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Continued research and development in this area will be crucial to unlocking the full potential of SLMs in revolutionising healthcare AI. Five examples of small language models in healthcare AI Clinical Decision Support: Example: An SLM trained on a specific disease (e.g.,
Artificial Intelligence is utilized in various areas of healthcare, ranging from drugdevelopment to medical image analysis. By examining vast amounts of patient data, healthcare professionals can recognize risk factors, predict outcomes, and develop personalized treatment plans.
Furthermore, by fostering connectivity and automating tasks, DHTs reduce administrative burdens, accelerate recruitment, and improve overall trial transparency, ultimately contributing to faster and more reliable drugdevelopment. In a recent review paper appeared on Nature , Mittermaier et al. in 2010 to 11.4%
The treatment algorithms are personalized for each patient based on machine learning and ensure the right drug dosages and treatments for the right patient at the right time. For example, the Brigham Home Hospital program leveraged Biofourmis’ AI-based technology to improve outcomes while lowering costs by 38%, the company reported.
Examples include pacemakers, insulin pumps, and surgical robots. Digital health: This refers to the use of technology to deliver healthcare services remotely. Examples include telehealth, e-prescriptions, and patient portals. Biotechnology: This is the use of living organisms to develop new medical products and services.
In dentistry, for example, AI is rapidly improving doctor-patient communication, increasing patient trust and compliance with recommended care––delivering a subtle but impactful boost to healthcare equity by reducing disparities in how individuals perceive the dental healthcare system and, in turn, manage their own dental health.
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