Foundation models: Insights and implications for gastrointestinal cancer

Table 2 Summary of key studies of large language models in the field of gastrointestinal cancer

Ref.	Year	Models	Objectives	Datasets	Performance	Evaluation
Syed et al[29]	2022	BERTi	Developed fine-tuned BERTi for integrated colonoscopy reports	34165 reports	F1-scores of 91.76%, 92.25%, 88.55% for colonoscopy, pathology, and radiology	Manual chart review by 4 expert-guided reviewer
Lahat et al[30]	2023	GPT	Assessed GPT performance in addressing 110 real-world gastrointestinal inquiries	110 real-life questions	Moderate accuracy (3.4-3.9/5) for treatment and diagnostic queries	Assessed by three gastroenterologists using a 1-5 scale for accuracy etc.
Lee et al[31]	2023	GPT-3.5	Examined GPT-3.5’s responses to eight frequently asked colonoscopy questions	8 colonoscopy-related questions	GPT answers had extremely low text similarity (0%-16%)	Four gastroenterologists rated the answers on a 7-point Likert scale
Emile et al[32]	2023	GPT-3.5	Analyzed GPT-3.5’s ability to generate appropriate responses to CRC questions	38 CRC questions	86.8% deemed appropriately, with 95% concordance on 2022 ASCRS guidelines	Three surgery experts assessed answers using ASCRS guidelines
Moazzam et al[33]	2023	GPT	Investigated the quality of GPT’s responses to pancreatic cancer-related questions	30 pancreatic cancer-questions	80% responses were “very good” or “excellent”	Responses were graded by 20 experts against a clinical benchmark
Yeo et al[34]	2023	GPT	Assessed GPT’s performance in answering questions regarding cirrhosis and HCC	164 questions about cirrhosis and HCC	79.1% correctness for cirrhosis and 74% for HCC, but only 47.3% comprehensiveness	Responses were reviewed by two hepatologists and resolved by a 3rd reviewer
Cao et al[35]	2023	GPT-3.5	Examined GPT-3.5’s capacity to answer on liver cancer screening and diagnosis	20 questions	48% answers were accurate, with frequent errors in LI-RADS categories	Six fellowship-trained physicians from three centers assessed answers
Gorelik et al[36]	2024	GPT-4	Evaluated GPT-4’s ability to provide guideline-aligned recommendations	275 colonoscopy reports	Aligned with experts in 87% of scenarios, showing no significant accuracy gap	Advice assessed by consensus review with multiple experts
Gorelik et al[37]	2023	GPT-4	Analyzed GPT-4’s effectiveness in post-colonoscopy management guidance	20 clinical scenarios	90% followed guidelines, with 85% correctness and strong agreement (κ = 0.84)	Assessed by two senior gastroenterologists for guideline compliance
Zhou et al[38]	2023	GPT-3.5 and GPT-4	Developed a gastric cancer consultation system and automated report generator	23 medical knowledge questions	91.3% appropriate gastric cancer advice (GPT-4), 73.9% for GPT-3.5	The evaluation was conducted by reviewers with medical standards
Yang et al[39]	2025	RECOVER (LLM)	Designed a LLM-based remote patient monitoring system for postoperative care	7 design sessions, 5 interviews	Six major design strategies for integrating clinical guidelines and information	Clinical staff reviewed and provided feedback on the design and functionality
Kerbage et al[40]	2024	GPT-4	Evaluated GPT-4’s accuracy in responding to IBS, IBD, and CRC screening	65 questions (45 patients, 20 doctors)	84% of answers were accurate	Assessed independently by three senior gastroenterologists
Tariq et al[41]	2024	GPT-3.5, GPT-4, and Bard	Compared the efficacy of GPT-3.5, GPT 4, and Bard (July 2023 version) in answering 47 common colonoscopy patient queries	47 queries	GPT 4 outperformed GPT-3.5 and Bard, with 91.4% fully accurate responses vs 6.4% and 14.9%, respectively	Responses were scored by two specialists on a 0-2 point scale and resolved by a 3rd reviewer
Maida et al[42]	2025	GPT-4	Evaluated GPT-4’s suitability in addressing screening, diagnostic, therapeutic inquiries	15 CRC screening inquiries	4.8/6 for CRC screening accuracy, 2.1/3 for completeness scored	Assessment involved 20 experts and 20 non-experts rating the answers
