Large Language Models (LLMs) stand on the brink of reshaping the field of aging and dementia care, challenging the one-size-fits-all paradigm with their capacity for precision medicine and individualized treatment strategies. The "Large Pre-Trained Models with a Focus on AD/ADRD and Healthy Aging" symposium, organized by the National Institute on Aging and the Johns Hopkins Artificial Intelligence & Technology Collaboratory for Aging Research, served as a platform for exploring this potential. The symposium brought together diverse experts to discuss the integration of LLMs in aging and dementia care. They highlighted the roles LLMs can play in clinical decision support and predictive analytics, while also addressing critical ethical concerns including bias, privacy, and the responsible use of artificial intelligence (AI). The discussions focused on the need to balance technological advancement with ethical considerations in AI deployment. In conclusion, the symposium projected a future where LLMs not only revolutionize healthcare practices but also pose significant challenges that require careful navigation.
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http://dx.doi.org/10.1093/gerona/glae176 | DOI Listing |
iScience
January 2025
Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, the College of Life Sciences, Northwest University, Xi'an 710069, China.
Bacteriophages (phages) are increasingly viewed as a promising alternative for the treatment of antibiotic-resistant bacterial infections. However, the diversity of host ranges complicates the identification of target phages. Existing computational tools often fail to accurately identify phages across different bacterial species.
View Article and Find Full Text PDFClin Transl Radiat Oncol
March 2025
Department of Radiation Oncology, Maria Sklodowska-Curie National Research Institute of Oncology, Wawelska 15B, 02-034 Warsaw, Poland.
Background And Purpose: Pediatric radiotherapy patients and their parents are usually aware of their need for radiotherapy early on, but they meet with a radiation oncologist later in their treatment. Consequently, they search for information online, often encountering unreliable sources. Large language models (LLMs) have the potential to serve as an educational pretreatment tool, providing reliable answers to their questions.
View Article and Find Full Text PDFIdentifying immunosuppressed patients using structured data can be challenging. Large language models effectively extract structured concepts from unstructured clinical text. Here we show that GPT-4o outperforms traditional approaches in identifying immunosuppressive conditions and medication use by processing hospital admission notes.
View Article and Find Full Text PDFGlob Epidemiol
June 2025
Business Analytics (BANA) Program, Business School, University of Colorado, 1475 Lawrence St. Denver, CO 80217-3364, USA.
AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results. This paper demonstrates the potential of large language models (LLMs), such as ChatGPT, to facilitate statistical analyses, including survival data analyses, for health risk assessments. Through AI-guided analyses using relatively recent and advanced methods such as Individual Conditional Expectation (ICE) plots using Random Survival Forests and Heterogeneous Treatment Effects (HTEs) estimated using Causal Survival Forests, population-level exposure-response functions can be disaggregated into individual-level exposure-response functions.
View Article and Find Full Text PDFInt J Nurs Stud Adv
June 2025
Research Group Healthy Ageing, Allied Health Care and Nursing and FAITH Research, Hanze University of Applied Sciences, Petrus Driessenstraat 3, 9714, CA, Groningen, The Netherlands.
Introduction: Oral health care of older people in long-term care facilities is insufficient, stressing the need for clear evidence-based implementation strategies to improve oral care. In 2013, a systematic review was performed and new evidence was published. This study aimed to gain insights into implementation strategies used to promote or improve oral health care for older people in long-term care facilities, explore their effectiveness and uncover strategy content in behavioral change techniques, and report the differences between the current results and those of the 2013 study.
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