Microsoft AI

10 gain · 21 problem · 2 high-evidence

1. gainHealthModerate evidenceImpact 74

   In a 20-case test of congenital anomalies and tumors, ChatGPT, Gemini and Copilot achieved 80-95% diagnostic accuracy with detailed anatomical descriptions, suggesting potential as supplementary radiological diagnostic support.

   On September 9, 2026, a peer-reviewed comparative study reported testing ChatGPT, Gemini, and Microsoft Copilot on 20 radiological cases split between congenital anomalies and tumors. Each system received the same questions and images and was scored for diagnostic accuracy and explanatory completeness, with ChatGPT scoring 19 correct, Gemini 17, and Copilot 16.

   Sep 10, 2026

   1 source

   • Clinical AnatomyPeer-reviewed
2. gainHealthModerate evidenceImpact 73

   Manus architecture using raw 3D CBCT data detected and correctly diagnosed 95% of jaw lesions in 97 patients, outperforming 2D panoramic inputs.

   A cross-sectional study tested four AI chatbots on 97 anonymized CBCT cases of jaw lesions, comparing performance on reconstructed 2D panoramic views and, for Manus, raw 3D DICOM data. Reports were scored for accuracy, relevance and feasibility, revealing statistically significant differences between systems.

   Aug 5, 2026

   1 source

   • Dentomaxillofacial RadiologyPeer-reviewed
3. gainHealthModerate evidenceImpact 69

   Pairing language models with the SCAI semantic knowledge pipeline raised accuracy on 313 USMLE sample questions for Gemini, Llama 3 70B, and DeepSeek-R1-Distill, with statistically significant gains on all three exam steps.

   Researchers tested four language models, Google Gemini, Microsoft Copilot, Meta Llama 3 70B, and DeepSeek-R1-Distill-Llama-70B, on official text-only USMLE sample questions from Steps 1, 2 CK, and 3. Each model was run with and without SCAI, a semantic knowledge system that supplies clinical context to the host model. Three of the four models improved significantly, while Copilot barely changed.

   Oct 17, 2026

   1 source

   • IEEE Journal of Biomedical and Health InformaticsPeer-reviewed
4. gainHealthModerate evidenceImpact 69

   Perplexity Pro and Copilot earned higher DISCERN information-quality medians (55.0 and 54.0) than Gemini 2.5 Pro (44.0) and ChatGPT-5 (42.0) on osteoporosis questions.

   Researchers put the same 25 standardized osteoporosis questions to ChatGPT-5, Gemini 2.5 Pro, Copilot and Perplexity Pro, producing 100 responses. Two specialists who did not know which model wrote each answer scored them with several quality and safety instruments, and automated indices measured readability.

   Oct 15, 2026

   1 source

   • Health Science ReportsPeer-reviewed
5. problemHealthModerate evidenceImpact 69

   All four chatbots wrote above the sixth-grade readability benchmark, and the safety review found clinically relevant omissions and an unsupported claim in the answers.

   Researchers put the same 25 standardized osteoporosis questions to ChatGPT-5, Gemini 2.5 Pro, Copilot and Perplexity Pro, producing 100 responses. Two specialists who did not know which model wrote each answer scored them with several quality and safety instruments, and automated indices measured readability.

   Oct 15, 2026

   1 source

   • Health Science ReportsPeer-reviewed
6. gainEducationModerate evidenceImpact 69

   All five tested LLMs answered every one of 20 Apply- and Analyze-level anatomy questions correctly, while students averaged about two-thirds accuracy on the same items.

   Researchers at Bursa Uludag University Faculty of Medicine gave 20 clinically oriented anatomy multiple-choice questions to five LLMs: ChatGPT-5, Gemini 2.5 Pro, Claude, Microsoft Copilot and DeepSeek R1. Each model answered every question correctly, while students from the 2025-2026 academic year averaged 67.95%. Student accuracy dropped from Apply-level to Analyze-level questions, but the LLMs stayed at ceiling.

