# Healthcare & Life Sciences Published articles for Healthcare & Life Sciences. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Health Plans: Your BI Tells You MLR Moved. Can Your AI Tell You Why? DevFeed: [Health Plans: Your BI Tells You MLR Moved. Can Your AI Tell You Why?]() Original publisher: [Read original article]() Author: Aaron Zavora; Jonathan Thompson Published: 2026-09-11T18:26:59Z Content type: article Language: en Sources: [Databricks]() Topics: [databricks](), [Conversational AI](), [Artificial Intelligence](), [data]() Tags: [ai](), [conversational-ai](), [dashboards](), [data](), [databricks](), [healthcare](), [healthcare-life-sciences](), [industries](), [metrics]() ### AI overview The article explains how AI can help health plan finance teams move beyond BI dashboards that identify a higher medical loss ratio (MLR) and instead determine the causes and appropriate corrective actions. It describes Databricks and Abacus as combining governed enterprise data with payer-specific data, business context, and operational knowledge, while conversational AI lets finance leaders ask questions in plain language and receive answers more quickly. ### Source excerpt A health plan CFO closes the month after the usual round of extracts, spreadsheets,... ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents DevFeed: [Developing NVIDIA Holoscan Applications with CLI, Skills, and AI Coding Agents]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-19T22:22:37Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Holoscan](), [AI-assisted coding](), [Command-line interface](), [Artificial Intelligence](), [real-time](), [Medical imaging](), [Robotics]() Tags: [agentic-ai-generative-ai](), [agents](), [ai-coding](), [clara](), [cli](), [computer-vision-video-analytics](), [edge](), [edge-computing](), [featured](), [healthcare-life-sciences](), [holoscan](), [medical-devices](), [medical-imaging](), [monai](), [nvidia](), [real-time](), [robotics](), [skills](), [video-analytics]() ### AI overview An NVIDIA Holoscan developer article about building real-time edge AI applications with CLI skills and AI coding agents, covering use cases including medical imaging and robotics. ### Source excerpt NVIDIA Holoscan is a platform for building real-time AI applications at the edge, from medical imaging to robotics. HoloHub is its companion repository: a... ## Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit DevFeed: [Accelerating End-to-End Co-Folding Performance with NVIDIA BioNeMo Agent Toolkit]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-10T13:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering](), [cpu]() Tags: [agentic-ai-generative-ai](), [ai-agents](), [bionemo](), [cuda](), [developer-tools-techniques](), [drug-discovery](), [featured](), [gpu](), [healthcare-life-sciences](), [hpc-scientific-computing](), [inference](), [multi-gpu](), [nvidia](), [performance](), [simulation-modeling-design]() ### AI overview The article describes NVIDIA BioNeMo Agent Toolkit accelerations for biomolecular co-folding workflows, focusing on faster MSA generation, inference, serving, and multi-GPU scaling for drug-discovery workloads. ### Source excerpt Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein... ## Training mRNA Language Models Across 25 Species for $165 DevFeed: [Training mRNA Language Models Across 25 Species for $165]() Original publisher: [Read original article]() Author: Maziyar Panahi Published: 2026-03-31T08:23:44Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Open Source](), [Language models](), [Transformer](), [Tooling](), [Code]() Tags: [ai](), [code](), [healthcare-life-sciences](), [language-models](), [models](), [open-source](), [tooling](), [training]() ### AI overview OpenMed describes an open-source protein AI pipeline covering structure prediction, amino acid sequence design, and codon optimization for mRNA expression. The article compares transformer architectures for codon-level language modeling, reports CodonRoBERTa-large-v2 as the strongest model in its experiments, and details scaling the system to 25 species with runnable code and results. ### Source excerpt By OpenMed, Open-Source Agentic AI for Healthcare & Life Sciences TL;DR: We built an end-to-end protein AI pipeline covering structure prediction, sequence design, and codon optimization. After comparing multiple transformer architectures for codon-level language modeling, CodonRoBERTa-large-v2 emerged as the clear winner with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT.