Google DeepMind

27 gain · 29 problem · 0 high-evidence

1. gainHealthModerate evidenceImpact 78

   Structural modeling tools such as AlphaFold and RoseTTAFold enable high-accuracy 3D prediction of proteins encoded by multidrug-resistant bacterial genomes, facilitating functional annotation and accelerating design of new antimicrobial compounds.

   By July 30 2026, a narrative review in Journal of Computer-Aided Molecular Design described the use of structural modeling and artificial intelligence for functional prediction of proteins encoded by multidrug-resistant bacterial genomes. It reported that tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating annotation of hypothetical proteins and identification of conserved domains and catalytic sites.

   Jul 31, 2026

   1 source

   • Journal of Computer-Aided Molecular DesignPeer-reviewed
2. problemHealthModerate evidenceImpact 78

   AI-driven modeling for antimicrobial resistance target discovery continues to face persistent challenges around experimental validation and genomic variability, limiting confirmation of predicted functions.

   By July 30 2026, a narrative review in Journal of Computer-Aided Molecular Design described the use of structural modeling and artificial intelligence for functional prediction of proteins encoded by multidrug-resistant bacterial genomes. It reported that tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating annotation of hypothetical proteins and identification of conserved domains and catalytic sites.

   Jul 31, 2026

   1 source

   • Journal of Computer-Aided Molecular DesignPeer-reviewed
3. 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
4. gainHealthModerate evidenceImpact 74

   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
5. gainHealthModerate evidenceImpact 72

   Large language models directed patients with musculoskeletal complaints to currently practicing, specialty-appropriate providers in the requested city, with ChatGPT achieving 100% appropriateness in the tested queries.

   Researchers prompted ChatGPT, DeepSeek, and Gemini with standardized musculoskeletal complaints for Lynchburg, VA and Trumbull, CT, and judged whether recommended physicians were currently practicing locally in the relevant specialty and whether phone numbers were correct. By the August 13, 2026 publication date, ChatGPT was appropriate in all 17 recommendations, while Gemini and DeepSeek were appropriate in 43% and 40% of recommendations respectively.

   Aug 14, 2026

   1 source

   • JAAOS: Global Research and ReviewsPeer-reviewed
6. gainScienceModerate evidenceImpact 72

   AI-driven enzyme engineering enables rapid, precise design of synthetic synzymes that catalyze non-natural reactions for use in pharmaceuticals, biofuels, and environmental remediation.

   As of December 2025, researchers synthesized AI methods for enzyme engineering, using structure-prediction, generative, and reinforcement learning models combined with high-throughput screening to design and optimize enzymes, including synthetic synzymes for non-natural reactions.

   Jul 20, 2026

   1 source

   • MoleculesPeer-reviewed
7. gainScienceHigh evidenceImpact 71

   A three-track neural network that jointly processes sequence, distance, and coordinate information enables accurate prediction of protein structures and protein-protein complexes, approaching DeepMind's accuracy.

   In work published August 20, 2021, researchers built on the CASP14-era DeepMind approach to protein folding. They developed RoseTTAFold, a three-track network that processes sequence, distance, and coordinate information simultaneously, achieving accuracies approaching those of DeepMind.

   Jul 13, 2026

   5 sources

   • SciencePeer-reviewed
   • Annual Review of BiophysicsPeer-reviewed
   • Acta Crystallographica Section D Structural BiologyPeer-reviewed
   • MedComm – Future MedicinePeer-reviewed
   • Quarterly Reviews of BiophysicsPeer-reviewed
8. gainScienceHigh evidenceImpact 71

   AlphaFold provides a computational method that regularly predicts protein three-dimensional structures with atomic accuracy from amino acid sequence alone, even without homologous structures.

   On July 15, 2021, Nature published the AlphaFold study describing a redesigned neural network that predicts the three-dimensional structure a protein will adopt based solely on its amino acid sequence. The authors reported validation in CASP14, where the model regularly achieved atomic accuracy even when no homologous structure was available and performed competitively with experimental structures.

   Jul 13, 2026

   5 sources

   • NaturePeer-reviewed
   • International Journal of Molecular SciencesPeer-reviewed
   • MoleculesPeer-reviewed
   • Nature MethodsPeer-reviewed
   • Genome BiologyPeer-reviewed
9. gainHealthModerate evidenceImpact 69

   Large language models including ChatGPT-o3, ChatGPT-5.2, Gemini 3 and DeepSeek provided generally acceptable clinical accuracy when answering 30 frequently asked patient questions about robotic-assisted total knee arthroplasty.

   A September 2026 peer-reviewed study in The Knee compared four large language models on 30 common patient questions about robotic-assisted total knee arthroplasty, evaluating responses with DISCERN, QAMAI, a 5-point clinical accuracy scale, and PEMAT and Flesch-Kincaid readability measures.

   Sep 13, 2026

   1 source

   • The KneePeer-reviewed
10. problemHealthModerate evidenceImpact 69

    LLM-generated answers to patient questions about robotic-assisted total knee arthroplasty remained above recommended patient-education reading levels and should be regarded as supplementary rather than standalone sources of information.

    A September 2026 peer-reviewed study in The Knee compared four large language models on 30 common patient questions about robotic-assisted total knee arthroplasty, evaluating responses with DISCERN, QAMAI, a 5-point clinical accuracy scale, and PEMAT and Flesch-Kincaid readability measures.

    Sep 13, 2026

    1 source

    • The KneePeer-reviewed
11. 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
12. gainHealthModerate evidenceImpact 68

    Three large language models achieved higher accuracy than a panel of hip preservation experts on a 21-item consensus-based questionnaire covering femoroacetabular impingement syndrome, hip dysplasia and microinstability.

    A peer-reviewed study published September 7, 2026 compared three large language models to 10 international hip preservation experts on a 21-item questionnaire derived from consensus guidelines for femoroacetabular impingement, dysplasia and microinstability. Gemini scored 100%, ChatGPT 98.4% and Claude 96.8% versus 90.5% for experts, with models also showing higher intra-item agreement.

    Sep 8, 2026

    1 source

    • Knee Surgery, Sports Traumatology, ArthroscopyPeer-reviewed