2f:I[703058,["/_next/static/immutable/chunks/3bhq_5eh1d760.js","/_next/static/immutable/chunks/1855-ogyi8eh4.js","/_next/static/immutable/chunks/2pfsjpn6k6zlg.js","/_next/static/immutable/chunks/26c7v3_3uwnwu.js","/_next/static/immutable/chunks/25a3ozcrvi1ff.js","/_next/static/immutable/chunks/2r4wx50nvjh_4.js","/_next/static/immutable/chunks/23fsw06hsi5b1.js","/_next/static/immutable/chunks/06_yt270h41rg.js","/_next/static/immutable/chunks/3ezsiz-7shkna.js","/_next/static/immutable/chunks/20x2y96ldu5wx.js","/_next/static/immutable/chunks/2lxntcm3pd1_h.js","/_next/static/immutable/chunks/00dgco8irq3jw.js","/_next/static/immutable/chunks/00nwe-kq0rboc.js","/_next/static/immutable/chunks/2051ffx3xzio9.js"],"PaperListPageClient"] 30:T81d,Artificial intelligence (AI)-driven chatbots have been rapidly adopted across research, education, business, marketing and medicine. Most interactions, however, come from non-experts using chatbots like search engines, including for everyday health and medical queries. We conducted an original study to audit chatbot responses in health and medical fields prone to misinformation. Five popular chatbots were assessed: Gemini (Google), DeepSeek (High-Flyer), Meta AI (Meta), ChatGPT (OpenAI) and Grok (xAI). In February 2025, each chatbot was prompted with 10 questions from five categories: cancer, vaccines, stem cells, nutrition and athletic performance. We deployed an adversarial-like framework, using open- and closed-ended prompts designed to strain models toward misinformation or contraindicated advice. Two experts from each category rated responses as 'non-problematic', 'somewhat problematic' or Nearly half (49.6%) of responses were problematic: 30% somewhat problematic and 19.6% highly problematic. Response quality did not differ significantly among chatbots (p=0.566) but Grok generated significantly more highly problematic responses than would be expected under a random distribution (z-score +2.07, p=0.038). Performance was strongest in vaccines (mean z-score -2.57) and cancer (-2.12), and weakest in stem cells (+1.25), athletic performance (+3.74) and nutrition (+4.35). Chatbot outputs were consistently expressed with confidence and certainty; from 250 total questions, there were only two refusals to answer (0.8%), both from Meta AI. Reference quality was poor, with a median completeness score of 40% (Q1-Q3: 20-67%). Chatbot hallucinations and fabricated citations precluded any chatbot from producing a fully accurate reference list. All readability scores were graded as 'Difficult' (30-50), equivalent to college sophomore-senior level. The audited chatbots performed poorly when answering questions in misinformation-prone health and medical fields. Continued deployment without public education and oversight risks amplifying misinformation.31:Tc4b, Objectives Artificial intelligence (AI)-driven chatbots have been rapidly adopted across research, education, business, marketing and medicine. Most interactions, however, come from non-experts using chatbots like search engines, including for everyday health and medical queries. Design We conducted an original study to audit chatbot responses in health and medical fields prone to misinformation. Methods Five popular chatbots were assessed: Gemini (Google), DeepSeek (High-Flyer), Meta AI (Meta), ChatGPT (OpenAI) and Grok (xAI). In February 2025, each chatbot was prompted with 10 questions from five categories: cancer, vaccines, stem cells, nutrition and athletic performance. We deployed an adversarial-like framework, using open- and closed-ended prompts designed to strain models toward misinformation or contraindicated advice. Two experts from each category rated responses as ‘non-problematic’, ‘somewhat problematic’ or ‘ highly problematic’ using a coding matrix based on objective, predefined criteria. Citations were scored for accuracy and completeness, and each response was given a Flesch Reading Ease score. Results Nearly half (49.6%) of responses were problematic: 30% somewhat problematic and 19.6% highly problematic. Response quality did not differ significantly among chatbots (p=0.566) but Grok generated significantly more highly problematic responses than would be expected under a random distribution (z-score +2.07, p=0.038). Performance was strongest in vaccines (mean z-score –2.57) and cancer (–2.12), and weakest in stem cells (+1.25), athletic performance (+3.74) and nutrition (+4.35). Chatbot outputs were consistently expressed with confidence and certainty; from 250 total questions, there were only two refusals to answer (0.8%), both from Meta AI. Reference quality was poor, with a median completeness score of 40% (Q1–Q3: 20–67%). Chatbot hallucinations and fabricated citations precluded any chatbot from producing a fully accurate reference list. All readability scores were graded as ‘Difficult’ (30–50), equivalent to college sophomore–senior level. Conclusions The audited chatbots performed poorly when answering questions in misinformation-prone health and medical fields. Continued deployment without public education and oversight risks amplifying misinformation. 25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"Generative Artificial Intelligence: modality Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on Generative Artificial Intelligence (modality). Clinician-written summaries and actionable practice insights. 