ChatGPT vs Claude: Scoping Review with ☸️SAIMSARA.

Name: SAIMSARA Evidence Object digital::CHATGPT_VS_CLAUDE_SS
Creator: SAIMSARA
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SAIMSARA

doi:10.62487/saimsarafe254f1e

ChatGPT vs Claude: Scoping Review with ☸️SAIMSARA

Issue 3, Volume 1, 2026

Editorial note

• Last update: 2026-05-17 21:52:28

What is this paper about

ChatGPT vs Claude is not a contest with one winner: the evidence map shows that model performance flips by task, version, modality, and risk level across medicine, education, coding, safety, and research workflows. The full evidence map turns scattered benchmark studies into a practical guide for choosing the right model, identifying where human oversight is essential, and understanding where each system actually performs best.

Human-verified editorial review Verified by World ID proof-of-human. This editorial layer was submitted from a SAIMSARA account verified as a unique human.

Evidence preview · Did you know?

Realistic hospital medication-safety scene with clinicians checking pediatric dosage support from two AI systems.

AI can look useful in narrow care tasks

Did you know? Claude-3.0 and ChatGPT-4o achieved 100% accuracy in pediatric medication dosage calculations and were faster than nurses.

This is a strong practical signal, but it applies to a narrow task — not autonomous clinical care.

Realistic medical AI comparison scene with radiology workstations and two different diagnostic workflows.

The winner can flip inside medicine

Did you know? Claude beat ChatGPT-4o in stroke DWI interpretation, 67.2% vs 32.7%, while ChatGPT-4o beat Claude in MRI sequence classification, 97.7% vs 73.1%.

The real question is not which model is best, but which model fits the exact task, modality, and endpoint.

Realistic AI safety and cybersecurity governance scene with clinicians and engineers reviewing model-risk alerts.

Safety rankings can collapse

Did you know? Claude had lower jailbreak success than ChatGPT in CySecBench, 17% vs 65%, but a later role-play attack found both systems above 94% failure.

Governance cannot rely on one benchmark because safeguards change by version, prompt style, and attack type.

Swipe sideways on mobile · full evidence map opens after unlock

Abstract:

Keywords:

Review Stats

Final search date and database lock: 2026-05-14 22:02:42 CEST

Plan: Pro (expanded craft tokens; source: Semantic Scholar)

Source: Semantic Scholar

Total Abstracts/Papers: 4779

Downloaded Abstracts/Papers: 1000

Included original and non-original Abstracts/Papers (all): 531

Included original Abstracts/Papers (Vote counting by direction of effect): 519

Reference Index (links used in paper): 156

Total participants/sample observations (topic deduplicated ΣN): 6,412,048

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Unlock the full evidence map

The full evidence review, including the Introduction, Methods, Results, Discussion, Conclusion, figures, and complete reference index, opens after purchase or sign-in. The Evidence Object JSON is a separate machine-readable evidence product: a concentrated synthesis of results, topic-level evidence, and discussion across original and non-original studies. It can be directly input into your LLM, agent, or RAG workflow.

Reference Index (156)

