-RFE model	111	251 expert-designed features from 2D and 3D PET/CT images	AUC: 0.93 ± 0.01	Validation phase	[44]
		Random forest model	182	431 radiomics features. Two types of CT parameters with dual phase CT	AUC: 0.975	Validation phase	[45]
	AP severity early prediction	LightGBM model	215	Radiomics features	Training cohort AUC: 0.992. Validation cohort AUC: 0.965. Test cohort AUC: 0.894	Hospital IT integration	[33,64]
		CTA model	192	Age, gender, BMI, temperature, pulse, systolic blood pressure, WBC, CRP, albumin, calcium, APACHE II and BISAP, Balthazar grade, CTSI, MCTSI, EPIC score	Training cohort AUC: 0.853. Validation cohort AUC: 0.833	Validation phase	[51]
		Random forest model	648	Blood urea nitrogen, serum creatinine, albumin, HDL, LDL, calcium, glucose	Training cohort AUC: 0.89. Test cohort AUC: 0.96	Validation phase	[48]
			740	Age, gender, BMI, comorbidities, serum biochemical index, CT	Training cohort AUC: 0.969. Training cohort AUC: 0.961	Validation phase	[52]
Prognosis management	Mortality	XGBoost model	499	WBC count, hemoglobin, platelet count, serum creatinine, albumin	AUC: 0.881	Hospital IT integration	[61,64]
		ANNs model	337	Total bilirubin, creatinine, amylase, lipase, LDH	AUC: 0.769	Experimental	[72]
	Multiple organ failure	ANNs model	312	Age, hematocrit, serum glucose, BUN, serum calcium	AUC: 0.96 ± 0.02	Experimental	[62]
		SVM model	263	HCT, K-time, IL-6, creatinine	AUC: 0.840	Validation phase	[69]
	Complications	ANNs model	217	Pancreatic necrosis rate, LDH, oxyhemoglobin saturation	AUC: 0.859 ± 0.048	Experimental	[66]
		XGBoost model	334	APACHE II, IAP, PCT rank	AUC: 0.9193	Experimental	[67]
		GBDT model	1672	Age, vasopressors, mechanical ventilation, GCS	AUC: 0.985	Validation phase	[70]
	Recurrence	XGBoost model	531	TG levels, smoking, drinking, ANC	AUC: 0.779	Experimental	[79]
		CECT model	389	Age, etiology, CTSI, hospital stay, pancreatic necrosis	AUC: 0.941	Experimental	[76]

The key information of 22 representative artificial intelligence model studies with high accuracy in pancreatitis diagnosis and prognosis, including prediction target, patient population, artificial intelligence methodology, input data type, performance indicator (e.g., area under the curve), and clinical translation stage. AI: Artificial intelligence; AP: Acute pancreatitis; AUC: Area under the curve; EUS: Endoscopic ultrasound; PDAC: Pancreatic ductal adenocarcinoma; CP: Chronic pancreatitis; DLR: Deep learning radiomics; CEUS: Contrast-enhanced ultrasound; AIP: Autoimmune pancreatitis; SVM-RFE: Support vector machine with recursive feature elimination; 2D: Two-dimensional; 3D: Three-dimensional; PET: Positron emission tomography; CT: Computed tomography; LightGBM: Light gradient boosting machine; CTA: Computed tomography angiography; BMI: Body mass index; WBC: White blood cell; CRP: C-reactive protein; APACHE: Accuracy of Acute Physiology and Chronic Health Evaluation; BISAP: Bedside Index of Severity in Acute Pancreatitis; CTSI: Computed tomography severity index; MCTSI: Modified computed tomography severity index; EPIC: Early prognostic imaging classification; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; BP: Back propagation; LDH: Lactate dehydrogenase; ANNs: Artificial neural networks; BUN: Blood urea nitrogen; HCT: Hematocrit; IL-6: Interleukin-6; IAP: Intra-abdominal pressure; PCT: Procalcitonin; GBDT: Gradient boosting decision tree; GCS: Glasgow Coma Scale; TG: Triglycerides; ANC: Absolute neutrophil count; CECT: Contrast-enhanced computed tomography.