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Affective AI use was associated with nearly double the rate of clinical emotional problems (57.7% vs. 29.2%; prevalence ratio, 1.98). After adjusting for loneliness, mattering, and demographics, the association persisted (B = 0.14), while functional AI use had only a modest effect (B = 0.01–0.04). The association was slightly attenuated in gender-diverse students compared with boys and girls.","limitations":"Cross-sectional design prevents causal conclusions — it is unclear whether emotional distress drives AI-seeking or vice versa. The sample is limited to Ontario school boards, limiting generalizability. Full text was under embargo; findings are based on the abstract and results sections only.","practice_takeaway":"When a young patient discloses using AI chatbots for emotional support, treat it as a clinical signal of psychological distress warranting further screening — independent of loneliness or social connectedness. Digital literacy programs and clinical frameworks should distinguish affective AI use from functional (school-related) use."}},{"doi":"10.1093/ckj/sfag200","pmid":null,"title":"Potential risks of generative artificial intelligence in kidney care","journal_title":"Clinical Kidney Journal","journal_issn":"2048-8505","published_at":"2026-07-17T00:00:00.000Z","url":"https://doi.org/10.1093/ckj/sfag200","source_score":7,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":false,"crossref":true,"unpaywall":true},"created_at":"2026-07-27T14:09:20.760Z","updated_at":"2026-07-27T14:09:20.760Z","tags":[{"paper_doi":"10.1093/ckj/sfag200","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"category","value":"Urology & Nephrology","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"condition","value":"Chronic Kidney Disease","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"condition","value":"Nephrology","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"impact_flag","value":"safety_signal","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"level_of_evidence","value":"7","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"modality","value":"Large Language Model","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"study_design","value":"Narrative Review","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"warning","value":"No empirical or patient-level data are presented; all risk characterizations are conceptual and opinion-based, which limits direct clinical applicability.","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"warning","value":"Study design classified as 'Narrative Review' — no systematic methods or PRISMA-type reporting are described.","value_numeric":null},{"paper_doi":"10.1093/ckj/sfag200","tag_type":"warning","value":"The paper does not evaluate any specific GenAI product or LLM platform, so findings are generalizable only at a broad level.","value_numeric":null}],"summaries":{"what_studied":"This narrative review examines the risk domains of generative AI (GenAI) — especially large language models — when integrated into kidney care, covering clinical decision support, documentation, patient education, and research applications in nephrology.","key_result":"Key risks identified span four interconnected domains: (1) data issues (bias, limited representativeness, poor data quality); (2) technical flaws (hallucinations, prompt-dependent variability); (3) cognitive hazards (automation bias, overreliance); and (4) systemic concerns (access inequities, linguistic/infrastructural disparities, environmental cost). No quantitative outcome data were reported.","limitations":"- Narrative review with no systematic search strategy or quantitative synthesis, limiting reproducibility and comprehensiveness.\n- Risk domains are described conceptually without nephrology-specific empirical evidence to quantify their real-world impact.\n- Rapidly evolving landscape means some findings may become outdated quickly.","practice_takeaway":"Clinicians using GenAI tools in nephrology should treat AI outputs as probabilistic drafts requiring active critical appraisal — not final answers. Human-in-the-loop oversight, structured prompting, and rigorous local validation are essential before acting on AI-generated recommendations in high-stakes renal care decisions."}},{"doi":"10.3399/bjgp26x745737","pmid":"42209278","title":"AI does not ‘say’: implications of using anthropomorphic language to describe AI use","journal_title":"British Journal of General Practice","journal_issn":"0960-1643","published_at":"2026-05-28T00:00:00.000Z","url":"https://doi.org/10.3399/bjgp26x745737","source_score":5,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-06-04T13:23:47.051Z","updated_at":"2026-06-04T13:23:47.051Z","tags":[{"paper_doi":"10.3399/bjgp26x745737","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"category","value":"Primary Health Care","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"condition","value":"Artificial Intelligence in Healthcare","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"level_of_evidence","value":"7","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"study_design","value":"Opinion / Commentary","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"warning","value":"No sample size is applicable; the paper is a narrative commentary.","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"warning","value":"The cited evidence on anthropomorphism and trust comes from cognitive science and human–computer interaction research, not clinical populations — direct clinical applicability is