The research team, led by Heba Wageh Abozaed and colleagues, constructed thirty standardized clinical scenarios representing the kinds of cases a prosthodontist encounters in daily practice. Each scenario was fed to three AI systems: Qwen 3.7 Plus, developed by Alibaba; Claude Sonnet 5, from Anthropic; and Mistral Medium 3.5, the European contender from Mistral AI. Every model generated a treatment plan for every scenario, producing ninety plans in total. This standardized design matters enormously. Earlier studies of AI in dentistry often used ad hoc prompts and single-model assessments, making comparisons nearly impossible. By holding the clinical material constant and varying only the model, the researchers isolated exactly what they wanted to measure: the quality of machine-generated clinical reasoning.

Judging the plans fell to two blinded prosthodontic evaluators who worked independently, neither knowing which model had produced which plan. Each plan was scored on a seven-domain rubric using a five-point Likert scale, covering diagnostic accuracy, treatment selection, treatment sequencing, completeness, adherence to references, patient safety, and overall clinical appropriateness. That structure is worth pausing on, because treatment planning in fixed prosthodontics is not a single decision but a chain of them. A clinician must first interpret the diagnostic information correctly, then choose among crowns, fixed partial dentures, implants, or other restorative pathways, then order those interventions sensibly, all while protecting the patient from harm and grounding every choice in accepted evidence. A model that writes fluent prose but skips a critical step, or recommends an irreversible procedure without adequate justification, would lose points precisely where it matters most.

The statistical machinery behind the comparison was appropriately serious for non-normal, paired ordinal data. The Friedman test, a non-parametric equivalent of repeated-measures analysis of variance, assessed whether the three models differed overall, and it did so emphatically: a chi-square statistic of 35.05 with two degrees of freedom, yielding a p-value below 0.001, and a Kendall’s W of 0.584 indicating a substantial consistency in how the models were ordered across the thirty scenarios. Pairwise contrasts followed using Wilcoxon signed-rank tests with Holm adjustment, a correction that controls the inflated risk of false positives when multiple comparisons are run on the same data. Every pairwise difference survived that correction, meaning the gaps between the models were statistically robust rather than artifacts of chance.

On the raw numbers, Qwen 3.7 Plus took the crown. Its mean total score of 33.82, with a standard deviation of 1.74, topped Claude Sonnet 5’s 32.52 (standard deviation 2.30) and left Mistral Medium 3.5 trailing at 29.68 (standard deviation 2.66). Because the rubric spans seven domains on a five-point scale, the theoretical maximum total is 35, which makes Qwen’s average strikingly close to perfection and highlights just how compressed the top of the scale had become. Within the individual domains, the picture grew more nuanced. Qwen scored highest on completeness and on adherence to references, suggesting it was the most thorough and the most evidence-anchored of the three. Claude, however, achieved the highest patient safety scores, a domain that many clinicians would argue outweighs almost any other. Mistral lagged across all seven domains, a uniform rather than selective weakness.

Then comes the twist that transforms this from a straightforward leaderboard into a genuinely important methodological caution. The two evaluators did not agree with each other very well. The single-measure intraclass correlation coefficient came in at just 0.378, well below conventional thresholds for acceptable reliability, and even the average-measure ICC, which reflects the reliability of the mean of both raters, reached only 0.549. Quadratic-weighted Cohen’s kappa told a similarly sobering story. In plain terms, the two expert judges, both blinded and both using the same rubric, saw the same ninety plans and scored them in ways that diverged substantially.

A sensitivity analysis made the consequences explicit. When each evaluator’s scores were analyzed separately, the model ranking flipped. Evaluator 1 placed Claude marginally above Qwen, while Evaluator 2 ranked Qwen clearly ahead of Claude. The researchers traced this discrepancy to a pronounced ceiling effect in Evaluator 1’s ratings: that evaluator assigned the maximum score to the majority of Qwen and Claude cases, compressing the scale so severely that meaningful differences between the two leading models simply could not register. Ceiling effects are a well-known hazard in performance assessment, and here they were strong enough to overturn the headline finding of the primary analysis. The authors are admirably direct about the implication: these findings do not support universal superiority of any single model.