AI-ENHANCED PRECISION DIAGNOSTICS

Accurate diagnosis of pancreatitis (and its specific subtype) is crucial for guiding treatment. However, diagnosis can be difficult; symptoms like abdominal pain, nausea, and vomiting are nonspecific, and routine tests are imperfect[24]. Traditional markers such as serum amylase and lipase can increase in AP, but they can also be elevated for other reasons (e.g., intestinal obstruction or gallbladder disease)[25], and some severe cases of pancreatitis may even present with only mildly elevated enzymes[26]. Imaging modalities such as contrast-enhanced CT, MRI, and EUS provide more direct evidence of pancreatic inflammation and its complications, but they require expert interpretation and are limited by factors such as variable image quality, the need for contrast, or exposure to radiation[27-29]. For example, early AP may not show clear findings on CT, and subtle necrosis or fluid collections can be missed[27]. Even among specialists, diagnostic disagreement is common, especially when differentiating CP from pancreatic cancer[29]. Given these challenges, there is a growing need for tools that can enhance diagnostic precision and reduce subjectivity. AI offers promising solutions across various diagnostic modalities. To contextualize the current landscape, we provide a visual summary of key AI-driven approaches and representative algorithm types in Figure 1 and Table 1.

Figure 1 Schematic overview of high-accuracy artificial intelligence models for the precision diagnosis of pancreatitis and their clinical advantages. Artificial intelligence-enhanced diagnostic tools, encompassing machine learning and deep learning algorithms, integrate multimodal data, ranging from laboratory and biochemical markers to radiologic imaging, endoscopic ultrasound, and computed tomography angiography. Representative models include random forest, XGBoost, SVM-RFE, LightGBM, V-Net, SegFormer, deep learning radiomics, PanSegNet, and others. These approaches have demonstrated utility in improving diagnostic accuracy and efficiency, enabling early severity stratification, differentiating pancreatic neoplasms from chronic pancreatitis, and informing more effective allocation of healthcare resources. The implementation status of each model is indicated by color coding: Experimental (blue), validation phase (orange), or clinically deployed (green). AI: Artificial intelligence; LightGBM: Light gradient boosting machine; CTA: Computed tomography angiography; SVM-RFE: Support vector machine-recursive feature elimination; DLR: Deep learning radiomics.

Medical imaging and radiomics

AI has made rapid inroads into imaging diagnostics. DL models, especially convolutional neural networks (CNNs), excel at processing imaging data[30]. In pancreatitis, CNNs have been trained to recognize visual patterns on CT and MRI scans that correlate with disease severity or presence[31-33]. For example, automated segmentation networks (such as U-Net or V-Net architectures) can precisely outline the pancreas and any necrotic regions on a CT slice (patient cohort: 144; AUC: 0.854)[31]. These segmentations allow quantification of disease burden (e.g., calculating necrosis volume), which can improve early severity grading. Other studies extract radiomic features from scans (e.g., quantitative texture, intensity, and shape descriptors) and feed them into ML classifiers (patient cohort: 275; AUC: 0.933)[32]. One approach uses portal-phase contrast-enhanced CT images of the pancreatic and peripancreatic regions to extract hundreds of radiomic features, then applies a gradient-boosting algorithm or logistic regression to predict who will develop severe AP (SAP) (patient cohort: 215; AUC: 0.992)[33]. These models have shown promising accuracy in small cohorts, suggesting that subtle imaging signals can be harnessed before conventional readout. Importantly, AI-driven imaging can reduce dependence on subjective interpretation[34-36]. For instance, a CNN trained on a large set of MRI images can flag even mild pancreatic inflammation with consistency, acting as a second reader[34]. In practice, such tools could alert clinicians to early pancreatitis changes that might otherwise be overlooked. It is worth noting that most imaging-based AI tools have been trained on adult data. However, anatomical and contrast differences in children, such as smaller organ size, distinct imaging characteristics, and varying etiologies, warrant dedicated models. For example, PanSegNet, a recently developed transformer-augmented nn-UNet version trained exclusively on pediatric MRI, achieves Dice similarity scores of about 0.88 and expert-level performance in acute and chronic pediatric pancreatitis cohorts (patient cohort: 499)[37]. This contrasts with adult models, such as CT-based CNNs, with limited pediatric validation. Consequently, we advocate for age-specific AI pipelines or adaptive learning frameworks that incorporate pediatric imaging and outcome data, supported by rigorous cross-age validation and regulatory approval processes.

