Archive for the ‘Ai’ Category
Lectio Divina and the Unchristian Assumptions of AI – Patheos
Lectio Divina and the Unchristian Assumptions of AI Patheos
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Lectio Divina and the Unchristian Assumptions of AI - Patheos
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Beauty Standards Make Me Ashamed Of My Features & AI Makes It Worse – Refinery29
Beauty Standards Make Me Ashamed Of My Features & AI Makes It Worse Refinery29
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Beauty Standards Make Me Ashamed Of My Features & AI Makes It Worse - Refinery29
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Microsoft collaborates with Mass General Brigham and University of WisconsinMadison to further advance AI foundation models for medical imaging -…
Collaborations empower the healthcare industry to create a vast array of medical imaging copilot applications that will help enhance radiologists experiences and contribute to better patient outcomes
REDMOND, Wash. July 24, 2024 Microsoft Corp. on Wednesday announced collaborations with leading academic medical systems Mass General Brigham and the University of Wisconsin School of Medicine and Public Health along with its partnering health system, UW Health, to accelerate solving some of the biggest challenges in radiology and further advance AI in medical imaging to drive clinician efficiency and enable better health outcomes. The collaborations will foster research and innovation tied to the advancement of high-performing multimodal AI foundation models that empower the entire radiology ecosystem to build on top of the secure Microsoft Azure AI platform and extend the Nuance (a Microsoft company) suite of radiology applications, delivering a wide array of high-value medical imaging copilot applications.
Medical imaging plays a crucial role in healthcare. Health systems spend an estimated $65 billion each year on imaging,[1] and approximately 80% of all hospital and health system visits include at least one imaging exam related to more than 23,000 conditions.[2] Faced with challenges that the overall healthcare industry grapples with, including physician burnout and staffing shortages, healthcare organizations are looking to generative AI to help reduce workloads, enhance workflow efficiencies, and improve the accuracy and consistency of medical image analysis for care delivery, clinical trials recruitment and drug discovery. Generative AI in radiology also may help enhance patient experiences by reducing wait times for imaging results, further opening up access to care and improving the quality of care.
With the right multimodal data-enriched medical imaging foundation models, Microsoft and its partners will explore how advanced algorithms and applications can help radiologists and other clinicians interpret medical images, as well as assist with report generation, disease classification and structured data analysis.
Microsoft has long been focused on the potential of providing high-performing first- and third-party advanced foundation models and copilot experiences across the ecosystem to empower everyone on the planet to achieve more. Additionally, Microsoft has been an innovator in the space of medical imaging research and biomedical natural language processing, collaborating with experts in medicine to democratize AI and empower researchers, hospitals, life science organizations and healthcare providers to develop new models and systems.
Through these collaborations, researchers and clinicians at Mass General Brigham, UW School of Medicine and Public Health, and UW Health will work with Microsoft to further advance state-of-the-art multimodal foundation models. The organizations will collaborate on the development, testing and validation of the latest breakthrough technology, deploying real-world use cases into clinical workflows3 including via Nuances PowerScribe radiology reporting platform, used by the majority of radiologists in the U.S., and the Nuance Precision Imaging Network, which offers a single point of access to automate and scale use of third-party medical imaging AI models for a range of modalities and specialties.
Generative AI has transformative potential to overcome traditional barriers in AI product development and to accelerate the impact of these technologies on clinical care. As healthcare leaders, we need to carefully and responsibly develop and evaluate such tools to ensure high-quality care is in no way compromised, said Keith J. Dreyer, D.O., Ph.D., chief data science officer and chief imaging officer at Mass General Brigham and leader of the Mass General Brigham AI business. Foundation models fine-tuned on Mass General Brighams vast multimodal longitudinal data assets can enable a shorter development cycle of AI/ML-based software as a medical device and other clinical applications, for example, to automate the segmentation of organs and abnormalities in medical imaging and increase radiologists efficiency and consistency.
Our institutions have a reputation for embracing technical innovations as opportunities to lead the transformation of our field with new scientific discovery and improvement in clinical care, said Scott Reeder, M.D., Ph.D., chair of the Department of Radiology, University of Wisconsin School of Medicine and Public Health, and radiologist at UW Health. We are excited to collaborate with Microsoft on the development, validation and thoughtful clinical investigation of generative AI in the medical imaging space. Our focus is to bridge the gap within medical imaging from innovation to patient care in ways that improve outcomes and make innovative care more accessible.