What, then, can responsibly be concluded? Mistral’s last-place finish across every domain appears to be the most stable result, consistent under both evaluators and across the sensitivity checks. Qwen’s strengths in completeness and reference adherence, and Claude’s edge in patient safety, are real observations from the primary analysis, but the fragility of the inter-rater agreement means any claim that one model is definitively better than the other should be treated as provisional. The study also stops short of testing whether AI-assisted planning actually improves outcomes for patients or learning outcomes for students; educational effectiveness was not directly assessed. What the study does establish is that the leading models can produce treatment plans that experts, on average, judge to be clinically appropriate across a demanding seven-domain rubric, and that the differences among them, while statistically detectable, are modest and measurement-dependent.

The educational implications may prove to be the most durable legacy of this work. The authors suggest that large language models could serve as adjunctive tools for case-based learning and for critical appraisal of treatment-planning decisions, and that framing fits the evidence well. A dental student who generates a plan with an AI system and then interrogates it, asking why a particular sequence was chosen, which alternatives were rejected, and whether the safety considerations were adequately addressed, is engaging in exactly the reflective reasoning that prosthodontic education tries to cultivate. The models become sparring partners rather than oracles. That use case also sidesteps the most dangerous failure mode, which would be a clinician or trainee accepting a machine-generated plan uncritically, particularly in a field where errors can mean irreversible removal of tooth structure.

For the broader conversation about AI in medicine, the study offers a template worth copying. Standardized scenarios, blinded expert evaluation, a multi-domain rubric, non-parametric statistics with multiplicity correction, and, crucially, a sensitivity analysis that exposed how much the conclusions depended on the raters themselves. The uncomfortable but valuable lesson is that evaluating AI is itself a measurement problem, and a rubric that allows one expert to hand out perfect scores to most cases will tell you more about the rubric than about the machine. As these models continue to improve at astonishing speed, the bottleneck in studies like this one may shift from the capability of the AI to the reliability of the humans judging it. Fixing that, perhaps through larger panels of evaluators, refined anchor descriptions for each score point, or harder scenarios that push plans away from the ceiling, will be essential before anyone can say with confidence which artificial intelligence plans a crown best. For now, the honest answer is that the top contenders are close, the measurement tools are shaky, and the most sensible role for AI in the prosthodontic workflow today is as a well-informed colleague whose plans are always worth questioning.

Subject of Research: Comparative evaluation of large language models for fixed prosthodontic treatment planning

Article Title: Comparative evaluation of three large language models for fixed prosthodontic treatment planning: a standardized scenario-based study

Article References: Abozaed, H. W., Alshenaiber, R., Albader, S., Algohar, A., Murayshed, M. S., & Alokla, M. (2026). Comparative evaluation of three large language models for fixed prosthodontic treatment planning: a standardized scenario-based study. BMC Medical Education. https://doi.org/10.1186/s12909-026-10560-9

Image Credits: AI Generated

DOI: 10.1186/s12909-026-10560-9

Keywords: large language models, artificial intelligence, prosthodontics, treatment planning, dental education, Qwen, Claude, Mistral, clinical decision-making, inter-rater reliability, BMC Medical Education, AI in dentistry

Paul Sebo of the University of Geneva and Ting Wang of Emporia State University set out to test whether two leading AI models, OpenAI’s ChatGPT and Anthropic’s Claude, would score identical scientific abstracts differently depending on the name and country attached to them. They also wanted to know something equally important for any future editorial application: whether the models give the same score to the same work when asked twice. The stakes are considerable. If AI reviewers are both unbiased and reproducible, they could become powerful tools for triaging the flood of submissions that modern journals face. If they are neither, their adoption could quietly distort who gets published and whose research shapes medicine.