Advanced endoscopy and ultrasound

EUS and contrast-enhanced ultrasound are increasingly used for pancreatic evaluation because of their real-time visualization and lack of ionizing radiation (in the case of ultrasound)[38]. However, interpretation of EUS images is notoriously user-dependent, requiring significant expertise. AI is helping to standardize this. DL classifiers trained on thousands of EUS images can learn the visual patterns of CP, pancreatic masses, and other lesions[39-41]. In one reported example, an AI model applied to contrast-enhanced ultrasound images could help distinguish pancreatic ductal adenocarcinoma from benign CP (patient cohort: 558; AUC: 0.986)[39]. With AI assistance, radiologists improved their diagnostic accuracy: Even less-experienced readers achieved performance close to experts when aided by the model[40]. Real-time applications are also in development. Computer-aided detection systems using architectures like SegFormer (a type of visual transformer model) have been built to identify and segment pancreatic lesions on EUS video frames as the endoscope moves (patient cohort: 165; AUC: 0.95)[41]. These systems report high area under the receiver operating characteristic curve for detecting lesions and provide bounding boxes or outlines of suspicious areas. By highlighting abnormalities in real time, such AI tools could reduce missed lesions and decrease the need for repeat procedures.

Autoimmune pancreatitis and other special cases

Some forms of pancreatitis, like autoimmune pancreatitis (AIP), pose particular diagnostic challenges. AIP can mimic pancreatic cancer on imaging, and vice versa[42,43]. ML approaches have been applied here as well. For example, a support vector machine trained on radiomic features from 18-F-fluorodeoxyglucose-positron emission tomography/CT scans could distinguish AIP from pancreatic cancer with accuracy around 85%, outperforming traditional radiologist assessment (patient cohort: 111; AUC: 0.93 ± 0.01)[44]. Similarly, computer analysis of CT texture features has helped identify the characteristic imaging patterns of AIP (patient cohort: 182; AUC: 0.975)[45]. These examples illustrate that AI can pick up on nuanced image traits sometimes imperceptible to the naked eye to improve diagnostic certainty in difficult cases.

Laboratory and biochemical data

In addition to imaging, AI models can enhance diagnosis by integrating laboratory tests. Instead of relying on a single enzyme level, ML can combine dozens of lab values and clinical inputs at once[46-48]. For instance, studies have trained algorithms on admission blood work (amylase, lipase, white cell count, C-reactive protein, calcium, etc.) and demographics to predict whether a patient’s AP will become severe[46]. A notable example used the XGBoost algorithm on a large patient dataset (patient cohort: 61894), which achieved an AUC of about 0.92 for predicting SAP[47]. Other models, including random forests and neural networks, showed similar improved prediction of SAP when combining multiple biomarkers (patient cohort: 648; AUC: 0.89)[48]. AI can also dynamically assess lab trends: For example, temporal patterns in inflammatory markers or vital signs in the first 24 hours could be fed into recurrent neural networks to forecast worsening disease[49]. An interesting application is using unconventional biomarkers: One research group combined quantitative contrast-enhanced ultrasound imaging data with a serum biomarker (heat shock protein 70) and found that an AI classifier could automatically grade the extent of pancreatic necrosis[50]. These approaches demonstrate that AI can fuse biological markers with imaging for more sensitive diagnosis.