inferred, not demonstrated.","value_numeric":null},{"paper_doi":"10.3399/bjgp26x745737","tag_type":"warning","value":"This is an expert opinion/commentary with no original empirical data; the 'impact_flag' of practice_changing reflects the clinical relevance of the argument, not trial-level evidence.","value_numeric":null}],"summaries":{"what_studied":"This opinion piece examines the use of anthropomorphic language (e.g., 'AI says', 'AI thinks') when describing AI outputs in professional, clinical, and academic settings, and argues for more precise, accountability-preserving terminology.","key_result":"The author argues that personifying AI inflates perceived authority and trust (citing cognitive science evidence), obscures human accountability, masks statistical uncertainty, and flattens output variability — and proposes neutral alternatives such as 'the model generated X' or 'when prompted with X, the system output X'.","limitations":"This is a commentary without primary data; claims about clinical impact of anthropomorphic language rely on cited studies from non-clinical settings (human–computer interaction and cognitive science).","practice_takeaway":"When sharing AI-generated content in clinical or team settings, use precise language that names the tool, version, and prompt used — this preserves accountability and prompts critical appraisal. Avoid phrases like 'AI says' or 'AI recommends', which can suppress dissent and displace responsibility from the clinician."}},{"doi":"10.1097/lvt.0000000000000914","pmid":"42190268","title":"Emerging applications of traditional and generative artificial intelligence in Liver Transplantation","journal_title":"Liver Transplantation","journal_issn":"1527-6465","published_at":"2026-05-25T00:00:00.000Z","url":"https://doi.org/10.1097/lvt.0000000000000914","source_score":4,"is_retracted":false,"is_open_access":false,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-06-01T13:39:35.978Z","updated_at":"2026-06-01T13:39:35.978Z","tags":[{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"category","value":"Surgery","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"condition","value":"Chronic Liver Disease","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"condition","value":"Hepatocellular Carcinoma","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"condition","value":"Liver Transplantation","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"level_of_evidence","value":"7","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"modality","value":"Deep Learning","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"modality","value":"Large Language Models","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"modality","value":"Machine Learning","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"study_design","value":"Narrative Review","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"warning","value":"Study design labelled as 'Narrative Review'; it is possible the authors frame it as a 'systematic' or 'scoping' review, but the abstract language supports narrative classification.","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"warning","value":"The full text of the paper was not available — the PDF body contained an unrelated GI society statement rather than the article content. All structured data were derived exclusively from the abstract.","value_numeric":null},{"paper_doi":"10.1097/lvt.0000000000000914","tag_type":"warning","value":"This is a narrative review with no original patient data; no sample size tag is applicable.","value_numeric":null}],"summaries":{"what_studied":"A narrative review of AI applications across the liver transplantation (LT) patient journey, covering traditional machine learning (ML), deep learning (DL), and generative AI (GenAI)/large language models (LLMs) — from patient selection and donor-recipient matching to post-transplant outcomes.","key_result":"ML models (random forest, gradient boosting) improve donor utilization and graft survival prediction; DL imaging models assess graft volumetry and steatosis; post-transplant neural networks classify graft injury; LLMs enable digital phenotyping of social determinants of health and simulate transplant selection committees.","limitations":"Full text was not retrievable — findings are based solely on the abstract. No original data or comparative outcomes are reported; translation to routine practice is limited by local validation gaps, workflow integration challenges, and infrastructure requirements.","practice_takeaway":"AI tools spanning ML risk stratification, DL imaging, and GenAI-assisted decision support are increasingly relevant across the LT workflow, but clinicians should be aware that most lack prospective validation and routine implementation remains limited."