AI for Healthcare Training in Tashkent

This guided, live training in Tashkent (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists aiming to optimize models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

By the conclusion of this training, participants will be able to:

• Optimize AI models on healthcare datasets, including EMRs, imaging, and time-series data.
• Utilize transfer learning, domain adaptation, and model compression in medical scenarios.
• Handle privacy, bias, and regulatory compliance aspects in model development.
• Deploy and monitor optimized models in real-world healthcare environments.

Generative AI refers to technology capable of producing new content, including text, images, and recommendations, derived from specific prompts and data inputs.

This instructor-led live training, available online or on-site, is designed for beginner to intermediate-level healthcare professionals seeking to leverage generative AI and prompt engineering to enhance efficiency, accuracy, and communication within medical settings.

Upon completion of this training, participants will be able to:

• Grasp the core concepts of generative AI and prompt engineering.
• Utilize AI tools to streamline clinical, administrative, and research workflows.
• Ensure the ethical, secure, and compliant application of AI in healthcare environments.
• Refine prompts to obtain consistent and accurate outcomes.

Course Format

• Interactive lectures and discussions.
• Practical exercises and case study analysis.
• Hands-on experimentation with AI tools.

Customization Options

• To arrange customized training for this course, please contact us.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level data scientists and healthcare professionals who wish to leverage AI for advanced healthcare applications using Google Colab.

By the end of this training, participants will be able to:

• Implement AI models for healthcare using Google Colab.
• Use AI for predictive modeling in healthcare data.
• Analyze medical images with AI-driven techniques.
• Explore ethical considerations in AI-based healthcare solutions.

This instructor-led, live training in Tashkent (available online or on-site) is designed for healthcare professionals and researchers who wish to leverage ChatGPT to enhance patient care, streamline workflows, and improve healthcare outcomes.

By the end of this training, participants will be able to:

• Understand the fundamentals of ChatGPT and its applications in healthcare.
• Utilize ChatGPT to automate healthcare processes and interactions.
• Provide accurate medical information and support to patients using ChatGPT.
• Apply ChatGPT for medical research and analysis.

This instructor-led, live training in Tashkent (online or on-site) is aimed at beginner-level to intermediate-level healthcare professionals, data analysts, and policy makers who wish to understand and apply generative AI in the context of healthcare.

By the end of this training, participants will be able to:

• Explain the principles and applications of generative AI in healthcare.
• Identify opportunities for generative AI to enhance drug discovery and personalised medicine.
• Utilise generative AI techniques for medical imaging and diagnostics.
• Assess the ethical implications of AI in medical settings.
• Develop strategies for integrating AI technologies into healthcare systems.

Ollama serves as a streamlined platform for executing large language models directly on local infrastructure.

This instructor-led live training, available both online and onsite, targets intermediate-level healthcare professionals and IT teams looking to deploy, customize, and implement Ollama-based AI solutions within clinical and administrative settings.

Upon completing this training, participants will be able to:

• Install and configure Ollama to ensure secure usage in healthcare environments.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customize models to accommodate healthcare-specific terminology and tasks.
• Apply best practices for privacy, security, and regulatory compliance.

Format of the Course

• Interactive lecture and discussion.
• Hands-on demonstrations and guided exercises.
• Practical implementation in a sandboxed healthcare simulation environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

This instructor-led, live training in Tashkent (online or onsite) is tailored for intermediate to advanced healthcare professionals and AI developers seeking to deploy AI-driven healthcare solutions.

Upon completion of this training, participants will be able to:

• Understand the role of AI agents in healthcare and diagnostics.
• Develop AI models for medical image analysis and predictive diagnostics.
• Integrate AI with electronic health records (EHR) and clinical workflows.
• Ensure compliance with healthcare regulations and ethical AI practices.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level healthcare professionals and data scientists who wish to understand and apply AI technologies in healthcare environments.

By the end of this training, participants will be able to:

• Identify key healthcare challenges that AI can address.
• Analyze AI’s impact on patient care, safety, and medical research.
• Understand the relationship between AI and healthcare business models.
• Apply fundamental AI concepts to healthcare scenarios.
• Develop machine learning models for medical data analysis.