Integrated diagnostic models

Real-world diagnosis often requires synthesizing multiple data streams. Recent AI efforts capitalize on this by creating multimodal models that take in clinical scores, lab tests, and imaging features together[51-53]. For example, a decision tree algorithm was designed to combine standard severity scores (like APACHE II and Bedside Index of Severity in Acute Pancreatitis) with imaging scores (such as the Balthazar grading of CT or early prognostic imaging classification score for necrosis). This hybrid model achieved 100% specificity and 94.8% overall accuracy in early disease severity prediction, far exceeding any single score alone (patient cohort: 192; AUC: 0.853)[51]. Other teams have trained random forests on a full panel of inputs (e.g., patient demographics, serum markers, CT scan features, etc.) to stratify AP risk. These all-in-one classifiers typically outperform traditional univariate rules, offering earlier and more confident identification of patients who need intensive care (patient cohort: 740; AUC: 0.969)[52]. In practice, such AI-driven tools could run in the background of an electronic medical record: As soon as a new lab result or scan report arrives, the model could recalculate the patient’s risk and alert the care team if concerning patterns emerged. By integrating data in this way, AI helps ensure that no critical information is overlooked during the complex diagnostic process.

AI-DRIVEN DYNAMIC PROGNOSTICATION

Once pancreatitis is diagnosed, predicting its course becomes the next priority. Clinical management hinges on anticipating who will do well with standard care and who may rapidly deteriorate. Traditionally, scores like APACHE II or Ranson’s are used to estimate severity and mortality risk[54], but they are relatively crude and static. AI offers a more dynamic and nuanced approach to prognostication by processing longitudinal data and recognizing nonlinear interactions[55-57]. A summary of key AI-driven models and their prognostic applications is illustrated in Figure 2 and Table 1.

Figure 2 Representative artificial intelligence models for prognosis management in pancreatitis. A range of machine learning and neural network-based algorithms have been applied to predict recurrence, assess disease severity and mortality risk, and forecast complications such as acute respiratory distress syndrome, acute kidney injury, and multiple organ failure. Notable models include XGBoost, gradient boosting decision tree, back propagation, support vector machine, and feed-forward neural networks. These models, particularly when integrated into multi-omics platforms, enable individualized prognostication and support precision management strategies in clinical practice. The implementation status of each model is indicated by color coding: Experimental (blue), validation phase (orange), or clinically deployed (green). ARDS: Acute respiratory distress syndrome; AI: Artificial intelligence.

Predicting severity and mortality

SAP can escalate quickly, with organ failure and death rates around 20%-30% in the most critical patients[58]. AI models can help by analyzing early indicators and identifying high-risk individuals. ML algorithms have been trained on cohorts of AP patients to predict outcomes like intensive care unit (ICU) admission or 30-day mortality[59]. In comparative studies, modern models such as XGBoost and gradient boosting decision trees consistently outperformed traditional approaches[60]. For example, an XGBoost model analyzing admission vitals and labs achieved an accuracy of over 90% in forecasting 30-day mortality in SAP patients (patient cohort: 499; AUC: 0.881)[61]. Moreover, an artificial neural network achieved 96% accuracy and 98% specificity in predicting in-hospital mortality (patient cohort: 312; AUC: 0.960 ± 0.020), highlighting the potential of DL in real-time mortality risk assessment[62]. Notably, AI can continuously update risk as new data arrive. Dynamic forecasting models can take time-series inputs (like hourly urine output, blood pressure, or lab trends) to revise severity predictions in real time[63]. This approach enables clinicians to adapt treatment plans in real time. For example, escalating therapy when early warning signs arise or withholding unnecessary interventions if the patient’s trajectory appears favorable. In fact, a recent study integrated LightGBM and XGBoost models into the hospital information system for AP patients, enabling real-time prediction of sepsis, ICU admission, and mortality[64].