}},{"doi":"10.2196/91367","pmid":"42155999","title":"Automated Safety Testing and Reporting Application for Conversational Safety Monitoring of Generative AI Tools for Mental Health: Development and Validation Study","journal_title":"JMIR Mental Health","journal_issn":"2368-7959","published_at":"2026-05-19T00:00:00.000Z","url":"https://doi.org/10.2196/91367","source_score":8,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-05-26T13:35:17.321Z","updated_at":"2026-05-26T13:35:17.321Z","tags":[{"paper_doi":"10.2196/91367","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"category","value":"Psychiatry","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"condition","value":"Mental Health","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"condition","value":"Self-Harm","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"impact_flag","value":"safety_signal","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"level_of_evidence","value":"3","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"sample_size","value":"n=100","value_numeric":100},{"paper_doi":"10.2196/91367","tag_type":"study_design","value":"Diagnostic Validation Study","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"warning","value":"Level of evidence assigned as 3 (diagnostic validation study with synthetic data); may be downgraded to 4–5 given the artificial dataset.","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"warning","value":"PDF metadata references a different document ('Barriers to Care in a Changing Practice Environment: 2024 Practitioner Pulse Survey'), suggesting a possible metadata mismatch unrelated to the paper content.","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"warning","value":"Sex of participants is not applicable here (synthetic conversation dataset); sex tag omitted.","value_numeric":null},{"paper_doi":"10.2196/91367","tag_type":"warning","value":"The full text body of the paper was not extractable from the PDF (all page content was blank in the source); summaries and tags are based solely on the abstract and results sections provided.","value_numeric":null}],"summaries":{"what_studied":"This study developed and validated ASTRA (Automated Safety Testing and Reporting Application), an external AI-based system designed to detect clinically relevant risk behaviors in AI-mediated mental health conversations. It was tested on 100 synthetic therapeutic conversations written by licensed clinicians, covering 8 predefined risk categories, using human coder consensus as the reference standard.","key_result":"ASTRA achieved accuracy >0.90 across all 8 risk behavior categories, with uniformly high specificity and sensitivity ranging from 0.55 to 1.00. Agreement with human raters was substantial to almost perfect (κ=0.65–1.00), including reliable detection of subtle self-harm disclosures.","limitations":"- Dataset consisted entirely of synthetic conversations (n=100); real-world generalizability is unconfirmed.\n- Small sample size limits statistical precision for rarer risk categories.\n- Sensitivity varied widely (0.55–1.00) across categories, indicating uneven performance.","practice_takeaway":"Independent safety monitoring tools like ASTRA show promise for flagging risk in AI-assisted mental health platforms, but should be considered complementary—not replacements—for clinical oversight. Clinicians deploying generative AI tools in mental health settings should advocate for validated external safety layers pending larger real-world studies."}},{"doi":"10.1001/jamaophthalmol.2026.1307","pmid":"42133343","title":"Generative Artificial Intelligence–Driven Voice Assistance for Patient Education in Ophthalmology","journal_title":"JAMA Ophthalmology","journal_issn":"2168-6165","published_at":"2026-05-14T00:00:00.000Z","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13177191/","source_score":11,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-05-20T13:23:20.769Z","updated_at":"2026-05-20T13:23:20.769Z","tags":[{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"category","value":"Ophthalmology","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"condition","value":"Chronic Kidney Disease","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"condition","value":"Congestive Heart Failure","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"level_of_evidence","value":"3","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"modality","value":"Large Language Model","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"sample_size","value":"n=3475","value_numeric":3475},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"study_design","value":"Prospective Comparative Evaluation Study","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"warning","value":"Evaluators posing as patients were all licensed healthcare professionals, not representative of the general patient population, which may inflate performance scores.","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"warning","value":"Level of evidence assigned as '3' given the comparative nature without randomization; it is borderline between 2 and 3 given the non-patient evaluator pool.","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"warning","value":"Study design label is ambiguous — this is an industry-sponsored evaluation by the developers of Polaris; no independent external validation is reported.","value_numeric":null},{"paper_doi":"10.1001/jamaophthalmol.2026.1307","tag_type":"warning","value":"The paper's DOI and journal attribution (JAMA Ophthalmology, 2026) do not match the actual full text, which is the Polaris/Hippocratic AI arXiv preprint (arXiv:2403.13313, March 2024) focused on general healthcare AI — not ophthalmology or intravitreal therapy. The abstract provided in the metadata appears to be from a different paper. Summaries and tags reflect the actual full text content.","value_numeric":null}],"summaries":{"what_studied":"This paper describes and evaluates Polaris, a multi-agent LLM constellation system (~1 trillion total parameters) designed for real-time, voice-based, non-diagnostic patient-AI healthcare conversations — covering medication review, lab/vital interpretation, nutrition guidance, and hospital policy Q&A.","key_result":"In subjective evaluation by >1,100 U.S.