Page 28 - Inovacije i izazovi u obrazovanju i sestrinskoj skrbi - KNJIGA SAŽETAKA

INOVACIJE I IZAZOVI U OBRAZOVANJU I SESTRINSKOJ SKRBI - KNJIGA SAŽETAKA

Edith Coronado Martinez, PhD
Vydiant, Inc.
Lausanne, Switzerland

Uses of AI, Machine Learning, and Large Language
Models in Medical Applications

The rapid evolution of artificial intelligence (AI) and machine learning (ML), particularly in
the fields of Natural Language Processing (NLP) and large language models (LLMs), is
transforming the medical landscape, offering unprecedented opportunities to enhance
diagnostics, treatment, and patient care. Here, we explore the integration of AI, ML, and LLMs
into various medical applications, highlighting their potential to revolutionize healthcare by
improving accuracy, personalization, and efficiency.

NLP and LLMs, such as GPT-4, significantly expand the capabilities of AI in healthcare by
processing and analyzing vast amounts of unstructured clinical text, including electronic health
records (EHRs), medical literature, and patient notes. These models can extract valuable
insights that improve decision-making and patient outcomes. For instance, NLP algorithms can
identify key clinical information, such as symptoms, diagnoses, and treatment plans, enabling
more accurate and efficient patient documentation. Moreover, LLMs can assist healthcare
professionals by summarizing patient records, generating medical reports, and providing
decision support based on the latest medical literature.

In addition to diagnostics, AI and ML are playing a pivotal role in personalized medicine. By
analyzing vast amounts of patient data, including genetic profiles, lifestyle factors, and
historical treatment outcomes, these technologies can generate tailored treatment
recommendations. This personalized approach ensures that therapies are more effective and
minimizes adverse reactions, aligning with the goals of precision medicine. Moreover,
predictive analytics models are being utilized to forecast disease progression and patient
responses to treatments, enabling proactive interventions and optimizing resource allocation.

The integration of NLP and LLMs into healthcare also holds promise for enhancing patient
engagement. These technologies can provide personalized health advice and answer patient
queries in real-time, improving patient understanding and adherence to treatment plans.
Additionally, NLP-driven tools can facilitate patient education, simplifying complex medical
jargon and empowering patients to take an active role in their healthcare.

Overall, the integration of NLP and LLMs with AI and ML technologies is poised to play a
crucial role in advancing medical applications, ultimately leading to better healthcare outcomes
and more efficient healthcare systems.

Key words: large language models (LLM), Natural Language Processing(NLP), ChatGPT4.0,
medical diagnostics, Generative Artificial Intelligence

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