Forecasting complications

Beyond overall mortality, predicting specific complications is equally important. Organ failure (respiratory, renal, cardiovascular) is a leading cause of death in SAP[65]. AI systems can warn of impending organ dysfunction by recognizing complex biomarker signatures[66-71]. For example, neural network models have been reported to predict complications such as multiorgan failure with high specificity and sensitivity (patient cohort: 337; AUC: 0.769)[62,72]. ML models, such as artificial neural networks and XGBoost model, have been used to predict acute respiratory distress syndrome in the days following AP onset (patient cohort: 217; AUC: 0.859 ± 0.048)[66] or to identify patients at high risk for acute kidney injury based on their evolving lab data (patient cohort: 334; AUC: 0.9193)[67]. Another serious complication is infected pancreatic necrosis, which demands prompt intervention[68]. Early studies have shown that combining imaging data (e.g., CT necrosis extent) with biochemical markers (like C-reactive protein or procalcitonin) in an AI model improves prediction of infection or abscess formation[51-53]. Venous complications, such as splanchnic vein thrombosis, have also been studied[73]; AI models that incorporate inflammation and coagulation markers along with imaging findings are being developed to identify these risks[74]. In short, any complication that follows certain detectable early signals becomes a target for AI-assisted prediction, allowing preemptive management.

Recurrence prediction

Recurrent AP, defined as multiple episodes of AP in a single patient, poses a risk for progression to CP and long-term pancreatic dysfunction[75]. AI has begun to tackle this by identifying patients prone to relapse. Models have been built using a mix of patient history (such as alcohol use or genetic mutations), initial lab values, and even baseline imaging features[76-79]. For example, an ML classifier applied to first-episode AP data predicted recurrence with high accuracy in both training and validation sets (patient cohort: 389; AUC: 0.941)[76]. Another approach used radiomic analysis of the pancreas on initial CT scans, revealing that subtle texture differences identified by AI could distinguish patients likely to develop recurrent AP[77]. These AI tools enable stratified surveillance and early preventive interventions, such as prioritizing gallstone removal, monitoring alcohol abstinence, or tailoring follow-up schedules for high-risk individuals[78,80,81].

Integrated prognostic platforms

Like diagnostic algorithms, AI-based prognostic systems benefit from the integration of heterogeneous data types, including clinical, imaging, laboratory, and emerging molecular datasets. Some teams have begun to explore “digital phenotyping” platforms that combine genetic, biochemical, and imaging data to construct individualized risk profiles[82]. Though still exploratory in pancreatitis, this integrative approach has shown promise in adjacent fields. For example, in pancreatic cancer, AI has been used to combine radiomic features with immunological markers (e.g., programmed death ligand-1 expression or CD8+ T-cell infiltration) to predict response to immunotherapy[83]. A similar framework could potentially be adapted for pancreatitis, whereby molecular signatures, such as cytokine profiles, oxidative stress markers, or gut microbiome features, may be integrated into prognostic models to stratify patients and guide immunomodulatory or anti-inflammatory treatments. Such multi-modal AI tools may ultimately enable a shift from reactive care to proactive risk interception.

INTEGRATIVE THERAPEUTIC INNOVATION

While diagnostic and prognostic tools help manage pancreatitis, therapeutic innovation is also accelerating with AI[84-88]. Two major directions are emerging: The application of AI to personalize and optimize current treatment strategies (precision therapy), and the use of AI-driven platforms for discovering or repositioning therapeutic agents. Figure 3 summarizes these integrative therapeutic innovations, highlighting the interplay between real-time patient data, AI-based drug discovery, and network pharmacology.