-licensed nurses and >130 physicians across 3,475+ conversations, Polaris matched or exceeded human nurses on bedside manner (86.3% vs. 82.7%), patient education (88.2% vs. 78.5%), and medical safety (96.9% 'nothing incorrect' vs. 81.2%). On task-specific benchmarks, Polaris outperformed GPT-4 on key capabilities: lab reference range identification (96% vs. 78%), OTC toxicity detection (82% vs. 38%), condition-specific disallowed OTCs (89% vs. 56%), and hospital policy Q&A (86% vs. 36%).","limitations":"- Evaluators were healthcare professionals acting as patients, not real patients — limiting generalizability to actual patient populations.\n- All use cases focused on three conditions (CHF, CKD, colonoscopy prep); performance in other clinical contexts is unknown.\n- Training data and physician-approved reference tables are proprietary, making independent replication difficult.","practice_takeaway":"A safety-focused, voice-based AI agent can perform on par with human nurses for non-diagnostic patient calls, including medication review, lab interpretation, and dietary guidance — with built-in escalation to human clinicians for high-risk situations. Clinicians considering AI-assisted patient outreach should note this system is designed to augment, not replace, licensed staff, and has not yet been validated in real (non-simulated) patient populations."}},{"doi":"10.1002/eat.70128","pmid":"42068161","title":"Benchmarking Generative Artificial Intelligence Against Human Judgment in Eating Disorder Case Recognition and Treatment Recommendations","journal_title":"International Journal of Eating Disorders","journal_issn":"0276-3478","published_at":"2026-05-02T00:00:00.000Z","url":"https://doi.org/10.1002/eat.70128","source_score":8,"is_retracted":false,"is_open_access":false,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-05-08T13:23:40.477Z","updated_at":"2026-05-08T13:23:40.477Z","tags":[{"paper_doi":"10.1002/eat.70128","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"brand","value":"ChatGPT","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"brand","value":"Claude","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"brand","value":"Gemini","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"category","value":"Psychology, Clinical","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"condition","value":"Anorexia Nervosa","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"condition","value":"Atypical Anorexia Nervosa","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"condition","value":"Binge-Eating Disorder","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"impact_flag","value":"practice_changing","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"level_of_evidence","value":"4","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"sample_size","value":"n=20","value_numeric":20},{"paper_doi":"10.1002/eat.70128","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"study_design","value":"Vignette-Based Benchmarking Study","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"warning","value":"Human benchmark comparisons draw from previously published studies with different populations and conditions, not a concurrent controlled comparison—direct head-to-head inference should be made cautiously.","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"warning","value":"Sample size of n=20 prompt repetitions per vignette is very small for an AI evaluation; results may not reflect variability across broader prompt phrasings or model versions.","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"warning","value":"Study design is classified as a vignette-based benchmarking study, which does not fit neatly into standard evidence hierarchies; level of evidence is approximate.","value_numeric":null},{"paper_doi":"10.1002/eat.70128","tag_type":"warning","value":"The AI models tested (ChatGPT-5.4, Gemini-3, Claude-4.5) are cutting-edge versions as of publication; findings may not generalize to earlier or future model versions.","value_numeric":null}],"summaries":{"what_studied":"This study evaluated whether generative AI (ChatGPT-5.4, Gemini-3, Claude-4.5) could accurately identify eating disorder (ED) presentations and recommend appropriate treatment, benchmarking AI responses (20 prompts per vignette) against published human data—clinicians for a restrictive ED vignette (anorexia nervosa/atypical AN across low-, average-, and higher-weight conditions) and community participants for a binge-eating disorder (BED) vignette.","key_result":"ChatGPT-5.4 identified an ED in 100% of low- and average-weight cases and 90% of higher-weight cases, vs. 47%, 21%, and 16% of clinicians; it recommended specialized ED treatment 100% of the time across all weight conditions vs. 35%, 19%, and 17% of clinicians. For BED, ChatGPT correctly identified the diagnosis in 100% of runs vs. 59% of community participants, and endorsed evidence-based treatments 100% vs. 50% of the time.","limitations":"- Vignette-based design limits generalizability to real clinical encounters with nuanced, evolving presentations.\n- Human benchmark data came from separate published studies with different participant samples and methodologies, not a direct head-to-head comparison.