Tag: #lifesciences

Blog Guest authored by HonChong Chang, Director of Cloud & Devops at Bristol Myers Squibb With operations spread across five continents, global biopharmaceutical company Bristol Myers Squibb’s (BMS) cloud team had too much on their plate serving its multi-account, multi-region project teams, without enough resources or hours in the day. Manual workflows for incoming requests […]

Blog guest-authored by Mustaqhusain Kazi, Global Head of Roche Informatics Strategy and Digital Innovation, and Neeraj Agarwal, Distinguished Enterprise Architect, Foundational Tech, PHC and Science at Roche. In the 1990s, the “blockbuster model” reigned supreme, with pharmaceutical companies designing therapies for larger populations with a one-size-fits-all approach. Today, with increased digitization and an explosion of patient […]

The success of pharmaceutical and biotechnology organizations rests on their ability to bring new therapies to market, which requires submitting clinical trial data to regulatory agencies. However, it involves massive quantities of data and significant manual labor. To automate and optimize the process, life sciences organizations are leveraging curated solutions powered by the cloud—affording them […]

Cisco Vicenty, Case for Quality program manager at the U.S. Food and Drug Administration (FDA), joins Senthil Gurumoorthi, security assurance lead for Healthcare & Life Sciences at AWS, in a conversation about the FDA’s recently released guidance for its Case for Quality initiative. The two discuss what has changed in the guidance, what it means […]

Vignesh Shetty, Senior Vice President and General Manager of Edison AI & Platform at GE Healthcare, joins Taha Kass-Hout, MD, MS Vice President and Chief Medical Officer at Amazon Web Services (AWS), to discuss strategies for building a modern data strategy to fuel innovation and improve patient care. GE Healthcare provides medical technology, pharmaceutical diagnostics, […]

Population genomics initiatives amass a multitude of clinical, omics, and phenotypic data from diverse participants. This data is critical to a range of healthcare use cases, including biomarker discovery, drug repurposing, patient stratification, and precision medicine. However, strict data security and regulatory frameworks often make large-scale data migration unfeasible, creating a fragmented data landscape that […]

Digital transformation in medicine is paving the way for innovation across healthcare disciplines, and integrated diagnostics is one area seeing significant breakthroughs. Dan Sheeran, General Manager of Healthcare and Life Sciences at Amazon Web Services (AWS), sat down with Dr. Jonathan Usuka, Chief Strategy and Informatics Officer at REALM, to understand the drivers behind healthcare’s […]

New AWS for Health Guidance: Multi-modal and Multi-omics The new era of personalized health relies on data to guide more customized patient treatments, therapeutics, and diagnoses. Genomics sits at the core of personalized health, and by taking into account the individual variability among people and diseases, clinicians can create more personalized care journeys and targeted treatments. […]

Introduction Training medical computer vision (CV) models requires a scalable compute and storage infrastructure. Training a medical CV model is unique compared to training a CV model in other domains, as we described in the first part of this blog series. In this second post, we show you how we scale a medical semantic segmentation training […]

Introduction Computer vision (CV) and machine learning (ML) has been playing an increasingly import role in the field of medical imaging and radiology in the past decade. CV is the field that uses computers and programs to replicate human vision abilities such as recognition, classification, detection and measurement. Thanks to the advancement of deep learning […]