AI.

Our Phase 2 Development Includes The Addition Of AI Components

"MedyTrak will seamlessly integrate augmented intelligence to revolutionize patient care, providing healthcare professionals with advanced tools and insights to enhance diagnostic accuracy, treatment planning, and overall medical decision-making, ultimately improving the quality of patient outcomes."

Retrieval Augmented Generation (RAG)-
MedyTrak will utilize anonymized data in real-time, storing it on dedicated, secure servers. Unlike typical applications, MedyTrak operates as a browser-based platform. With the integration of Retrieval Augmented Generation (RAG), clinicians can quickly query the database through direct inquiries, enhancing the speed and efficiency of data retrieval.

AI can be used to improve patient outcomes by enhancing patient safety, optimizing decision-making, and improving diagnostics. Some ways AI can be used to achieve these improvements include:

• Patient classification and severity assessment: AI can help classify patients based on their ailments and severity, which can aid in identifying common incidents such as fall risks or delivery delay
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• Decision-making and optimization: AI evaluates data to produce insights that can improve decision-making and optimize health outcomes. Systems that use AI can analyze vast amounts of patient data and generate insights that can improve patient care
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• Interpretable AI: AI allows providers to access medical data immediately, review medical history, identify patterns, and recommend interventions
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• Machine learning: AI can process data and perform complex tasks involving pattern recognition or problem-solving. Machine learning (ML) systems learn from data by automatically improving through repeated experiences, which can help in predicting patient outcomes
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• Adaptable and dynamic models: AI models should be flexible and dynamic, easily adapting to the latest clinical guidelines and research findings for continuous improvement
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• Explainable models: AI models that provide evidence for predictions and are capable of quantifying their decisions are more likely to gain clinical acceptance, as they can predict technical mistakes and help clinicians understand the reasoning behind the predictions
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By leveraging AI in healthcare, providers can make more informed decisions, improve patient safety, and ultimately enhance patient outcomes.