Figure 3 Artificial intelligence-driven strategies for therapeutic innovation in pancreatitis. Artificial intelligence (AI) facilitates precision treatment through clinical decision support systems that integrate multimodal patient data to optimize resuscitation, nutritional support, pain management, and intensive care unit triage. Machine learning algorithms dynamically update risk profiles and stratify patients to guide individualized interventions. AI is also accelerating drug discovery and repositioning by enabling high-throughput screening of chemical libraries, predicting compound-target interactions, and identifying repurposable agents with anti-inflammatory or antifibrotic potential. Additionally, network pharmacology approaches powered by AI are uncovering bioactive compounds and mechanistic targets within traditional Chinese medicine formulations, bridging empirical practices with molecular pharmacology. These integrative frameworks represent a paradigm shift in pancreatitis care, offering data-driven insights into both modern and traditional therapeutic avenues. AI: Artificial intelligence; FDA: Food and Drug Administration; TCM: Traditional Chinese medicine.

Personalized and precision treatment

The goal of precision medicine is to match the right therapy to the right patient[89]. In pancreatitis care, this can mean deciding how aggressively to resuscitate a patient with fluids, when to start enteral nutrition, whether to admit to the ICU, or what kind of pain management to use[90]. AI-driven decision support systems can assist by synthesizing patient-specific information[84-86]. For instance, AI models that continuously update a patient’s risk status could signal whether an AP patient requires urgent transfer to intensive care or invasive monitoring, potentially before any obvious clinical deterioration[84]. ML can also help stratify CP patients; by analyzing patterns in pain scores, imaging, and genetic factors, algorithms might predict which patients will benefit from endoscopic procedures vs those who may need surgery or novel biologic therapies[85]. Although these precision interventions are still in the research phase, early results are encouraging. Several AI models have already demonstrated superior accuracy in outcome prediction[91]. For example, a study reported an XGBoost model using ICU data achieved an area under the receiver operating characteristic curve of about 0.93 for predicting ICU transfer or death, whereas Ranson’s score achieved only approximately 0.74[47]. Such performance gaps suggest that integrating rich clinical data via AI can refine treatment decisions.

AI-accelerated drug discovery

Developing new drugs for pancreatitis has been challenging, partly due to the disease’s complexity. AI is beginning to change the early drug discovery process[87,88]. In other fields (like oncology and infectious diseases), DL frameworks have been used to screen millions of compounds virtually against disease targets, identifying candidates that would take much longer to find manually[89-92]. For pancreatitis, similar strategies could be applied. For instance, AI can analyze large chemical and pharmacological databases to propose existing drugs (or natural compounds) that might inhibit pathways involved in pancreatic inflammation or fibrosis[93]. This drug “repositioning” approach is powerful because it starts with compounds already known to be safe in humans[94]. Early examples include using ML models to predict anti-inflammatory activity of Food and Drug Administration-approved drugs or to match drug-target profiles with the genetic signatures of pancreatitis[95]. While specific pancreatitis drugs are still in preclinical stages[96], the toolbox is available: Techniques like graph neural networks, ensemble learning, and high-throughput virtual screening could soon reveal new candidate therapies. Importantly, these AI-driven predictions would require laboratory validation, but they dramatically speed up the search phase.

Network pharmacology and traditional medicines

Network pharmacology, particularly when combined with AI, has emerged as a pivotal approach in decoding the complex mechanisms of TCM and accelerating drug discovery for pancreatitis[97,98]. A growing body of evidence suggests that the multi-component and multi-target nature of TCM is particularly amenable to network-level analysis, offering valuable opportunities for therapeutic innovation in AP and CP[99]. Central to these efforts is the rapid evolution of comprehensive databases that link herbal ingredients to molecular targets and disease phenotypes[100]. Resources such as the Traditional Chinese Medicine Systems Pharmacology Database[101], HERB 2.0[102], BATMAN-TCM 2.0[103], ETCM 2.0[104], and TCMBank[105] collectively provide structured annotations of thousands of herbal compounds, their pharmacokinetic profiles, and predicted or validated interactions with genes and pathways. These are often cross-referenced with disease-specific target databases including GeneCards[106], OMIM[107], TTD[108], and DisGeNET[109], enabling a systematic integration of herbal pharmacology with disease biology. Such synergy between herbal knowledge and disease networks allows researchers to construct multi-layered drug-target-pathway networks, facilitating hypothesis generation and therapeutic repositioning.