[8] Intelligent (but artificial) Feedback in Spanish as a Foreign Language: Evaluation of ChatGPT and Claude as Text Correction Tools — https://doi.org/10.4995/eurocall.2025.23827
[10] Evaluation of Advanced Artificial Intelligence Algorithms’ Diagnostic Efficacy in Acute Ischemic Stroke: A Comparative Analysis of ChatGPT-4o and Claude 3.5 Sonnet Models — https://doi.org/10.3390/jcm14020571
[11] Political Bias in Large Language Models: A Comparative Analysis of ChatGPT-4, Perplexity, Google Gemini, and Claude — https://doi.org/10.1109/access.2024.3523764
[12] Performance of Advanced Artificial Intelligence Models in Traumatic Dental Injuries in Primary Dentition: A Comparative Evaluation of ChatGPT-4 Omni, DeepSeek, Gemini Advanced, and Claude 3.7 in Terms of Accuracy, Completeness, Response Time, and Readability — https://doi.org/10.3390/app15147778
[14] DeepSeek vs. ChatGPT vs. Claude: A Comparative Study for Scientific Computing and Scientific Machine Learning Tasks — https://doi.org/10.1016/j.taml.2025.100583
[15] Evaluating LLMs for Code Generation in HRI: A Comparative Study of ChatGPT, Gemini, and Claude — https://doi.org/10.1080/08839514.2024.2439610
[16] Comparative Evaluation of Responses from ChatGPT-5, Gemini 2.5 Flash, Grok 4, and Claude Sonnet-4 Chatbots to Questions About Endodontic Iatrogenic Events — https://doi.org/10.3390/healthcare13202615
[20] Comparison of chatbots’ accuracy in endodontics questions in dentistry specialization exam in Türkiye: ChatGPT-4o, Gemini Advanced, Copilot, and Claude — https://doi.org/10.1186/s12903-025-07346-8
[21] A Comparative Study on the Performance of ChatGPT-4 and Claude 3 in Translating Engineering Specialized Course Textbooks — https://doi.org/10.5220/0013999300004912
[22] 49-PUB: Diagnosis of Polycystic Ovary Syndrome with General-Purpose Online Large Language Models—A Prospective Evaluation Study of ChatGPT-3.5, ChatGPT-4o, Llama-3, and Claude-3.5 — https://doi.org/10.2337/db25-49-pub
[24] Comparative analysis of LLMs performance in medical embryology: A cross‐platform study of ChatGPT, Claude, Gemini, and Copilot — https://doi.org/10.1002/ase.70044
[25] Evaluating the Accuracy and Reliability of Large Language Models (ChatGPT, Claude, DeepSeek, Gemini, Grok, and Le Chat) in Answering Item-Analyzed Multiple-Choice Questions on Blood Physiology — https://doi.org/10.7759/cureus.81871
[29] Assessing Bias in Large Language Models: A Comparative Study of ChatGPT, Gemini, and Claude — https://doi.org/10.1109/mosicom63082.2024.10881888
[30] A Comparative Assessment of Advanced Conversational Agents: A Multifaceted Evaluation of ChatGPT, Gemini, Perplexity, and Claude — https://doi.org/10.46338/ijetae0224_07
[31] Accuracy of ChatGPT-3.5, ChatGPT-4o, Copilot, Gemini, Claude, and Perplexity in advising on lumbosacral radicular pain against clinical practice guidelines: cross-sectional study — https://doi.org/10.3389/fdgth.2025.1574287
[35] Performance of Large Language Models in Recognizing Brain MRI Sequences: A Comparative Analysis of ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro — https://doi.org/10.3390/diagnostics15151919
[36] The Role of Claude 3.5 Sonet and ChatGPT-4 in Posterior Cervical Fusion Patient Guidance. — https://doi.org/10.1016/j.wneu.2025.123889
[44] Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis — https://doi.org/10.2196/59273
[45] ChatGPT, Gemini, and Claude in clinical and dermoscopic image analysis of basal cell carcinoma and its common mimickers: A comparative performance analysis — https://doi.org/10.1016/j.xjidi.2026.100463
[47] Evaluating the Readability and Quality of Bladder Cancer Information from AI Chatbots: A Comparative Study Between ChatGPT, Google Gemini, Grok, Claude and DeepSeek — https://doi.org/10.3390/jcm14217804
[49] Evaluating Large Language Models in Cardiology: A Comparative Study of ChatGPT, Claude, and Gemini — https://doi.org/10.3390/hearts6030019
[54] Have we reached artificial general intelligence? Comparison of ChatGPT, Claude, and Gemini to human literacy and education benchmarks — https://doi.org/10.22495/cocv22i1art8