The experimental design was deliberately austere. The researchers randomly selected ten general internal medicine journals from the Journal Citation Reports, each with an impact factor of at least 1.5, and pulled five original research abstracts per journal from the Web of Science, for a total of fifty abstracts published between 2023 and 2026. Each abstract was then evaluated under four fictional author identities: an American woman named Rachel Smith, an American man named John Smith, a woman from Côte d’Ivoire named Fatoumata Diallo, and a man from Côte d’Ivoire named Amadou Diallo. Using fictional identities rather than real researchers allowed the team to manipulate exactly one variable, the identity cue, while keeping everything else constant and avoiding the ethical tangle of attaching published work to real people.

Every abstract-identity combination was scored twice by each model, with each evaluation conducted in a fresh chat session to prevent any memory of previous judgments from contaminating the results. The prompt was identical every time: act as a scientific reviewer, and rate the abstract on three dimensions using a 0 to 10 scale, namely overall scientific quality, novelty, and likelihood of acceptance at a high-quality conference or journal. The models were instructed to output only three numbers, no explanations. That discipline produced 800 evaluations in total, 400 per model, all collected between April 15 and April 30, 2026, using the standard user interfaces with default settings.

The headline finding is striking in its symmetry. For ChatGPT, median quality and novelty scores were identical across all four identities, with only minimal variation in acceptance scores that followed no consistent gender or geographic pattern. For Claude, all three scores were identical across identities. Multivariable ordinal logistic regression, adjusted for journal, found no overall association between author identity and any score, with one partial exception: a global test for Claude’s acceptance scores hinted at a possible association, but no individual comparison reached statistical significance. The authors urge caution even about the isolated differences that did appear, such as slightly lower novelty and acceptance scores that ChatGPT gave to abstracts attributed to the American man, because the overall tests were not significant and many comparisons were performed on a modest number of unique abstracts.

Reproducibility, the second pillar of the study, was equally encouraging. When the same abstract was scored twice under the same identity, percent agreement exceeded 0.98 for both models across all three outcomes. Fleiss’ kappa, a statistic that corrects for agreement expected by chance and weights larger disagreements more heavily, ranged from 0.88 to 0.89 for ChatGPT, indicating excellent consistency, and from 0.74 to 0.80 for Claude, indicating substantial agreement. ChatGPT was thus the steadier grader, though both models were far more consistent with themselves than human reviewers typically are with each other, a well-documented weakness of traditional peer review.

The scores were not blind to everything, however. Abstracts from higher-impact journals received systematically higher ratings across all three dimensions, with each point of impact factor associated with roughly 35 to 40 percent higher odds of a better score. Interestingly, the journals’ names and impact factors were never shown to the models, so this pattern cannot reflect prestige bias directly. It more likely reflects genuine differences in the writing quality and study characteristics of abstracts published in more selective venues, though the relationship was not perfectly linear, and one mid-tier journal, Annals of Family Medicine, consistently ranked low despite its respectable impact factor of 5.1. The models also differed from each other: Claude assigned significantly lower quality and acceptance scores than ChatGPT overall, suggesting that different AI systems bring meaningfully different evaluative standards to the same text.

The authors are careful, almost insistently so, about what these results do not prove. Consistency is not validity. The fact that a model gives the same score twice says nothing about whether that score is accurate, insightful, or useful for editorial decisions, and the study included no human reference standard against which to calibrate the numbers. The restricted range of scores, mostly clustered between 2 and 9 with medians of 5 to 7, may itself have inflated the agreement statistics. The task was also radically simplified compared with real peer review: abstracts rather than full manuscripts, numerical scores rather than narrative critique, and a single standardized prompt rather than the messy, iterative dialogue that characterizes genuine refereeing. Subtler biases that might surface in open-ended written feedback would be invisible in this design.

There are further limits worth keeping in mind. Fifty abstracts from a single medical specialty cannot speak for all of science, and only two models, at specific versions, were tested; both are updated frequently, so the findings may not hold for future releases. The identity manipulation covered only two countries and two genders, a narrow slice of the diversity of the global research community, and the researchers could not verify whether the models actually registered the identity cues at all. An absence of score differences could mean the models are genuinely impartial, or simply that they ignored the names entirely. The study was also not preregistered, which the authors acknowledge transparently.