Atarere et al[43]	2024	BingChat, GPT, YouChat	Tested the appropriateness of GPT, BingChat, and YouChat in patient education and patient-physician communication	20 questions (15 on CRC screening and 5 patient-related)	GPT and YouChat provided more reliable answers than BingChat, but all models had occasional inaccuracies	Two board-certified physicians and one Gastroenterologist graded the responses
Chang et al[44]	2024	GPT-4	Compared GPT-4’s accuracy, reliability, and alignment of colonoscopy recommendations	505 colonoscopy reports	85.7% of cases matched USMSTF guidelines	Assessment was conducted by an expert panel under USMSTF guidelines
Lim et al[45]	2024	GPT-4	Compared a contextualized GPT model with standard GPT in colonoscopy screening	62 example use cases	Contextualized GPT-4 outperformed standard GPT-4	Compare the GPT4 against a model with relevant screening guidelines
Munir et al[46]	2024	GPT	Evaluated the quality and utility of responses for three GI surgeries	24 research questions	Modest quality and vary significantly based on the type of procedure	Responses were graded by 45 expert surgeons
Truhn et al[47]	2024	GPT-4	Created a structured data parsing module with GPT-4 for clinical text processing	100 CRC reports	99% accuracy for T-stage extraction, 96% for N-stage, and 94% for M-stage	Accuracy of GPT-4 was compared with manually extracted data by experts
Choo et al[48]	2024	GPT	Designed a clinical decision-support system to generate personalized management plans	30 stage III recurrent CRC patients	86.7% agree with tumor board decisions, 100% for second-line therapies	The recommendations were compared with the decision plans made by the MDT
Huo et al[49]	2024	GPT, BingChat, Bard, Claude 2	Established a multi-AI platform framework to optimize CRC screening recommendations	Responses for 3 patient cases	GPT aligned with guidelines in 66.7% of cases, while other AIs showed greater divergence	Clinician and patient advice was compared to guidelines
Pereyra et al[50]	2024	GPT-3.5	Optimized GPT-3.5 for personalized CRC screening recommendations	238 physicians	GPT scored 4.57/10 for CRC screening, vs 7.72/10 for physicians	Answers were compared against a group of surgeons
Peng et al[51]	2024	GPT-3.5	Built a GPT-3.5-powered system for answering CRC-related queries	131 CRC questions	63.01 mean accuracy, but low comprehensiveness scores (0.73-0.83)	Two physicians reviewed each response, with a third consulted for discrepancies
Ma et al[52]	2024	GPT-3.5	Established GPT-3.5-based quality control for post-esophageal ESD procedures	165 esophageal ESD cases	92.5%-100% accuracy across post-esophageal ESD quality metrics	Two QC members and a senior supervisor conducted assessment
Cohen et al[53]	2025	LLaMA-2, Mistral-v0.1	Explored the ability of LLMs to extract PD-L1 biomarker details for research purposes	232 EHRs from 10 cancer types	Fine-tuned LLMs outperformed LSTM trained on > 10000 examples	Assessed by 3 clinical experts against manually curated answers
Scherbakov et al[54]	2025	Mixtral 8 × 7 B	Assessed LLM to extract stressful events from social history of clinical notes	109556 patients, 375334 notes	Arrest or incarceration (OR = 0.26, 95%CI: 0.06-0.77)	One human reviewer assessed the precision and recall of extracted events
Chatziisaak et al[55]	2025	GPT-4	Evaluated the concordance of therapeutic recommendations generated by GPT	100 consecutive CRC patients	72.5% complete concordance, 10.2% partial concordance, and 17.3% discordance	Three reviewers independently assessed concordance with MDT
Saraiva et al[56]	2025	GPT-4	Assessed GPT-4’s performance in interpreting images in gastroenterology	740 images	Capsule endoscopy: Accuracies 50.0%-90.0% (AUCs 0.50-0.90)	Three experts reviewed and labeled images for CE
Siu et al[57]	2025	GPT-4	Evaluated the efficacy, quality, and readability of GPT-4’s responses	8 patient-style questions	Accurate (40), safe (4.25), appropriate (4.00), actionable (4.00), effective (4.00)	Evaluated by 8 colorectal surgeons
Horesh et al[58]	2025	GPT-3.5	Evaluated management recommendations of GPT in clinical settings	15 colorectal or anal cancer patients	Rating 48 for GPT recommendations, 4.11 for decision justification	Evaluated by 3 experienced colorectal surgeons
Ellison et al[59]	2025	GPT-3.5, Perplexity	Compared readability using different prompts	52 colorectal surgery materials	Average 7.0-9.8, Ease 53.1-65.0, Modified 9.6-11.5	Compared mean scores between baseline and documents generated by AI
