医療におけるAIとAR/VRのトレーニングコース

エージェント型AIは、定義された制約内であらゆる目標を達成するために計画し、推論を行い、ツールを使用して行動するAIシステムのアプローチです。

このインストラクター主導のライブトレーニング（オンラインまたは対面）は、中級レベルの医療およびデータチームを対象としており、臨床および運用ユースケース向けにエージェント型AIソリューションを設計、評価、管理することを目指しています。

このトレーニング終了時には、参加者は以下のことができるようになります：

• 医療の文脈でのエージェント型AIの概念と制約を説明する。
• 計画、記憶、ツール使用を備えた安全なエージェントワークフローを設計する。
• 臨床文書や知識ベースを使用した検索強化型エージェントを構築する。
• ガードレールとヒューマンインザループ制御を使用して、エージェントの動作を評価、監視、管理する。

コース形式

• 双方向講義とファシリテーションされたディスカッション。
• サンドボックス環境でのガイドルアブとコードウォークスルー。
• 安全性、評価、管理に関するシナリオベースの演習。

コースカスタマイゼーションオプション

• このコースのカスタムトレーニングを依頼する場合は、お問い合わせください。

This instructor-led, live training in 日本 (online or onsite) is aimed at intermediate-level to advanced-level healthcare professionals and AI developers who wish to implement AI-driven healthcare solutions.

By the end 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 日本 (online or onsite) is aimed at beginner-level professionals who wish to learn how to apply AI techniques to AR and VR systems.

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

• Understand the basics of AI and its role in AR/VR technologies.
• Explore key AI-driven techniques for enhancing AR/VR experiences.
• Implement simple AI models in AR/VR applications.
• Use AI to improve interactivity and user experiences in AR/VR.

この講師主導のライブトレーニングは、日本（オンラインまたはオンサイト）で開催され、中級レベルのデータサイエンティストや医療専門家向けで、Google Colab を使用して高度な医療アプリケーションに AI を活用することを目指しています。

このトレーニングの終了時には、参加者は以下のことが Able to になります：

• Google Colab を使用して医療向けの AI モデルを実装します。
• 医療データでの予測モデリングに AI を活用します。
• AI 駆動型の技術で医学画像を分析します。
• AI ベースの医療ソリューションにおける倫理的配慮を探ります。

本講座はオンラインまたは対面で行われる講師主導のライブトレーニングであり、中級レベルの医療専門家やデータサイエンティストを対象としています。AI技術を医療環境に応用したい方におすすめです。

本講座終了時には、参加者は以下のことが Able to:

• AIが解決できる主要な医療課題を特定します。
• AIの患者ケア、安全性、および医学研究への影響を分析します。
• AIと医療ビジネスモデルの関係を理解します。
• 医療シナリオに基本的なAI概念を適用します。
• 医療データ分析用の機械学習モデルを開発します。

This instructor-led, live training in 日本 (online or onsite) is aimed at 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 日本 (online or onsite) is aimed at intermediate-level healthcare professionals, biomedical engineers, and AI developers who wish to leverage Edge AI for 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.

This instructor-led, live training in 日本 (online or onsite) is aimed at intermediate-level to advanced-level medical AI developers and data scientists who wish to fine-tune models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

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

• Fine-tune AI models on healthcare datasets including EMRs, imaging, and time-series data.
• Apply transfer learning, domain adaptation, and model compression in medical contexts.
• Address privacy, bias, and regulatory compliance in model development.
• Deploy and monitor fine-tuned models in real-world healthcare environments.

生成AIは、プロンプトやデータに基づいて新しいコンテンツ（テキスト、画像、推奨など）を作成する技術です。

このインストラクター主導のライブトレーニング（オンラインまたはオンサイト）は、初級から中級レベルの医療専門家を対象としており、生成AIとプロンプトエンジニアリングを使用して医療現場での効率性、正確さ、コミュニケーションを向上させることを目指しています。

このトレーニング終了時には、受講者は以下のことができるようになります：

• 生成AIとプロンプトエンジニアリングの基本を理解する。
• AIツールを使用して臨床、管理、研究業務を効率化する。
• 医療分野でのAIの倫理的、安全かつ適切な使用を確保する。
• 一貫性と正確性のある結果を得るためにプロンプトを最適化する。

コースの形式

• 対話型講義とディスカッション。
• 実践的な演習と事例研究。
• AIツールの実際の操作。

コースのカスタマイズオプション

• このコースのカスタマイズされたトレーニングを希望される場合は、お問い合わせください。

This instructor-led, live training in 日本 (online or onsite) 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 personalized medicine.
• Utilize 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.

LangGraph enables stateful, multi-actor workflows powered by LLMs with precise control over execution paths and state persistence. In healthcare, these capabilities are crucial for compliance, interoperability, and building decision-support systems that align with medical workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

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

• Design healthcare-specific LangGraph workflows with compliance and auditability in mind.
• Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
• Apply best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications in healthcare production settings.

Format of the Course

• Interactive lecture and discussion.
• Hands-on exercises with real-world case studies.
• Implementation practice in a live-lab environment.

Course Customization Options

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

This instructor-led, live training in 日本 (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 analyze 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.

Ollama is a lightweight platform for running large language models locally.

This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare practitioners and IT teams who wish to deploy, customize, and operationalize Ollama-based AI solutions within clinical and administrative environments.

Upon completing this training, participants will be able to:

• Install and configure Ollama for secure use in healthcare settings.
• Integrate local LLMs into clinical workflows and administrative processes.
• Customize models for 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 日本 (online or onsite) is aimed at intermediate-level healthcare professionals and AI developers who wish to leverage prompt engineering techniques for improving 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 review.
• Enhance drug discovery and clinical decision-making with AI-driven prompts.
• Ensure compliance with regulatory and ethical standards in healthcare AI.

TinyML is the integration of machine learning into low-power, resource-limited wearable and medical devices.

This instructor-led, live training (online or onsite) is aimed at intermediate-level practitioners who wish to implement TinyML solutions for healthcare monitoring and diagnostic applications.

After 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 for AI-driven insights.
• Optimize models for low-power and memory-constrained wearable devices.
• Evaluate the clinical relevance, reliability, and safety of TinyML-driven outputs.

Format of the Course

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

Course Customization Options

• For tailored training that aligns with specific healthcare devices or regulatory workflows, please contact us to customize the program.