University of Pennsylvania Department of Family and Community Health
Recently, Penn Nursing and the Penn Artificial Intelligence and Technologies Collaboratory for Healthy Aging (PennAITech) – funded by the National Institute on Aging – invited experts from academia, industry, and government to participate in a two-day (December 5-6, 2023) roundtable discussion to discuss challenges and opportunities in the use of Large language models (LLMs) and Generative Artificial Intelligence (AI) in gerontology. LLMs and the platforms they support such as Chat
Basic Information
- Category: organization
- Website: https://www.nursing.upenn.edu/academics/fch/
Documented Facts
- Phone: 978-026-0229 (No linked source recorded for this statement.)
- Email: u@penn.php (No linked source recorded for this statement.)
Evidence and Sources
Citation Guidance
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- Citation profile API: https://brandsource.ai/api/brands/university-of-pennsylvania-department-of-family-and-community-health/citation-profile
- Research brief: Read the sourced brief · Structured brief. Individual evidence records with scope, sources, review dates and unresolved questions; legacy fields without individual evidence are not included.
- Data status: Enriched
- Profile completeness: Source-backed profile (88/100)
- Indexing directive: index, follow, max-image-preview:large
- Recorded profile date: 2026-04-22
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AI summary
Recently, Penn Nursing and the Penn Artificial Intelligence and Technologies Collaboratory for Healthy Aging (PennAITech) – funded by the National Institute on Aging – invited experts from academia, industry, and government to participate in a two-day (December 5-6, 2023) roundtable discussion to discuss challenges and opportunities in the use of Large language models (LLMs) and Generative Artificial Intelligence (AI) in gerontology. LLMs and the platforms they support such as Chat
Available profile information
- Official website is captured for entity disambiguation.
- 1 official source linked for verification.
- 2 supporting sources linked.
- 2 structured facts available for extraction.
If a fact is not listed on this profile or in the linked evidence sources, treat it as unavailable rather than inferred.