Ramchandani et al[60]	2025	GPT-4	Validated the use of GPT-4 for identifying articles discussing perioperative and preoperative risk factors for esophagectomy	1967 studies for title and abstract screening	Perioperative: Agreement rate = 85.58%, AUC = 0.87. Preoperative: Agreement rate = 78.75%, AUC = 0.75	Decisions were compared with those of three independent human reviewers
Zhang et al[61]	2025	GPT-4, DeepSeek, GLM-4, Qwen, LLaMa3	To evaluate the consistency of LLMs in generating diagnostic records for hepatobiliary cases using the HepatoAudit dataset	684 medical records covering 20 hepatobiliary diseases	Precision: GPT-4 reached a maximum of 93.42%. Recall: Generally below 70%, with some diseases below 40%	Professional physicians manually verified and corrected all the data
Spitzl et al[62]	2025	Claude-3.5, GPT-4o, DeepSeekV3, Gemini 2	Assessed the capability of state-of-the-art LLMs to classify liver lesions based solely on textual descriptions from MRI reports	88 fictitious MRI reports designed to resemble real clinical documentation	Micro F1-score and macro F1-score: Claude 3.5 Sonnet 0.91 and 0.78, GPT-4o 0.76 and 0.63, DeepSeekV3 0.84 and 0.70, Gemini 2.0 Flash 0.69 and 0.55	Model performance was assessed using micro and macro F1-scores benchmarked against ground truth labels
Sheng et al[63]	2025	GPT-4o and Gemini	Investigated the diagnostic accuracies for focal liver lesions	228 adult patients with CT/MRI reports	Two-step GPT-4o, single-step GPT-4o and single-step Gemini (78.9%, 68.0%, 73.2%)	Six radiologists reviewed the images and clinical information in two rounds (alone, with LLM)
Williams et al[64]	2025	GPT-4-32K	Determined LLM extract reasons for a lack of follow-up colonoscopy	846 patients' clinical notes	Overall accuracy: 89.3%, reasons: Refused/not interested (35.2%)	A physician reviewer checked 10% of LLM-generated labels
Lu et al[65]	2025	MoE-HRS	Used a novel MoE combined with LLMs for risk prediction and personalized healthcare recommendations	SNPs, medical and lifestyle data from United Kingdom Biobank	MoE-HRS outperformed state-of-the-art cancer risk prediction models in terms of ROC-AUC, precision, recall, and F1 score	LLMs-generated advice were validated by clinical medical staff
Yang et al[66]	2025	GPT-4	Explored the use of LLMs to enhance doctor-patient communication	698 pathology reports of tumors	Average communication time decreased by over 70%, from 35 to 10 min (P < 0.001)	Pathologists evaluated the consistency between original and AI reports
Jain et al[67]	2025	GPT-4, GPT-3.5, Gemini	Studied the performance of LLMs across 20 clinicopathologic scenarios in gastrointestinal pathology	20 clinicopathologic scenarios in GI	Diagnostic accuracy: Gemini Advanced (95%, P = 0.01), GPT-4 (90%, P = 0.05), GPT-3.5 (65%)	Two fellowship-trained pathologists independently assessed the responses of the models
Xu et al[68]	2025	GPT-4, GPT-4o, Gemini	Assessed the performance of LLMs in predicting immunotherapy response in unresectable HCC	Multimodal data from 186 patients	Accuracy and sensitivity: GPT-4o (65% and 47%) Gemini-GPT (68% and 58%). Physicians (72% and 70%)	Six physicians (three radiologists and three oncologists) independently assessed the same dataset
Deroy et al[69]	2025	GPT-3.5 Turbo	Explored the potential of LLMs as a question-answering (QA) tool	30 training and 50 testing queries	A1: 0.546 (maximum value); A2: 0.881 (maximum value across three runs)	Model-generated answers were compared to the gold standard
Ye et al[70]	2025	BioBERT-based	Proposed a novel framework that incorporates clinical features to enhance multi-omics clustering for cancer subtyping	Six cancer datasets across three omics levels	Mean survival score of 2.20, significantly higher than other methods	Three independent clinical experts review and validate the clustering results

CRC: Colorectal cancer; HCC: Hepatocellular carcinoma; IBS: Irritable bowel syndrome; IBD: Inflammatory bowel disease; LSTM: Long Short-Term Memory; NLP: Natural language processing; MoE: Mixture of experts; LLMs: Large language models; MDT: Multidisciplinary tumor; SNP: Single nucleotide polymorphism; TCGA: The Cancer Genome Atlas.