This framework has been applied to pancreatitis by identifying shared targets between TCM formulations and genes implicated in inflammatory signaling, oxidative stress, or tissue injury[110-118]. For example, by integrating targets of classic formulas such as Da Chaihu Tang[110] or Xiaochaihu Decoction[111] with AP gene signatures derived from GeneCards and OMIM, researchers have uncovered the modulation of pathways such as mitogen-activated protein kinase, phosphatidylinositol 3-kinase/protein kinase B, and tumor necrosis factor, frequently validated through both protein-protein interaction network analysis and enrichment in Kyoto Encyclopedia of Genes and Genomes or Gene Ontology terms. These computational insights often precede or complement in vivo studies: Compounds like quercetin, kaempferol, or emodin, predicted as central network hubs, have subsequently demonstrated anti-inflammatory or anti-apoptotic effects in preclinical models[112-114]. Further strengthening this approach, transcriptomics and metabolomics data are increasingly incorporated to refine compound selection and mechanism prediction[115-118]. One notable example is the use of integrated network pharmacology and adipose-pancreatic transcriptomic data to elucidate how Chaiqin Chengqi Decoction modulates the nuclear factor-E2-related factor 2/heme oxygenase-1 axis in obesity-associated pancreatitis[110].

ML further enhances this paradigm by enabling high-throughput analysis of TCM prescriptions, prediction of pharmacodynamic activity, and extraction of latent therapeutic patterns[119]. Recent studies have employed supervised learning algorithms such as random forests to model the therapeutic efficacy of hundreds of empirical herbal prescriptions[98], identifying core herbal components, such as Rhubarb (Da Huang), Radix Bupleuri (Chai Hu), and Fructus Aurantii (Zhi Shi), that are most predictive of clinical benefit. These approaches not only confirm traditional usage patterns but also provide quantitative insight into herb-herb synergy and treatment efficacy. Moreover, unsupervised learning and association rule mining can identify novel herb combinations with potential therapeutic relevance, while DL models are increasingly used to screen compound-target interactions and to predict new herb-disease associations based on molecular similarity or knowledge graph embeddings. For example, the TCMBank database integrates three-dimensional chemical structures with AI models to forecast compound functionality and target binding, offering a scalable platform for herbal drug repurposing.

Despite these advances, challenges remain in standardizing herbal data, ensuring batch reproducibility, and experimentally validating predictions. Herbal complexity, variability in preparation, and lack of quantitative pharmacokinetic data hinder AI model calibration and generalizability[120]. Addressing these limitations will require the development of robust ontologies, incorporation of real-world metadata such as geographical origin and processing methods, and integration with experimental pharmacology to verify computational predictions. Nonetheless, the integration of AI with network pharmacology and TCM databases provides a powerful framework for identifying novel therapeutic leads and optimizing personalized treatment strategies in pancreatitis. As these approaches mature, they are likely to reshape how complex diseases like pancreatitis are approached, by combining empirical wisdom with systems-level computation and mechanistic insight.

CHALLENGES AND FUTURE DIRECTIONS

Despite its emerging potential, deploying AI in pancreatitis care faces several key obstacles that must be overcome for safe and effective clinical translation.

Data and methodological robustness

Variability in clinical workflows, imaging protocols, and electronic health record systems across institutions undermines model generalizability. Many AI algorithms have been developed on small, single-center cohorts, heightening the risk of overfitting and limiting their applicability to new patient populations[121,122]. Inconsistent data preprocessing and the absence of standardized benchmarks further hinder cross-study comparisons. Moreover, few tools undergo external or prospective validation, a critical step to confirm real-world performance. To address these gaps, future efforts should prioritize multi-center collaborations, harmonize data collection standards, pre-register development protocols, employ rigorous hold-out test sets, and launch prospective clinical trials.