[56] LLM-Driven Event Log Generation from Forensic Cases: A Comparative Study of ChatGPT, Claude, and Gemini — https://doi.org/10.1109/cascon66301.2025.00115
[58] Comparative study of the performance of ChatGPT-4, Claude, Gemini, Mistral, and perplexity on multiple-choice questions in cardiology — https://doi.org/10.1186/s12872-025-05431-y
[66] Performance of ChatGPT, Bard, Claude, and Bing on the Peruvian National Licensing Medical Examination: a cross-sectional study — https://doi.org/10.3352/jeehp.2023.20.30
[69] Artificial Intelligence-Based Chatbots’ Ability to Interpret Mammography Images: A Comparison of Chat-GPT 4o and Claude 3.5 — https://doi.org/10.58600/eurjther2599
[70] Chat, Gemini and Claude at the dinner table: assessing general-purpose AI tools for carbohydrate counting in the context of type 1 diabetes. — https://doi.org/10.1016/j.diabres.2025.113031
[74] AI at the Helm: Evaluating Claude 3.5 Sonet and ChatGPT-4.0 in Tympanoplasty Management. — https://doi.org/10.1097/mao.0000000000004812
[78] A Cross-Sectional Study Evaluating the Quality of AI-Generated Patient Education Guides on Diet and Exercise for Diabetes, Hypertension, and Obesity Using ChatGPT-4o, Google Gemini 1.5, Claude Sonnet 4, Perplexity, and Grok — https://doi.org/10.7759/cureus.106221
[79] Concordance of ChatGPT, Gemini, Claude, and OpenEvidence with the 2024 AAOS guidelines on acute isolated meniscal pathology. — https://doi.org/10.1016/j.knee.2026.104427
[80] Code Generation by Large Language Models: A Comparative Analysis of ChatGPT, Claude, and DeepSeek — https://doi.org/10.18178/ijeetc.15.1.19-28
[82] Accuracy of ChatGPT , Gemini, Claude and DeepSeek in Carbohydrate Counting — https://doi.org/10.1111/dom.70747
[83] COMPARATIVE ANALYSIS OF LLM-BASED AI ASSISTANTS FOR EFL WRITING INSTRUCTION: CHATGPT, CLAUDE, GEMINI, AND DEEPSEEK — https://doi.org/10.31851/wdz59c93
[85] How well do large language models interpret ECGs? A comparative benchmark of chatGPT - 4o, claude, and gemini using standardised teaching cases — https://doi.org/10.1093/ehjdh/ztaf143.122
[88] GP or ChatGPT? Ability of large language models (LLMs) to support general practitioners when prescribing antibiotics — https://doi.org/10.1093/jac/dkaf077
[89] Assessing the performance of 8 AI chatbots in bibliographic reference retrieval: Grok and DeepSeek outperform ChatGPT, but none are fully accurate — https://doi.org/10.48550/arxiv.2505.18059
[94] A Comparative Study of Artificial Intelligence-Enabled Business Foreign Language Teaching in Universities: Taking ChatGPT-4o, Claude3.5 Sonnet and ERNIE 4.0 Turbo as Examples — https://doi.org/10.1145/3724504.3724507
[96] Claude AI and Literature Reviews: An Experiment in Utility and Ethical Use — https://doi.org/10.1353/lib.2025.a961199
[99] Evaluating Rater Effects of Large Language Models in Automated Essay Scoring: GPT, Claude, Gemini, and DeepSeek — https://doi.org/10.1111/emip.70018
[100] Jailbroken: How Does LLM Safety Training Fail? — https://doi.org/10.48550/arxiv.2307.02483
[102] SycEval: Evaluating LLM Sycophancy — https://doi.org/10.48550/arxiv.2502.08177
[103] Generalization bias in large language model summarization of scientific research — https://doi.org/10.1098/rsos.241776
[106] Advancing AI-driven thematic analysis in qualitative research: a comparative study of nine generative models on Cutaneous Leishmaniasis data — https://doi.org/10.1186/s12911-025-02961-5
[111] Perceptions and Use of AI Chatbots among Students in Higher Education: A Scoping Review of Empirical Studies — https://doi.org/10.3390/educsci14080922
[114] Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models — https://doi.org/10.1038/s41746-025-01746-4
[115] Current safeguards, risk mitigation, and transparency measures of large language models against the generation of health disinformation: repeated cross sectional analysis — https://doi.org/10.1136/bmj-2023-078538
[116] Accuracy of latest large language models in answering multiple choice questions in dentistry: A comparative study — https://doi.org/10.1371/journal.pone.0317423
[118] Idiosyncrasies in Large Language Models — https://doi.org/10.48550/arxiv.2502.12150
