Wikipedia

Draft:Generative AI for Personalized Medicine

GenAIS (Generative AI Supplements) is a type of generative AI designed for personalized medicine. Developed by Triangel Scientific from Silicon Valley, USA, this platform aims to improve the prevention and treatment of chronic diseases.

Background

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Generative artificial intelligence (generative AI) are AI technologies that can generate text, images, videos, and other data in response to prompts. These systems, trained on large datasets, identify patterns and structures, enabling them to create new, similar data. Notable examples include chatbots like ChatGPT, image generators like DALL-E, and video creators like Runway Gen-2.[1][2]

Capabilities

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GenAIS employs machine learning algorithms to analyze genetic polymorphisms and metabolic profiles. It integrates genetic data, biochemical markers, and medical histories to offer tailored recommendations. The platform uses advanced AI models like transformers and diffusion models, which have been instrumental in medical imaging, protein structure prediction, and clinical documentation [2] .

Working principles

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GenAIS is based on scientific research and client data to generate personalized recommendations. It considers genetic variations and metabolic pathways to optimize biochemical processes and address deficiencies. This approach is grounded in the principles of precision medicine, where treatments are tailored to individual patient profiles[1].

Clinical studies

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Studies indicate that using GenAIS enhances the prevention and treatment of chronic diseases. For example, one study showed reduced HbA1c levels and improved metabolic indicators in diabetic patients. Another study found that AI-guided supplement and medication recommendations were more effective than traditional methods in lowering LDL cholesterol and triglyceride levels [3][4][5].

Benefits for physicians

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GenAIS assists physicians by offering automated, scientifically-based recommendations, reducing workload, and increasing treatment accuracy, thus minimizing errors. It enhances clinical decision-making by providing data-driven insights, improving patient outcomes.[1]

Applications

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The platform can integrate with existing medical systems and is used to train medical personnel in the effective use of AI tools. This integration facilitates seamless data flow and enhances the overall efficiency of medical practice [1][2]

References

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  1. ^ a b c d Ghebrehiwet, Isaias; Zaki, Nazar; Damseh, Rafat; Mohamad, Mohd Saberi (2024-04-25). "Revolutionizing personalized medicine with generative AI: a systematic review". Artificial Intelligence Review. 57 (5): 128. doi:10.1007/s10462-024-10768-5. ISSN 1573-7462.
  2. ^ a b c Shokrollahi, Yasin; Yarmohammadtoosky, Sahar; Nikahd, Matthew M.; Dong, Pengfei; Li, Xianqi; Gu, Linxia (2023-10-01), A Comprehensive Review of Generative AI in Healthcare, arXiv:2310.00795
  3. ^ Pokushalov, Evgeny; Ponomarenko, Andrey; Smith, John; Johnson, Michael; Garcia, Claire; Pak, Inessa; Shrainer, Evgenya; Kudlay, Dmitry; Bayramova, Sevda; Miller, Richard (2024-06-26). "Efficacy of AI-Guided (GenAISTM) Dietary Supplement Prescriptions versus Traditional Methods for Lowering LDL Cholesterol: A Randomized Parallel-Group Pilot Study". Nutrients. 16 (13): 2023. doi:10.3390/nu16132023. ISSN 2072-6643. PMC 11243060. PMID 38999770.
  4. ^ Pokushalov, Evgeny; Ponomarenko, Andrey; Smith, John; Johnson, Michael; Garcia, Claire; Pak, Inessa; Shrainer, Evgenya; Kudlay, Dmitry; Bayramova, Sevda; Miller, Richard (January 2024). "Efficacy of AI-Guided (GenAISTM) Dietary Supplement Prescriptions versus Traditional Methods for Lowering LDL Cholesterol: A Randomized Parallel-Group Pilot Study". Nutrients. 16 (13): 2023. doi:10.3390/nu16132023. ISSN 2072-6643. PMC 11243060. PMID 38999770.
  5. ^ "ClinicalTrials.gov". clinicaltrials.gov. Retrieved 2024-07-16.
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