\n- Only structured prompt scenarios were tested; AI performance in open-ended or adversarial interactions is unknown.","practice_takeaway":"Generative AI tools outperformed both clinicians and community members in recognizing EDs—especially in higher-weight patients—and in recommending evidence-based care, suggesting potential value as a decision-support or public-education resource. Clinicians should be aware of their own weight-related biases when assessing patients for restrictive EDs at average or higher body weights."}},{"doi":"10.1136/bmjopen-2025-112695","pmid":"41980854","title":"Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit","journal_title":"BMJ Open","journal_issn":"2044-6055","published_at":"2026-04-15T00:00:00.000Z","url":"https://doi.org/10.1136/bmjopen-2025-112695","source_score":828,"is_retracted":false,"is_open_access":true,"raw_metadata":{"ooir":{"day":"2026-04-19","doi":"10.1136/bmjopen-2025-112695","days":"30","issn":"2044-6055","rank":"6","field":"Clinical Medicine","score":"828","category":"Medicine, General & Internal","categories":["Medicine, General & Internal"],"_categories":["Medicine, General & Internal"]},"pubmed":{"doi":"10.1136/bmjopen-2025-112695","title":"Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit.","journal":"BMJ open","methods":"","results":"Nearly half (49.6%) of responses were problematic: 30% somewhat problematic and 19.6% highly problematic. Response quality did not differ significantly among chatbots (p=0.566) but Grok generated significantly more highly problematic responses than would be expected under a random distribution (z-score +2.07, p=0.038). Performance was strongest in vaccines (mean z-score -2.57) and cancer (-2.12), and weakest in stem cells (+1.25), athletic performance (+3.74) and nutrition (+4.35). Chatbot outputs were consistently expressed with confidence and certainty; from 250 total questions, there were only two refusals to answer (0.8%), both from Meta AI. Reference quality was poor, with a median completeness score of 40% (Q1-Q3: 20-67%). Chatbot hallucinations and fabricated citations precluded any chatbot from producing a fully accurate reference list. All readability scores were graded as 'Difficult' (30-50), equivalent to college sophomore-senior level.","abstract":"$30","keywords":["Artificial Intelligence","Health","Medicine"],"pubmed_id":"41980854","conclusions":"","publication_date":"2026-04-15T00:00:00"},"crossref":{"DOI":"10.1136/bmjopen-2025-112695","URL":"https://doi.org/10.1136/bmjopen-2025-112695","ISSN":["2044-6055","2044-6055"],"link":[{"URL":"https://syndication.highwire.org/content/doi/10.1136/bmjopen-2025-112695","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"page":"e112695","type":"journal-article","issue":"4","score":1,"title":["Generative artificial intelligence-driven chatbots and medical misinformation: an accuracy, referencing and readability audit"],"author":[{"given":"Nicholas B","family":"Tiller","sequence":"first","affiliation":[{"name":"The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California, USA"}]},{"given":"Alessandro R","family":"Marcon","sequence":"additional","affiliation":[{"name":"Health Law Institute, University of Alberta, Edmonton, Alberta, Canada"}]},{"given":"Marco","family":"Zenone","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, University of Ottawa, Ottawa, Ontario, Canada"}]},{"given":"Kristin E","family":"Kidd","sequence":"additional","affiliation":[{"name":"Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"}]},{"given":"Asker E","family":"Jeukendrup","sequence":"additional","affiliation":[{"name":"School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK"}]},{"given":"Zubin","family":"Master","sequence":"additional","affiliation":[{"name":"Department of Social Sciences and Health Policy, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA"},{"name":"Wake Forest Institute of Regenerative Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA"},{"name":"Maya Angelou Center for Healthy Communities, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA"},{"name":"Center for Bioethics, Health & Society, Wake Forest University, Winston-Salem, North Carolina, USA"}]},{"given":"Timothy","family":"Caulfield","sequence":"additional","affiliation":[{"name":"Health Law Institute, University of Alberta, Edmonton, Alberta, Canada"}]}],"issued":{"date-parts":[[2026,4]]},"member":"239","prefix":"10.1136","source":"Crossref","volume":"16","created":{"date-time":"2026-04-15T00:05:12Z","timestamp":1776211512000,"date-parts":[[2026,4,15]]},"indexed":{"date-time":"2026-05-13T02:01:43Z","timestamp":1778637703549,"date-parts":[[2026,5,13]]},"license":[{"URL":"https://creativecommons.org/licenses/by-nc/4.0/","start":{"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000,"date-parts":[[2026,4,14]]},"delay-in-days":13,"content-version":"unspecified"}],"subject":[],"abstract":"$31","accepted":{"date-parts":[[2026,2,11]]},"language":"en","relation":{},"resource":{"primary":{"URL":"https://bmjopen.bmj.com/lookup/doi/10.1136/bmjopen-2025-112695"}},"subtitle":[],"deposited":{"date-time":"2026-04-15T00:05:20Z","timestamp":1776211520000,"date-parts":[[2026,4,15]]},"issn-type":[{"type":"print","value":"2044-6055"},{"type":"electronic","value":"2044-6055"}],"published":{"date-parts":[[2026,4]]},"publisher":"BMJ","reference":[{"DOI":null,"key":"2026041417050707000_16.4.e112695.1","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Statista . 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RealToxicityPrompts: evaluating neural toxic degeneration in language models. In: Cohn T , He Y , Liu Y , eds. 