[120] Competency of Large Language Models in Evaluating Appropriate Responses to Suicidal Ideation: Comparative Study — https://doi.org/10.2196/67891
[125] Evaluation of Alignment Between Large Language Models and Expert Clinicians in Suicide Risk Assessment. — https://doi.org/10.1176/appi.ps.20250086
[127] An Examination of Generative AI Response to Suicide Inquires: Content Analysis — https://doi.org/10.2196/73623
[128] How does DeepSeek-R1 perform on USMLE? — https://doi.org/10.1101/2025.02.06.25321749
[129] Comparing Large Language Models for antibiotic prescribing in different clinical scenarios: which perform better? — https://doi.org/10.1016/j.cmi.2025.03.002
[131] Do LLMs consider security? an empirical study on responses to programming questions — https://doi.org/10.1007/s10664-025-10658-6
[132] ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding — https://doi.org/10.48550/arxiv.2305.14196
[133] From Chatbots to Phishbots?: Phishing Scam Generation in Commercial Large Language Models — https://doi.org/10.1109/sp54263.2024.00182
[135] Confirmation and Specificity Biases in Large Language Models: An Explorative Study — https://doi.org/10.1109/mis.2024.3513992
[154] Can large language models assist with pediatric dosing accuracy? — https://doi.org/10.1038/s41390-025-03980-8
[157] Accuracy of Prospective Assessments of 4 Large Language Model Chatbot Responses to Patient Questions About Emergency Care: Experimental Comparative Study — https://doi.org/10.2196/60291
[158] Thyro-GenAI: A Chatbot Using Retrieval-Augmented Generative Models for Personalized Thyroid Disease Management — https://doi.org/10.3390/jcm14072450
[162] Fine-Tuning Large Language Models for Regional Dialect Comprehended Question answering in Bangla — https://doi.org/10.1109/sceecs64059.2025.10940303
[164] Evaluation of validity and reliability of AI Chatbots as public sources of information on dental trauma. — https://doi.org/10.1111/edt.13000
[166] Evaluation of Generative Artificial Intelligence Models in Predicting Pediatric Emergency Severity Index Levels — https://doi.org/10.1097/pec.0000000000003315
[168] Diagnostic performance of advanced large language models in cystoscopy: evidence from a retrospective study and clinical cases — https://doi.org/10.1186/s12894-025-01740-8
[169] Designing LLM-Resistant Programming Assignments: Insights and Strategies for CS Educators — https://doi.org/10.1145/3641554.3701872
[170] The performance of large language model-powered chatbots compared to oncology physicians on colorectal cancer queries — https://doi.org/10.1097/js9.0000000000001850
[175] The actual performance of large language models in providing liver cirrhosis-related information: A comparative study — https://doi.org/10.1016/j.ijmedinf.2025.105961
[178] Beyond Text Generation: Assessing Large Language Models’ Ability to Reason Logically and Follow Strict Rules — https://doi.org/10.3390/ai6010012
[179] Performance Evaluation of 3 Large Language Models for Nutritional Content Estimation from Food Images — https://doi.org/10.1016/j.cdnut.2025.107556
[180] Benchmarking LLM chatbots’ oncological knowledge with the Turkish Society of Medical Oncology’s annual board examination questions — https://doi.org/10.1186/s12885-025-13596-0
[181] Currently Available Large Language Models Do Not Provide Musculoskeletal Treatment Recommendations That are Concordant With Evidence-Based Clinical Practice Guidelines. — https://doi.org/10.1016/j.arthro.2024.07.040
[185] An Empirical Characterization of Outages and Incidents in Public Services for Large Language Models — https://doi.org/10.1145/3676151.3719372
[186] How Effective are LLMs for Data Science Coding? A Controlled Experiment — https://doi.org/10.1109/msr66628.2025.00041
[192] Evaluation of Vision-Language Models for Detection and Deidentification of Medical Images with Burned-In Protected Health Information. — https://doi.org/10.1148/radiol.243664
[195] Accuracy of large language models in data extraction from randomized controlled trials in sleep medicine: A proof-of-concept study. — https://doi.org/10.1016/j.smrv.2025.102192