Still, the implications are provocative. At a moment when journals are wrestling with reviewer shortages and with evidence that human peer review itself suffers from gender, geographic, and institutional bias, the prospect of an evaluator that treats a manuscript from Geneva and one from Abidjan identically is genuinely newsworthy. The authors suggest that structured, low-stakes applications such as initial screening, detection of reporting deficiencies, and editorial triage may be the most realistic near-term uses for these tools, rather than replacing human reviewers outright. The most informative next step, they argue, would be to run LLM-based review on manuscripts for which real human review reports and editorial decisions already exist, allowing a direct head-to-head comparison of judgments and biases. Until then, the message of this study is a measured one: in the sterile laboratory of standardized abstract scoring, the machines showed no favoritism and remarkable self-consistency. Whether they can sustain that discipline in the messy, argumentative world of real scientific publishing remains an open and urgent question.

Subject of Research: Bias and reproducibility of large language models as evaluators of scientific abstracts in peer review

Article Title: Bias and Reliability of AI-Based Peer Review: A Comparative Study of ChatGPT and Claude Evaluating Scientific Abstracts

Article References: Sebo, P., & Wang, T. (2026). Bias and Reliability of AI-Based Peer Review: A Comparative Study of ChatGPT and Claude Evaluating Scientific Abstracts. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10806-8

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10806-8

Keywords: artificial intelligence, large language models, peer review, ChatGPT, Claude, bias, reproducibility, scientific publishing, gender bias, geographic bias, abstracts, internal medicine

The study, published in Discover Artificial Intelligence, was designed to mirror how ordinary people actually use these tools. On a single day, 30 June 2025, the team submitted all fifteen questions to each model through standard consumer web interfaces, exactly as a patient would, with no API access, no custom settings, and no prompt engineering. The questions themselves were drawn exhaustively from the official NHS page of common questions about pregabalin, a resource compiled and validated by clinical pharmacists for the general public. Each query came wrapped in a standardized instruction asking for a clear, factual, patient-appropriate answer that must include a warning if any potential danger existed. That explicit safety instruction is what makes the study’s central finding so striking.

Pregabalin was a deliberate and timely choice. The drug is a structural analogue of the neurotransmitter GABA, but it does not act on GABA receptors directly; instead it binds selectively to the alpha-2-delta-1 subunit of voltage-gated calcium channels, dampening the release of excitatory neurotransmitters. Its clinical footprint has expanded dramatically over the past decade. Global annual sales of gabapentinoids, the drug class that includes pregabalin, rose by 114.5 percent between 2012 and 2022, and by a staggering 180.9 percent in low- and middle-income countries, where specialist care is scarce and patients are especially likely to seek answers from digital platforms. Prescribing rates more than doubled over ten years, reaching 7.2 prescriptions per 100 patients in 2019, with the highest volumes among elderly patients juggling multiple medications. Off-label and recreational use have broadened the population asking questions even further.

To judge the answers, the researchers assembled a multidisciplinary panel of a rheumatologist, a neurologist, and a pharmacologist, each with at least five years of clinical experience, reflecting the three main specialties that prescribe and monitor pregabalin. The evaluators worked independently and blind to which model had produced each response, after a calibration session in which they jointly scored three practice answers to align their criteria. Six dimensions were assessed: warning compliance, accuracy, completeness, content safety, readability, and clinical relevance. The study followed the TRIPOD-LLM reporting guideline, an emerging standard for research involving large language models, and the team calculated inter-rater reliability using Fleiss’ kappa and intraclass correlation coefficients.