Transparency, trust, and human-AI integration

Most high-performing AI systems, particularly DL models, operate as “black boxes”, which hampers clinician trust and complicates regulatory approval[15]. The adoption of explainable AI techniques, such as saliency maps, feature-attribution algorithms, and clinically aligned surrogate models, is essential to demystify algorithmic decisions[123]. Even with greater transparency, AI predictions can yield false positives or negatives, each carrying significant clinical consequences[124]. Embedding AI within clinician-in-the-loop frameworks ensures that algorithmic recommendations are reviewed and contextualized by experienced physicians. Notably, meta-analyses show that human-AI hybrid teams outperform either party alone[125]. Interactive platforms (e.g., MedSyn[126]) that support iterative dialogue between clinicians and large language models can refine diagnostic hypotheses, particularly in complex or atypical cases such as AIP or evolving necrosis.

Ethics, equity, and regulatory compliance

Biases in training data, arising from underrepresentation of certain ethnicities, age groups, or comorbidities, can exacerbate health disparities. AI-based medical devices must comply with evolving Food and Drug Administration and European Medicines Agency requirements for transparency[127], continuous-learning oversight, and post-market surveillance. Multi-center data sharing introduces privacy challenges that demand privacy-preserving architectures; federated learning enables decentralized model training, while differential privacy and secure multiparty computation safeguard patient information under the Health Insurance Portability and Accountability Act and General Data Protection Regulation standards[128]. Embedding bias-mitigation measures, such as subgroup performance audits, fairness-aware learning algorithms, and equity dashboards, will be crucial to detect and correct demographic performance gaps before clinical deployment. Notably, while AI-driven network pharmacology approaches integrating TCM compounds have identified promising therapeutic targets, though most of these findings remain at the animal-model or bioinformatic-prediction stage. Prospective clinical trials are required to validate safety and efficacy before real-world implementation.

Five-year roadmap

Looking forward, several concrete milestones are anticipated within the next 5 years. These include: (1) Clinical deployment of real-time AI-powered risk stratification tools for triage in emergency and intensive care settings; (2) Integration of multi-omics profiles (genomic, proteomic, and metabolomic) with clinical and imaging data to refine disease subtyping and therapeutic targeting; (3) Wider adoption of advanced architectures such as transformer-based models (e.g., Vision Transformers[129], ClinicalBERT[130]) to manage sparse or unstructured clinical inputs; and (4) Development of standardized benchmarking frameworks for AI model evaluation, incorporating clinical interpretability, fairness metrics, and real-world impact assessment. Ultimately, realizing the full potential of AI in pancreatitis will require sustained collaboration across disciplines, spanning gastroenterology, computer science, bioethics and health policy, to develop intelligent systems that are not only accurate, but also equitable, interpretable and clinically actionable.

CONCLUSION

AI is rapidly transforming the landscape of pancreatitis care. In diagnostics, ML and DL models are enabling earlier and more accurate identification of pancreatic inflammation using complex inputs such as CT images and biomarker panels. In prognostics, AI allows real-time, individualized risk assessment, helping clinicians anticipate complications, guide ICU triage, and tailor monitoring strategies. In therapeutics, AI-driven methods are accelerating drug discovery and enhancing treatment personalization, especially when coupled with network pharmacology and TCM principles. Recent translational advances illustrate the feasibility of AI deployment in real-world clinical settings. For instance, a 2025 study demonstrated that LightGBM and XGBoost models were successfully integrated into hospital information systems to predict sepsis, ICU admission, and mortality in pancreatitis patients, supporting real-time clinical decision-making. Despite this progress, challenges remain. These include data heterogeneity, model interpretability, regulatory oversight, and the need for external validation in diverse patient populations. Moving forward, successful implementation will require a multidisciplinary strategy that bridges clinical expertise, computational modeling, and ethical design. Moreover, the future of pancreatitis care lies not in replacing clinicians but in augmenting their decision-making with intelligent systems. Human-AI hybrid models, built on reciprocal learning, contextual interpretation, and explainable predictions, may ultimately foster a new paradigm of collaborative, precision-driven medicine.