[197] Textual Proficiency and Visual Deficiency: A Comparative Study of Large Language Models and Radiologists in MRI Artifact Detection and Correction. — https://doi.org/10.1016/j.acra.2025.01.004
[198] Artificial intelligence in dental education: can AI-based chatbots compete with general practitioners? — https://doi.org/10.1186/s12909-025-07880-7
[203] Large language models for the Screening Step in Systematic Reviews in Dentistry. — https://doi.org/10.1016/j.jdent.2025.105877
[205] Security Vulnerabilities in AI-Generated JavaScript: A Comparative Study of Large Language Models — https://doi.org/10.1109/csr64739.2025.11130176
[207] Evaluation and comparison of large language models’ responses to questions related optic neuritis — https://doi.org/10.3389/fmed.2025.1516442
[210] Comparative Performance of the Leading Large Language Models in Answering Complex Rhinoplasty Consultation Questions — https://doi.org/10.1089/fpsam.2024.0206
[212] Prompt engineering for structured data: a comparative evaluation of styles and LLM performance — https://doi.org/10.55092/aias20250009
[213] Assessing large language models as assistive tools in medical consultations for Kawasaki disease — https://doi.org/10.3389/frai.2025.1571503
[214] Evaluating the clinical utility of multimodal large language models for detecting age-related macular degeneration from retinal imaging — https://doi.org/10.1038/s41598-025-18306-1
[218] Evaluating the Clinical Safety of LLMs in Response to High-Risk Mental Health Disclosures — https://doi.org/10.48550/arxiv.2509.08839
[219] Comparative evaluation of large language models performance in medical education using urinary system histology assessment — https://doi.org/10.1038/s41598-025-17571-4
[233] AI and inclusion in simulation education and leadership: a global cross-sectional evaluation of diversity — https://doi.org/10.1186/s41077-025-00355-1
[234] Clinical Failure of General-Purpose AI in Photographic Scoliosis Assessment: A Diagnostic Accuracy Study — https://doi.org/10.3390/medicina61081342
[235] Laissez-Faire Harms: Algorithmic Biases in Generative Language Models (Extended Abstract) — https://doi.org/10.1609/aies.v8i3.36722
[244] Artificial intelligence for the science of evidence synthesis: how good are AI-powered tools for automatic literature screening? — https://doi.org/10.1186/s12874-025-02644-9
[247] Is Artificial Intelligence Ready for Emergency Department Triage? A Retrospective Evaluation of Multiple Large Language Models in 39,375 Patients at a University Emergency Department — https://doi.org/10.3390/jcm15041512
[265] A Longitudinal Analysis of the Usefulness, Readability, Consistency, and Capacity of Artificial Intelligence Chatbot Responses Regarding the Reality of Chronic Pain in Children — https://doi.org/10.1002/pne2.70023
[266] Atlas-Assisted Bone Age Estimation from Hand–Wrist Radiographs Using Multimodal Large Language Models: A Comparative Study — https://doi.org/10.3390/diagnostics16030487
[267] Performance of generative large language models in answering questions from the Brazilian Retina and Vitreous Society certification exam. — https://doi.org/10.5935/0004-2749.2025-0113
[269] AI vs academia: Experimental study on AI text detectors’ accuracy in behavioral health academic writing — https://doi.org/10.1080/08989621.2024.2331757
[275] AI chatbots show promise but limitations on UK medical exam questions: a comparative performance study — https://doi.org/10.1038/s41598-024-68996-2
[277] Empathy AI in healthcare — https://doi.org/10.3389/fpsyg.2025.1680552
[278] Revolutionising Essay Evaluation — https://doi.org/10.4018/ijcallt.368226
[280] Evaluating the reference accuracy of large language models in radiology: a comparative study across subspecialties — https://doi.org/10.4274/dir.2025.253101
[286] Diagnostic capabilities of large language models in the detection of scaphoid fractures in the emergency department — https://doi.org/10.14744/tjtes.2025.98680
[287] Comparative analysis of language models in addressing syphilis-related queries — https://doi.org/10.4317/medoral.27092
[288] Large language models as a diagnostic support tool in neuropathology — https://doi.org/10.1002/2056-4538.70009