The headline numbers on accuracy look encouraging. ChatGPT-4o, Gemini 2.0 Flash, and Copilot each scored 0.933 for factual correctness, while Claude 3.5 Sonnet scored 0.867. Clinical relevance ranged from 0.867 to 1.000 across the four models. But the confidence intervals overlapped so extensively that no model could claim reliable superiority over any other, a point the authors emphasize repeatedly. With only fifteen questions and a single query per model, the differences fall well within the statistical noise. The researchers are candid about this: the point estimates are best read as an illustration of the evaluation framework rather than stable performance benchmarks, and the single-query design means the stochastic variability of these models, which can produce substantively different answers to identical prompts, was never characterized.

The safety findings are where the study bites. Every one of the fifteen questions was judged by panel consensus to carry a clinically relevant safety concern warranting a warning, and the prompt explicitly demanded one. Yet warning compliance scores ranged only from 0.467 for ChatGPT-4o and Gemini 2.0 Flash to 0.733 for Claude 3.5 Sonnet. In plain terms, the models ignored a direct safety instruction in roughly 27 to 53 percent of cases. The authors argue this is arguably more alarming than a simple failure to volunteer warnings, because it demonstrates that even explicit prompting cannot guarantee reliable safety communication. For a drug with dependence potential, meaningful interaction risks, and a heavy presence among polypharmacy patients, that gap between instruction and behavior is the study’s most clinically consequential result.

When the researchers dissected the errors, a clear pattern emerged. Oversimplification dominated, accounting for 56.3 percent of all recorded errors, followed by omission of essential information at 34.8 percent, with outright hallucinations making up 8.9 percent. Gemini 2.0 Flash logged the most errors at 32, while Claude 3.5 Sonnet logged the fewest at 25. The authors connect these patterns to plausible, though unproven, architectural explanations: token probability maximization may favor statistically common, simplified phrasing over clinically nuanced detail; attention-based architectures may underweight rare but critical content; and reinforcement learning from human feedback may optimize for fluency and consensus at the cost of completeness. Hallucinations, though rarest, remain the most dangerous failure mode, since a fabricated dosing recommendation or a fictitious drug interaction could translate directly into harm.

The study is notable as much for its transparency as for its findings. In an unusual move, the authors disclose that the rater-by-item scoring matrix and the original response corpus were not archived after analysis, that the rule used to collapse three raters’ scores into one per-question value was never recorded, and that formal paired statistical tests such as McNemar’s test therefore became impossible. Copilot’s characteristic inline citations may also have compromised the blinding protocol. Rather than presenting plausible reconstructions as fact, the team reports these gaps openly, framing the work as an exploratory evaluation whose primary contribution is the multidimensional framework itself and a model for honest documentation of methodological limitations in early-stage LLM research.

What should be done with these results? The authors recommend hybrid architectures that ground chatbot outputs in structured, validated pharmaceutical databases rather than relying on parametric knowledge alone, along with dedicated fine-tuning on pharmacovigilance corpora so that safety alerts are generated reliably rather than left to general instruction-following. They also propose uncertainty detection mechanisms that would signal a model’s limits and steer users toward professional consultation, and even a continuous error-monitoring system modeled on existing pharmacovigilance infrastructure. The limitations are real: one medication, fifteen questions, one query per model, English only, and no testing with actual patients. The team, based in Francophone West and Central Africa, already plans a follow-up in French, where the stakes may be even higher. For now, the message to patients is clear: chatbots can be a reasonable starting point for basic medication facts, but when it comes to the warnings that keep you safe, they still cannot be trusted to speak up.

Subject of Research: Evaluation of large language models answering patient questions about the medication pregabalin

Article Title: A structured exploratory multidisciplinary evaluation of four large language models responding to frequently asked patient questions about pregabalin

Article References: A structured exploratory multidisciplinary evaluation of four large language models responding to frequently asked patient questions about pregabalin. (n.d.). https://doi.org/10.1007/s44163-026-02394-7

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02394-7

Keywords: large language models, ChatGPT-4o, Copilot, Gemini, Claude, pregabalin, patient safety, medication information, warning compliance, hallucination, pharmacovigilance, TRIPOD-LLM