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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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