Agentic AI is an approach where AI systems plan, reason, and take tool-using actions to accomplish goals within defined constraints.

This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare and data teams who wish to design, evaluate, and govern agentic AI solutions for clinical and operational use cases.

By the end of this training, participants will be able to:

• Explain agentic AI concepts and constraints within healthcare contexts.
• Design safe agent workflows incorporating planning, memory, and tool usage.
• Build retrieval-augmented agents over clinical documents and knowledge bases.
• Evaluate, monitor, and govern agent behaviour using guardrails and human-in-the-loop controls.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises focusing on safety, evaluation, and governance.

Course Customisation Options

• To request a customised training for this course, please contact us to arrange.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level to advanced-level healthcare professionals, medical researchers, and AI developers who wish to apply multimodal AI in medical diagnostics and healthcare applications.

By the end of this training, participants will be able to:

• Understand the role of multimodal AI in modern healthcare.
• Integrate structured and unstructured medical data for AI-driven diagnostics.
• Apply AI techniques to analyse medical images and electronic health records.
• Develop predictive models for disease diagnosis and treatment recommendations.
• Implement speech and natural language processing (NLP) for medical transcription and patient interaction.

This instructor-led, live training in Tashkent (available online or on-site) is designed for intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI to create innovative healthcare solutions.

By the end of this training, participants will be able to:

• Understand the role and benefits of Edge AI in healthcare.
• Develop and deploy AI models on edge devices for healthcare applications.
• Implement Edge AI solutions in wearable devices and diagnostic tools.
• Design and deploy patient monitoring systems using Edge AI.
• Address ethical and regulatory considerations in healthcare AI applications.

LangGraph enables stateful, multi-actor workflows driven by large language models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, interoperability, and the development of decision-support systems that align with established medical workflows.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

Upon completion of this training, participants will be able to:

• Design healthcare-specific LangGraph workflows with compliance and auditability as core priorities.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Apply best practices to ensure reliability, traceability, and explainability within sensitive healthcare environments.
• Deploy, monitor, and validate LangGraph applications in production healthcare settings.

Course Format

• Interactive lectures and discussions.
• Practical exercises featuring real-world case studies.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized version of this training, please contact us to arrange.

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate-level healthcare professionals and AI developers who wish to apply prompt engineering techniques to improve medical workflows, research efficiency, and patient outcomes.

By the end of this training, participants will be able to:

• Understand the fundamentals of prompt engineering in healthcare.
• Use AI prompts for clinical documentation and patient interactions.
• Leverage AI for medical research and literature reviews.
• Enhance drug discovery and clinical decision-making with AI-driven prompts.
• Ensure compliance with regulatory and ethical standards in healthcare AI.

TinyML refers to the integration of machine learning into low-power, resource-constrained wearable and medical devices.

This instructor-led, live training (available online or on-site) is designed for intermediate-level practitioners who aim to implement TinyML solutions for healthcare monitoring and diagnostic applications.

Upon completing this training, participants will be able to:

• Design and deploy TinyML models for real-time health data processing.
• Collect, preprocess, and interpret biosensor data to derive AI-driven insights.
• Optimise models for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of outputs generated by TinyML systems.

Course Format

• Lectures supported by live demonstrations and interactive discussions.
• Hands-on practice using wearable device data and TinyML frameworks.
• Implementation exercises conducted in a guided lab environment.

Course Customisation Options

• For tailored training aligned with specific healthcare devices or regulatory workflows, please contact us to customise the programme.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level healthcare professionals who wish to apply AI and AR/VR solutions for medical training, surgery simulations, and rehabilitation.

By the end of this training, participants will be able to:

• Understand the role of AI in enhancing AR/VR experiences in healthcare.
• Use AR/VR for surgery simulations and medical training.
• Apply AR/VR tools in patient rehabilitation and therapy.
• Explore the ethical and privacy concerns in AI-enhanced medical tools.