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Constitutional AI: harmlessness from AI feedback. arXiv:2212.08073v1 [cs.CL]. 2022. doi:10.48550/arXiv.2212.08073","article-title":null,"journal-title":null,"doi-asserted-by":null},{"DOI":"10.1080/13538322.2012.711071","key":"2026041417050707000_16.4.e112695.56","year":"2012","issue":null,"author":"Bloxham","volume":"18","first-page":"185","unstructured":null,"article-title":"‘You can see the quality in front of your eyes’: grounding academic standards between rationality and interpretation","journal-title":"Quality in Higher Education","doi-asserted-by":"crossref"},{"DOI":"10.1007/978-3-030-02511-3_4","key":"2026041417050707000_16.4.e112695.57","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Delgado López-Cózar E , Orduña-Malea E , Martín-Martín A , et al . Google Scholar as a data source for research assessment. In: Glänzel W , Moed HF , Schmoch U , eds. Springer Handbook of Science and Technology Indicators. 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The origins of archaeological citations used by a generative artificial intelligence application. arXiv:2308.03301v1 [cs.AI]. 2023. doi:10.48550/arXiv.2308.03301","article-title":null,"journal-title":null,"doi-asserted-by":null},{"DOI":null,"key":"2026041417050707000_16.4.e112695.60","year":null,"issue":null,"author":null,"volume":null,"first-page":null,"unstructured":"Roundtable on Health Literacy, Board on Population Health and Public Health Practice, Institute of Medicine . In: Health Literacy: Past, Present, and Future: Workshop Summary. 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level of evidence is approximate.","value_numeric":null},{"paper_doi":"10.1136/bmjopen-2025-112695","tag_type":"warning","value":"The adversarial prompt framework was designed to stress-test models toward misinformation, which may not reflect typical real-world patient query patterns.","value_numeric":null}],"summaries":{"limitations":"Only 10 questions per category were tested, limiting generalizability; expert rater subjectivity may influence 'problematic' classifications despite predefined criteria; findings reflect a single snapshot in February 2025 and may not apply to updated model versions.","practice_takeaway":"Clinicians should warn patients that AI chatbots frequently produce inaccurate, confidently worded health information—especially in areas like nutrition and stem cells—and that chatbot-cited references cannot be trusted without independent verification.","what_studied":"An audit of five AI chatbots (Gemini, DeepSeek, Meta AI, ChatGPT, Grok) responding to 50 health/medical questions across five misinformation-prone categories (cancer, vaccines, stem cells, nutrition, athletic performance) in February 2025, using adversarial prompts rated by domain experts for accuracy, citation quality, and readability.","key_result":"49.6% of responses were problematic (30% somewhat, 19.6% highly problematic); Grok produced disproportionately more highly problematic responses (z=+2.07, p=0.038); nutrition and athletic performance were the weakest categories; citation completeness had a median of only 40%; all responses were at college-level reading difficulty, and hallucinated/fabricated citations appeared in every chatbot."}},{"doi":"10.5546/aap.2026-11022.eng","pmid":"41911409","title":"Generative artificial intelligence in medical scientificwriting: Practical and ethical considerations","journal_title":"Archivos Argentinos de Pediatria","journal_issn":"0325-0075","published_at":"2026-03-27T00:00:00.000Z","url":"https://doi.org/10.5546/aap.2026-11022.eng","source_score":3,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-06-10T14:24:46.605Z","updated_at":"2026-06-10T14:24:46.605Z","tags":[{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"age_group","value":"mixed","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"category","value":"Pediatrics","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"condition","value":"Scientific Publishing Integrity","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"generic","value":"large language models","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"level_of_evidence","value":"5","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"study_design","value":"Narrative Review / Expert Guidance","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"warning","value":"No original empirical study is reported; this is a guidance/opinion article — level of evidence is low and findings are not derived from a primary research study.","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"warning","value":"No sample size is applicable as this is a non-empirical article; the sample_size tag has been omitted.","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"warning","value":"Received and accepted dates listed in the paper (1-12-2026 and 1-13-2026) appear to be in an unusual format and may represent December 1 and January 13 across years, or could be erroneous metadata — interpret publication timeline with caution.","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022.eng","tag_type":"warning","value":"The study design label 'Narrative Review / Expert Guidance' is an approximation; the article is presented as practical guidance rather than a formal systematic or scoping review.","value_numeric":null}],"summaries":{"what_studied":"This narrative guidance article evaluates the ethical and practical use of generative AI tools in medical scientific writing, with