Biotechnology and Medicine
AI has long been deeply applied in the fields of biology and healthcare, with examples like the once-popular IBM Watson AI for medical imaging. The recent explosive advancements in AI have propelled its applications in biotechnology and healthcare to new heights.
Biotechnology
Nvidia Evo 2 Protein Design
https://blogs.nvidia.com/blog/evo-2-biomolecular-ai/
NVIDIA's introduces Evo 2, a cutting-edge biomolecular AI model designed to accelerate drug discovery and advance biological research through generative design and protein optimization.
AlphaFold 3
https://blog.google/technology/ai/google-deepmind-isomorphic-alphafold-3-ai-model/
AlphaFold 3, developed by Isomorphic Labs and Google DeepMind, predicts the structure and interactions of all life’s molecules with unprecedented accuracy. It models large biomolecules like proteins, DNA, and RNA, as well as small molecules (ligands), enabling insights into biological processes and accelerating drug discovery. The new AlphaFold Server, free for non-commercial research, allows scientists to use these capabilities. This model significantly improves upon previous methods, with potential applications in genomics, drug design, and more resilient crops.
For more information, visit Isomorphic Labs.
Updates:
Google DeepMind releases code behind its most advanced protein prediction program
Link: https://github.com/google-deepmind/alphafold3
Medical
FDA Elsa
Google Med Gemini
https://www.forbes.com/sites/talpatalon/2024/05/01/med-geminis-lions-roar/?sh=10e5238e3d72
Google Rad Explain
https://huggingface.co/spaces/google/rad_explain
Google has just launched Rad Explain, a Hugging Face Space that utilizes its MedGemma model to translate complex radiology reports into clear, patient-friendly language, enhancing medical communication and accessibility.
Agent Hospital
Check the paper here: https://arxiv.org/pdf/2405.02957
The paper introduces "Agent Hospital," a simulated hospital environment where autonomous agents, powered by large language models, interact to emulate the complete medical treatment process, enabling doctor agents to learn and improve their medical decision-making through experience.
Toward robust mammography-based models for breast cancer risk
https://www.science.org/doi/10.1126/scitranslmed.aba4373
Generative AI in healthcare: Adoption trends and what’s next
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