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.
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, 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.
Major news from Nature Medicine: researchers have released SleepFM, a foundation model that "learns the language of sleep". By integrating brain, heart, and respiratory signals, the model achieves a C-Index of 0.85 for dementia and 0.84 for all-cause mortality prediction. This is how we scale proactive medicine.
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.
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
Healthcare providers are turning to agentic AI to achieve a "touchless" Revenue Cycle Management (RCM) system. Unlike traditional generative AI, agentic AI can autonomously make decisions and execute complex end-to-end processes, potentially reducing collection costs by 30% to 60% while allowing staff to focus on high-value patient care.