specific considerations for pediatric literature and non-native English-speaking researchers.","key_result":"AI tools are acceptable for grammar correction, readability improvement, and translation, but generating content from scratch, interpreting data, or conducting literature reviews with AI is considered ethically unacceptable; a six-step 'human-in-the-loop' workflow is proposed.","limitations":"No original empirical data are presented; recommendations are based on narrative synthesis of existing guidelines and expert opinion, which limits the strength of evidence. The rapidly evolving AI tool landscape means some specific tool recommendations (priced as of January 2026) may already be outdated.","practice_takeaway":"Clinicians writing scientific manuscripts may use AI to polish language and organize structure, but must retain full authorship, disclose all AI use with tool name and version, and verify every fact and reference independently. AI cannot be listed as an author under any circumstances."}},{"doi":"10.5546/aap.2026-11022","pmid":null,"title":"Inteligencia artificial generativa en la escritura científicamédica: consideraciones prácticas y éticas","journal_title":"Archivos Argentinos de Pediatria","journal_issn":"0325-0075","published_at":"2026-03-27T00:00:00.000Z","url":"https://doi.org/10.5546/aap.2026-11022","source_score":3,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":false,"crossref":true,"unpaywall":true},"created_at":"2026-06-10T14:24:46.248Z","updated_at":"2026-06-10T14:24:46.248Z","tags":[{"paper_doi":"10.5546/aap.2026-11022","tag_type":"age_group","value":"pediatric","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"brand","value":"ChatGPT","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"brand","value":"Claude","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"brand","value":"Gemini","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"brand","value":"Grammarly","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"brand","value":"NotebookLM","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"category","value":"Pediatrics","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"condition","value":"Scientific Publishing Integrity","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"generic","value":"ChatGPT","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"level_of_evidence","value":"5","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"modality","value":"Generative Artificial Intelligence","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"study_design","value":"Narrative Review","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"warning","value":"No original empirical data are presented; all clinical/research claims are derived from cited secondary sources. The ChatGPT-4 pediatric abstract study (Gravel et al.) is referenced but not reproduced here.","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"warning","value":"Study design classification as 'Narrative Review' is inferred; the article also functions as a practical guidance document, which may blur standard design categories.","value_numeric":null},{"paper_doi":"10.5546/aap.2026-11022","tag_type":"warning","value":"This article is written in Spanish; the summary above is based on a full translation of the content. Nuances in ethical classification terminology (e.g., 'Área 1/2/3') are interpreted from the original Spanish framework.","value_numeric":null}],"summaries":{"what_studied":"This narrative review and practical guide evaluates the ethical and practical use of generative AI tools in scientific medical writing, with specific focus on pediatric literature and researchers who are non-native English speakers.","key_result":"AI tools are ethically acceptable for grammar correction, readability improvement, translation, and brainstorming (Area 1), conditionally acceptable for outline generation and content summarization under strict human supervision (Area 2), and ethically unacceptable for de novo manuscript drafting, hypothesis generation, data interpretation, or literature review (Area 3). A pediatric-specific study cited found ChatGPT-4 did not produce higher-quality abstracts than human authors, but its suggestions improved final human-written products.","limitations":"- This is a narrative review with no systematic methodology or original data; recommendations are expert-opinion based.\n- The AI tool landscape and editorial policies are rapidly evolving; the tool pricing and features listed are from January 2026 and may already be outdated.\n- No formal evaluation of the proposed 6-step workflow was conducted.","practice_takeaway":"Use AI tools to polish language, improve clarity, and assist with translation — but always write the first draft yourself and verify every AI-generated fact and citation against primary sources before submission. Always disclose the specific AI tools used, including name, version, and purpose, as required by most journals."}}],"initialOffset":10,"fetchConfig":{"kind":"tags","tags":[{"tag_type":"modality","value":"Generative Artificial Intelligence"}]},"breadcrumbItems":[{"name":"Home","href":"/"},{"name":"Research Hub","href":"/hub"},{"name":"Modalities","href":"/hub/tag/modality"},{"name":"Generative Artificial Intelligence"}],"hrefConfig":{"basePath":"/hub/papers","webViewQueryParams":{"hub_ctx":"tag","hub_tag_type":"modality","hub_tag_value":"Generative Artificial Intelligence"}}}]]

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