Can New Tech Prevent Falls in People with Multiple Sclerosis?

Multiple sclerosis often causes falls, leading to pain, injury, and insecurity. Dr. Riley Bove and her research team, with the support of NLM, developed the mobile app MS-FIT. MS-FIT tracks falls, analyzes health data, and may help clinicians provide personalized strategies to prevent falls.

Introducing the New NIH Public Access Policy!

NIH is committed to making research accessible to all with a new Public Access Policy. The new policy eliminates the twelve-month embargo to accelerate access to published results. This milestone ensures patients, families, and researchers can benefit quickly from discoveries, promoting transparency and equitable access to federally funded research.

Renewing Our Focus on Women’s Health Research

Women have historically been excluded from biomedical research, but recent initiatives and funding commitments aim to change this. NIH has released a Notice of Special Interest (NOSI) for interdisciplinary research that addresses conditions affecting women. Check out NLM's funding opportunities!

Not A Blank Space: Policy Considerations for AI in Research

Forecasting policy needs is challenging due to rapid technological advances. NIH’s Data Management and Sharing Policy includes flexibility for evolving tools like artificial intelligence (AI), which enhances research but requires responsible use. Learn more about how NIH provides resources to ensure AI research adheres to existing policies and plans to update guidelines as technology progresses.

Fostering the Researchers of Tomorrow: The NLM Data Science and Informatics Scholars Program

This summer’s NLM Data Science and Informatics Scholars Program welcomes its largest cohort of interns who are collaborating on innovative research in bioinformatics, data analytics, and more. Learn how they’re shaping the future of biomedical research and how NLM can accelerate your own career!

The Secret to Research Trainee Success? Hands-On and Real-World Experiences through NLM

group of research trainees facing the camera

Ariah Long joined the NLM Associate Fellowship Program, a one-year residency program for career librarians, to grow as a biomedical librarian and advance equity and innovation. As she approaches the end of her internship, she shares the opportunities NLM offers to students at every stage of their education.

At 20, the African Journal Partnership Program Enters Young Adulthood

The African Journal Partnership Program—supported by NLM and the NIH Fogarty International Center—collaborates with African journal editors to promote worldwide dissemination of their journals. See how AJPP improves the visibility and quality of African research for equitable health promotion and disease prevention.

Appreciating the Distinction: Clinical Informatics Research vs. Clinical Research Informatics

Looping GIF of woman wearing glasses typing near search engine pop-up window reading "Does NLM fund..." The phrases "Clinical Informatics Research" and "Clinical Research Informatics" alternate in the search bar.

NLM funds grants related to both Clinical Informatics Research and Clinical Research Informatics—two fields that sound similar but are distinct in the ways they contribute to advancing health care delivery and patient outcomes. NLM’s Dr. Allison Dennis discusses how these concepts relate and how researchers can apply to related funding opportunities.

Continuous Innovation Framework for NLM’s Biomedical Data Repositories

Can original data repositories, system architectures, and web interfaces last "to infinity and beyond"? Not quite, but NLM is meeting the challenge to flexibly adapt to data growth, fulfill high usage demands, and provide more responsive user services. Learn how NLM has been adopting these strategies to keep our offerings innovative.

Translational AI: A Necessity and Opportunity for Biomedical Informatics and Data Science

From guiding diagnosis to advancing research, artificial intelligence (AI) has a lot of potential. But this potential currently represents more promise than reality, and the actual use of AI in health care is still modest. We must treat AI the same way as any other tool we use in health care: “Show us the evidence.”