We are proud to announce our expanded collaborations with leading institutions like Mass General Brigham and UW. Along with other industry partners, our joint efforts aim to leverage the power of imaging foundation models to improve experiences and workflow efficiency across the radiology ecosystem in a way that is reliable, transparent and secure, said Peter Durlach, corporate vice president, Microsoft Health and Life Sciences. Together, we are not only advancing medical imaging, but also helping deliver more accessible and better-quality patient care in a very resource-constrained environment.
The industry continues to see rapid advancements in generative AI in radiology and other imaging specialties. With these advances comes an even greater responsibility to prioritize patient privacy and build systems guided by Microsofts Responsible AI principles. In addition to building our own AI systems responsibly and in ways that warrant peoples trust, we empower our customers with tools and features to do the same. We invest in our customers responsible AI goals in three ways:
The collaborations with Mass General Brigham, UW and many other industry partners aim to accelerate the development of high-performing foundation models for medical imaging that support and enable the greater healthcare ecosystem in a way that adheres to Microsofts responsible AI principles. Read more in our 2024 Responsible AI Transparency Report.
Microsoft (Nasdaq MSFT @microsoft) creates platforms and tools powered by AI to deliver innovative solutions that meet the evolving needs of our customers. The technology company is committed to making AI available broadly and doing so responsibly, with a mission to empower every person and every organization on the planet to achieve more.
1 JAMA.2012;307(22):2400-2409. doi:10.1001/jama.2012.5960
2 Healthcare, Definitive. Healthcare Analytics & Provider Data | Definitive Healthcare. Definitive Healthcare Database of Hospitals & Healthcare Providers, http://www.definitivehc.com
3 Subject to appropriate regulatory approvals
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NSF announces new AI test beds initiative to advance safety and security of AI technologies – National Science Foundation (.gov)
The U.S. National Science Foundation announces the launch of a new initiative that will invest in the development of artificial intelligence-ready test beds, a critical infrastructure designed to propel responsible AI research and innovation forward. These test beds, or platforms, will allow researchers to study new AI methods and systems in secure, real-world settings. The initiative calls for planning grants from the research community to accelerate the development of the test beds.
The initiative is aligned with Executive Order 14410 on the "Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence," signed in October 2023, underscoring the importance of creating a robust ecosystem for AI development that prioritizes safety, security and trustworthy considerations. The executive order emphasizes the extraordinary potential of AI to address urgent societal challenges while also highlighting the risks associated with possible irresponsible use. Recognizing this dual potential, NSF's AI-ready test beds initiative is set to enhance and advance the essential infrastructure needed for researchers to develop, test and refine responsible AI systems in real-world settings.
"Artificial intelligence holds incredible promise for advancing numerous fields, but its development must be guided by rigorous testing and evaluation in applications that involve decisions about, or contact with, people in the real world," said NSF Director Sethuraman Panchanathan. "With this initiative, NSF is demonstrating its commitment to innovate in AI and also ensure that those innovations are safe, secure and beneficial to society and our economy."
AI-ready test beds create an environment where AI researchers can deploy and assess the impact of their work and study the societal and economic impacts of AI-powered solutions, including various risks related to security, safety, privacy and fairness. For example, an AI-ready test bed may enable a researcher to evaluate a new AI solution for decision-making in a transportation scenario, or a test bed could allow an AI researcher to create new weather models and visualizations and assess them with meteorologists in the field. The infrastructure allows the researcher to innovate safely and collect real-world evidence that is beneficial to the intended users.
Projects funded by the initiative will lay the framework for providing researchers with scalable, real-world environments to test novel AI methods and their impacts. These test beds will support interdisciplinary collaborations, bringing together private AI laboratories, academia, civil society and third-party evaluators to support the design, development and deployment of AI systems, including associated privacy-enhancing technologies.
The initiative will offer planning grants to cultivate research teams that actively address the expansion or enhancement of an existing test bed to evaluate the impact on and interaction with users of novel AI methods. These grants will facilitate the collection of preliminary data, team formation, design efforts and the development of governance and management plans for scalable AI-ready test beds.
NSF encourages proposal submissions from institutions in the Established Program to Stimulate Competitive Research (EPSCoR) jurisdictions and collaborative proposals led by NSF EPSCoR institutions. This approach aims to engage a wide array of perspectives and scientific talent in addressing national AI research challenges and opportunities.
To learn more, read the Dear Colleague Letter: https://www.nsf.gov/pubs/2024/nsf24111/nsf24111.jsp
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