Top Tweets for #biomedicalAI
In the early hours of Friday, July 10, a team of researchers dropped the world's first general-purpose biomedical AI agent, which has the power to autonomously complete… https://t.co/8e2Dkr7sid #BiomedicalAI #MedicalInnovation #HealthTech #ArtificialIntelligence #AIResearch
Bad data is one of science's most expensive problems.
Reproducibility failures cost an estimated $28B/year in the US alone.
At DeSciLaunch, curated, verifiable datasets aren't a feature — they're foundational infrastructure. #AIScience #DeSci #ScienceResearch #SUI #BiomedicalAI
Check out my latest article on Linkedin: Enabling Trust: Best Practices for AI & Machine Learning in Healthcare https://t.co/4mPYhXCVcz
#HealthcareAI #MachineLearning #DigitalHealth #BiomedicalAI #MedTech
AI and machine learning are rapidly reshaping healthcare and biology—from improving diagnostics to accelerating drug discovery and decoding complex biological systems.
Explore the latest research in Research:
https://t.co/do7vZtx89B
#AIinHealthcare #MachineLearning #BiomedicalAI
Three research-analysis skills. Same biomedical protocol PDF.
Completely different interpretations of “evidence quality.”
🏆 Ranking:
1. Result Reliability Checker (AIPOCH)
2. citation-form-check
3. academic-verify
https://t.co/43n6PffudZ
#AIPOCH #BiomedicalAI #ResearchIntegrity #EvidenceAssessment #Bioinformatics #MachineLearning #ScientificWriting #ClinicalResearch #Omics #TranslationalMedicine
Don't miss our exciting keynote at @sewebmeda 🎤Prof. @FrankVanHarmele will show us how to use design patterns for neuro-symbolic medical decision support systems!
📍 2:00 PM, Room 1, May10, Dubrovnik @eswc_conf
https://t.co/nwQUoc6PE7
#KnowledgeGraphs #BiomedicalAI #healthcare
Don't miss our exciting keynote at #SeWeBMeDA2026! 📷Prof. Frank van Harmelen @FrankVanHarmele will show us how to use design patterns for neuro-symbolic medical decision support systems!
📷 May 10, Dubrovnik (co-located with @eswc_conf)
📷 https://t.co/nwQUoc6PE7
#BiomedicalAI
Join us on May 10 in Dubrovnik for an exciting afternoon of talks on neuro-symbolic AI for healthcare & more, co-located with @eswc_conf!
📷 Check out the full schedule: https://t.co/nwQUoc6PE7
#ESWC2026 #SemanticWeb #KnowledgeGraphs #BiomedicalAI #healthcare #lifescience
💥Highly recommended publication: "Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data"
🔗https://t.co/F3YRsHcVKJ
📌#FallRiskPrediction #DeepLearning #BiomedicalAI
💥Highly recommended publication: "Toward Interpretable Cell Image Representation and Abnormality Scoring for Cervical Cancer Screening Using Pap Smears"
🔗https://t.co/nb4we3S8VK
📌#Glaucoma #EyeHealthTech #BiomedicalAI
💥Highly recommended publication: "Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network"
🔗https://t.co/hqhUUYNBqX
🏫@EwhaWomansUniv
📌#BrainAgePrediction #DeepLearning #BiomedicalAI
Drug interactions, gene-disease links, protein functions — all buried in millions of papers in unstructured text.
OpenMed_HoC trains models to extract and structure that knowledge automatically.
#BiomedicalAI #DrugDiscovery #ClinicalNLP #OpenSource
This is foundational infrastructure for biomedical AI. Any team building a drug discovery tool, clinical knowledge graph, or literature monitoring system should be benchmarking here.
#BiomedicalAI #DrugDiscovery #ClinicalNLP
Drug interactions, gene-disease links, protein functions — all buried in millions of papers in unstructured text.
OpenMed_BioRED trains models to extract and structure that knowledge automatically.
#BiomedicalAI #DrugDiscovery #ClinicalNLP #OpenSource
Drug interactions, gene-disease links, protein functions — all buried in millions of papers in unstructured text.
OpenMed_HoC trains models to extract and structure that knowledge automatically.
#BiomedicalAI #DrugDiscovery #ClinicalNLP #OpenSource
Excited to share our new Nature Biotechnology Perspective: “Agentic AI and the rise of in silico team science in biomedical research.”
https://t.co/j2uLj3KFqa
#AgenticAI #BiomedicalAI #TeamScience #ComputationalBiology
Our perspectives piece on Agentic AI for biomedical research has been published in Nature Biotechnology https://t.co/PpFeGcvv0x #agenticAI #artificialintelligence #agents
Drug interactions, gene-disease links, protein functions — all buried in millions of papers in unstructured text.
OpenMed_SNOMED trains models to extract and structure that knowledge automatically.
#BiomedicalAI #DrugDiscovery #ClinicalNLP #OpenSource
The bottleneck in biomedical research isn't data — it's making sense of it. OpenMed_UMLS trains models to pull structured facts from free-text scientific literature.
#BiomedicalAI #DrugDiscovery #ClinicalNLP #OpenSource
This is foundational infrastructure for biomedical AI. Any team building a drug discovery tool, clinical knowledge graph, or literature monitoring system should be benchmarking here.
#BiomedicalAI #DrugDiscovery #ClinicalNLP
Biomedical literature grows by ~1 million papers per year. No human can read it all.
Models that can extract structured knowledge from that text are increasingly critical. OpenMed_DrugProt trains exactly that capability.
#BiomedicalAI #DrugDiscovery #ClinicalNLP #OpenSource
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