AI and Digital Health Informatics Integration
Transforming Healthcare with AI: Innovate, Integrate, and Inspire
Skills you will gain:
AI and Digital Health Informatics Integration is a dynamic program designed to merge the technological prowess of artificial intelligence with the nuanced demands of health informatics. The curriculum introduces participants to core concepts of AI such as machine learning, natural language processing, and predictive analytics, and integrates these with health informatics systems, data management, and electronic health records.
Aim: The program aims to equip participants with advanced knowledge and skills at the intersection of AI and health informatics. It focuses on leveraging artificial intelligence to enhance data analysis, decision-making, and patient care in the healthcare sector, preparing participants for the digital transformation of healthcare services.
Program Objectives:
- Understand the integration of AI technologies in healthcare informatics.
- Develop skills in managing and analyzing health data using AI tools.
- Apply AI to enhance clinical decision-making and patient management.
- Explore ethical considerations and regulatory compliance in digital health.
- Foster innovation in designing digital solutions for healthcare challenges.
What you will learn?
Week 1: Foundations of AI and Digital Health Systems
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Introduction to AI, ML, and big data in healthcare
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Digital health architectures and system interoperability
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Health informatics standards: HL7, FHIR, and DICOM
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EHRs, telehealth, and mobile health (mHealth) platforms
Week 2: Data Science for Healthcare Applications
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Health data acquisition, cleaning, and integration
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Clinical data mining and pattern recognition
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Time-series and imaging data in diagnostics
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Deep learning models in biomedical signal processing
Week 3: AI-Driven Decision Support and Risk Modeling
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Clinical Decision Support Systems (CDSS)
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Predictive analytics for patient outcomes
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AI tools for early diagnosis and treatment planning
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Ethics, fairness, and bias in algorithmic healthcare
Week 4: Translational AI, Regulations, and Innovation
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AI implementation in hospital and public health workflows
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Regulatory frameworks (FDA, CE, NDHM) and data privacy
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Case studies: Digital therapeutics, RPM, and virtual care
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Future trends: Digital twins, federated learning, and explainable AI
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Intended For :
- Undergraduate degree in Computer Science, Healthcare Informatics, or related fields.
- Healthcare professionals seeking to integrate AI technologies in their practice.
- Technologists and data scientists interested in health data applications.
Career Supporting Skills