[289] Harnessing the Power of LLMs for Service Quality Assessment From User-Generated Content — https://doi.org/10.1109/access.2024.3429290
[294] Evaluation of large language models for antimicrobial classification: implications for antimicrobial stewardship programs — https://doi.org/10.1017/ash.2025.10235
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[317] Leveraging LLMs for Systematic Reviews: Evaluating Venture Capital Investment Strategies Across Large Datasets — https://doi.org/10.1109/cinti67731.2025.11311772
[318] Advancing Sustainable Development Goal 4 – a comparative analysis of large language models — https://doi.org/10.1108/ijem-12-2024-0837
[323] Benchmarking Large Language Models Against Multidisciplinary Tumor Boards in Urological Oncology: Results from the Blinded, Prospective CONCORDIA Study. — https://doi.org/10.1016/j.euo.2025.11.016
[329] Arabian Nights or English Days? Accuracy of Large Language Models in Answering Bilingual Dental Multiple-Choice Questions. — https://doi.org/10.1111/eje.70018
[330] Artificial intelligence and dichotomania — https://doi.org/10.1017/jdm.2025.7
[341] Leveraging Generative AI for Inclusive Online Friendship Education: Integrating Easy-to-Read Guidelines for Enhanced Accessibility — https://doi.org/10.20849/ijsn.v10i1.1502
[359] SUN-813 GLP-1 and Cancer Risk: AI Chatbot Responses — https://doi.org/10.1210/jendso/bvaf149.1319
[365] Evaluation of validity, reliability, and readability of AI chatbots for gestational diabetes mellitus: a multi-model comparative study — https://doi.org/10.3389/fpubh.2026.1760871
[367] Comparative Performance of Large Language Models in Ophthalmology Referral Triage. — https://doi.org/10.7759/cureus.102060
[371] Phishing in the Era of AI: Detection and analysis using naïve bayes and logistic regression — https://doi.org/10.30574/gjeta.2026.27.1.0074
[381] Evaluation of Large Language Model-Based Chatbots for Dental Trauma Management: A Comparative Study Based on Accuracy, Consistency and Information Quality. — https://doi.org/10.1111/aej.70055
[385] Accuracy, quality, and readability analyses of responses from large language models to questions on pediatric dental sedation — https://doi.org/10.1186/s12903-026-08026-x
[386] AI in the Hot Seat: Head-to-Head Comparison of Large Language Models and Cardiologists in Emergency Scenarios — https://doi.org/10.3390/medsci14010033
[393] AI at the Sella Turcica: Multi-Model Large Language Model Evaluation in Pituitary Adenomas — https://doi.org/10.1016/j.bas.2026.105997
[398] Cross-Sectional Evaluation of Medical Disinformation Safeguards in Consumer-Facing Large Language Model Platforms — https://doi.org/10.2196/89831
[400] An analysis of the generative AI use as analyst in qualitative research in science education — https://doi.org/10.33361/rpq.2024.v.12.n.30.724
[401] Comparative Analysis of Large Language Models and Spine Surgeons in Surgical Decision-Making and Radiological Assessment for Spine Pathologies. — https://doi.org/10.1016/j.wneu.2024.11.114
[405] The Diagnostic Performance of Large Language Models and General Radiologists in Thoracic Radiology Cases — https://doi.org/10.1097/rti.0000000000000805
[408] Layout and Task Aware Instruction Prompt for Zero-shot Document Image Question Answering — https://doi.org/10.48550/arxiv.2306.00526
[410] Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study — https://doi.org/10.2196/56165
[420] Using Generative AI to Identify Arguments in Judges’ Reasons: Accuracy and Benefits for Students — https://doi.org/10.5204/lthj.3637
[425] Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology — https://doi.org/10.3390/dermato4040013
[427] Artificial intelligence possibilities to improve analytical policy capacity: the case of environmental policy innovation labs and sustainable development goals — https://doi.org/10.1080/25741292.2024.2385118
[429] The In-depth Comparative Analysis of Four Large Language AI Models for Risk Assessment and Information Retrieval from Multi-Modality Prostate Cancer Work-up Reports — https://doi.org/10.5534/wjmh.240173