   Oct 15, 2026

   1 source

   • Clinical AnatomyPeer-reviewed
7. problemEducationModerate evidenceImpact 69

   When LLMs rated each other's anatomy answers, they scored them as good as or better than human observers did, never lower, which undermines the reliability of LLM-based peer assessment.

   Researchers at Bursa Uludag University Faculty of Medicine gave 20 clinically oriented anatomy multiple-choice questions to five LLMs: ChatGPT-5, Gemini 2.5 Pro, Claude, Microsoft Copilot and DeepSeek R1. Each model answered every question correctly, while students from the 2025-2026 academic year averaged 67.95%. Student accuracy dropped from Apply-level to Analyze-level questions, but the LLMs stayed at ceiling.

   Oct 15, 2026

   1 source

   • Clinical AnatomyPeer-reviewed
8. problemHealthModerate evidenceImpact 68

   Chatbots sometimes confused similar congenital anomalies and provided less detailed anatomical descriptions in complex tumor cases, requiring caution and verification by qualified professionals before clinical use.

   On September 9, 2026, a peer-reviewed comparative study reported testing ChatGPT, Gemini, and Microsoft Copilot on 20 radiological cases split between congenital anomalies and tumors. Each system received the same questions and images and was scored for diagnostic accuracy and explanatory completeness, with ChatGPT scoring 19 correct, Gemini 17, and Copilot 16.

   Sep 10, 2026

   1 source

   • Clinical AnatomyPeer-reviewed
9. problemHealthModerate evidenceImpact 67

   OpenEvidence and ChatGPT-5 demonstrated greater agreement with the expert reference standard, although clinically significant diagnostic errors were observed across all evaluated systems.

   Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs. Material and methods Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt.

   Aug 23, 2026

   1 source

   • Journal of ProsthodonticsPeer-reviewed
10. gainHealthModerate evidenceImpact 67

    Guideline-specific and general-purpose LLMs produced responses broadly consistent with EAU erectile dysfunction recommendations, with Gemini 2.5 Pro and EAU Guidelines Bot achieving the highest composite scores.

    On 2026-08-07, a peer-reviewed comparative study tested five AI systems including the EAU Guidelines Bot, ChatGPT-5, Gemini 2.5 Pro, Copilot - Smart GPT-5, and Perplexity Pro on 13 questions drawn from strongly recommended EAU erectile dysfunction statements. Three senior reviewers rated each answer for relevance, clarity, structure, clinical utility, and factual accuracy.

    Aug 10, 2026

    1 source

    • International Journal of Impotence ResearchPeer-reviewed
11. problemHealthModerate evidenceImpact 67

    Performance varied significantly across models and domains, with Copilot and Perplexity scoring lower and greater inconsistency in clarity, structure, and clinical utility.

    On 2026-08-07, a peer-reviewed comparative study tested five AI systems including the EAU Guidelines Bot, ChatGPT-5, Gemini 2.5 Pro, Copilot - Smart GPT-5, and Perplexity Pro on 13 questions drawn from strongly recommended EAU erectile dysfunction statements. Three senior reviewers rated each answer for relevance, clarity, structure, clinical utility, and factual accuracy.

    Aug 10, 2026

    1 source

    • International Journal of Impotence ResearchPeer-reviewed
12. problemHealthModerate evidenceImpact 67

    Copilot and Claude produced the least accurate reports for jaw lesions, highlighting significant discrepancies in diagnostic accuracy across chatbots.

    A cross-sectional study tested four AI chatbots on 97 anonymized CBCT cases of jaw lesions, comparing performance on reconstructed 2D panoramic views and, for Manus, raw 3D DICOM data. Reports were scored for accuracy, relevance and feasibility, revealing statistically significant differences between systems.

    Aug 5, 2026

    1 source

    • Dentomaxillofacial RadiologyPeer-reviewed