[432] Towards Evaluating Large Language Models for Graph Query Generation — https://doi.org/10.48550/arxiv.2411.08449
[438] Creatividad humana vs. creatividad artificial: estudio comparativo entre estudiantes universitarios y chatbots en la generación de ideas — https://doi.org/10.5294/pacla.2024.27.1.10
[440] Transforming education: Navigating the human-AI ecosystem in psychological training and beyond — https://doi.org/10.1088/1755-1315/1415/1/012011
[442] Conversations With my Data: Exploring the Potential of Large Language Models in Qualitative Futures Research — https://doi.org/10.1177/19467567251330210
[447] Comparative Analysis of LLM-Generated Event Timeline Summarization for Legal Investigations — https://doi.org/10.1109/bigdata62323.2024.10826063
[448] How Soon Will Surgeons Become Mere Technicians? Chatbot Performance in Managing Clinical Scenarios. — https://doi.org/10.1016/j.jtcvs.2024.11.006
[450] Chat-based translation of Slavic languages with large language models — https://doi.org/10.63341/vitce/3.2024.43
[456] Factored Verification: Detecting and Reducing Hallucination in Summaries of Academic Papers — https://doi.org/10.48550/arxiv.2310.10627
[464] Empowering Radiologists With ChatGPT-4o: Comparative Evaluation of Large Language Models and Radiologists in Cardiac Cases. — https://doi.org/10.1097/rti.0000000000000846
[467] Enhancing responses from large language models with role-playing prompts: a comparative study on answering frequently asked questions about total knee arthroplasty — https://doi.org/10.1186/s12911-025-03024-5
[473] Exploring the potential of large language models to understand interpersonal emotion regulation strategies from narratives. — https://doi.org/10.1037/emo0001528
[474] Performance of large language models in medical licensing examinations: a systematic review and meta-analysis — https://doi.org/10.3352/jeehp.2025.22.36
[475] Optimizing theranostics chatbots with context-augmented large language models — https://doi.org/10.7150/thno.107757
[486] Appropriateness of Artificial Intelligence Chatbots in Diabetic Foot Ulcer Management. — https://doi.org/10.1177/15347346241236811
[495] Accuracy of Large Language Models When Answering Clinical Research Questions: Systematic Review and Network Meta-Analysis — https://doi.org/10.2196/64486
[496] CySecBench: Generative AI-based CyberSecurity-focused Prompt Dataset for Benchmarking Large Language Models — https://doi.org/10.48550/arxiv.2501.01335
[497] Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text — https://doi.org/10.48550/arxiv.2501.03212
[498] CARDBiomedBench: A Benchmark for Evaluating Large Language Model Performance in Biomedical Research — https://doi.org/10.1101/2025.01.15.633272
[500] Evaluating the Performance of State-of-the-Art Artificial Intelligence Chatbots Based on the WHO Global Guidelines for the Prevention of Surgical Site Infection: Cross-Sectional Study — https://doi.org/10.2196/75567
[506] Inter‐Rater Disagreements in Applying the Montreal Classification for Crohn's Disease: The Five‐Nations Survey Study — https://doi.org/10.1002/ueg2.12757
[510] Clinical applications of large language models in knee osteoarthritis: a systematic review — https://doi.org/10.3389/fmed.2025.1670824
[513] Cracks in the Guardrails: Comparative Study of Jailbreak Attacks on Large Language Models — https://doi.org/10.1109/ic3ecsbhi67834.2026.11468934
[514] Formation of an array of numerical features for classifying the authorship of software codes using symbolic cross-links between words — https://doi.org/10.34121/1028-9763-2026-2-70-78
[516] Applying AI Ethics Tools in Mining Industry Organizations — https://doi.org/10.46544/ams.v30i3.04
[522] An Empirical Evaluation of Large Language Models in Static Code Analysis for PHP Vulnerability Detection — https://doi.org/10.3897/jucs.134739
[523] Performance Evaluation of Large Language Models in Cervical Cancer Management Based on a Standardized Questionnaire: Comparative Study — https://doi.org/10.2196/63626
[527] Comparison of Large Language Models in Diagnosis and Management of Challenging Clinical Cases — https://doi.org/10.2147/opth.s488232