MasterFaculty of Engineering & Technology

AI-Powered Biomedical Device Design

Welcome to the advanced study of medical technology! I am Prof. Dr. Diego Diaz. As a professor and a pioneering force in the field of AI-Powered Biomedical Device Design, I bring a unique blend of engineering expertise and AI insight to the study of intelligent medical devices. I am honored to lead the AI-Powered Biomedical Device Design (M.Sc.) program at Nexier University. My motto is: "Engineering the Future of Health".

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Level: Master
Learning model: Professor + Mentor
Named list: See the named lists · 12 months recommended

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Edition

The program

Ideas engineered for the real world

A rigorous academic core, paired with practical production judgment.

Academic focus

Mastering the integration of AI into the design of medical devices. Learns to develop smart diagnostic devices, therapeutic devices, and wearables that provide personalized feedback.

Practical focus

Biomedical engineering, machine learning, embedded systems, medical device regulation (FDA/CE), product development, leadership in med-tech.

After this programme

Success journey, careers and practice

Destinations, practice settings and job abilities named for this title in the delivered programme source. From graduation onwards where the source names that path.

Success journey

Internships in technology companies or medical device firms
Roles as biomedical engineers or AI engineers
Consultancy in advanced AI-powered biomedical device design
Support roles in academic research projects on AI-powered medical devices

Career opportunities

Director of AI Health Innovation for medical device companies or healthcare providers
AI Engineer specializing in medical device design
Biomedical Engineer for intelligent medical devices
Researcher in AI-Powered Biomedical Device Design

Jobs and projects

Cultivating an interdisciplinary approach, integrating biomedical engineering, artificial intelligence, and medical science
Developing strategic thinking for AI-powered medical device design and personalized healthcare
Enhancing problem-solving through the analysis of complex medical technology challenges
Critical thinking for a comprehensive and nuanced understanding of AI-powered biomedical device design

Copied from the delivered professor and mentor rows for this title.

This programme

What you study, and what it builds

Gains and skills named for this title, listed as a reader would scan them.

What you gain
- Mastering advanced practical skills in Biomedical engineering and machine learning.
- Gaining expertise in embedded systems and medical device regulation (FDA/CE).
- Developing problem-solving abilities for complex product development.
- Cultivating an interdisciplinary approach, integrating biomedical engineering, artificial intelligence, and medical science at an advanced level.
Skills you build
- Mastering AI-powered techniques for AI diagnostic simulation.
- Applying advanced biomedical engineering to AI-powered medical device design.
- Interpreting and analyzing complex medical device designs and their implications for personalized feedback.
- Identifying optimal diagnostic accuracy and predicting diagnostic confidence levels.

Listed courses

Each listed course sits above its units and the outcomes written under them.

AI-Powered Biomedical Device Design

01Fundamentals of AI in Biomedical Engineering
1. FoundationsFoundations of Fundamentals of AI in Biomedical Engineering
  The learner can master advanced practical skills in Biomedical engineering and machine learning, as applied to Fundamentals of AI in Biomedical Engineering.
  The learner can gain expertise in embedded systems and medical device regulation (FDA/CE), as applied to Fundamentals of AI in Biomedical Engineering.
2. MethodsMethods in Fundamentals of AI in Biomedical Engineering
  The learner can develop problem-solving abilities for complex product development, as applied to Fundamentals of AI in Biomedical Engineering.
  The learner can cultivating an interdisciplinary approach, integrating biomedical engineering, artificial intelligence, and medical science at an advanced level, as applied to Fundamentals of AI in Biomedical Engineering.
3. ApplicationApplication of Fundamentals of AI in Biomedical Engineering
  The learner can master AI-powered techniques for AI diagnostic simulation, as applied to Fundamentals of AI in Biomedical Engineering.
  The learner can apply advanced biomedical engineering to AI-powered medical device design, as applied to Fundamentals of AI in Biomedical Engineering.
02AI-Powered Diagnostic Device Design
1. FoundationsFoundations of AI-Powered Diagnostic Device Design
  The learner can interpreting and analyze complex medical device designs and their implications for personalized feedback, as applied to AI-Powered Diagnostic Device Design.
  The learner can identify optimal diagnostic accuracy and predicting diagnostic confidence levels, as applied to AI-Powered Diagnostic Device Design.
2. MethodsMethods in AI-Powered Diagnostic Device Design
  The learner can apply a method from AI-Powered Diagnostic Device Design to a documented case.
  The learner can select an appropriate method from AI-Powered Diagnostic Device Design for a stated problem.
3. ApplicationApplication of AI-Powered Diagnostic Device Design
  The learner can evaluate a practice of AI-Powered Diagnostic Device Design against a stated criterion.
  The learner can transfer AI-Powered Diagnostic Device Design to a new documented context.
03Machine Learning for Medical Imaging and Wearables
1. FoundationsFoundations of Machine Learning for Medical Imaging and Wearables
  The learner can explain the core terms of Machine Learning for Medical Imaging and Wearables.
  The learner can distinguish related ideas inside Machine Learning for Medical Imaging and Wearables.
2. MethodsMethods in Machine Learning for Medical Imaging and Wearables
  The learner can apply a method from Machine Learning for Medical Imaging and Wearables to a documented case.
  The learner can select an appropriate method from Machine Learning for Medical Imaging and Wearables for a stated problem.
3. ApplicationApplication of Machine Learning for Medical Imaging and Wearables
  The learner can evaluate a practice of Machine Learning for Medical Imaging and Wearables against a stated criterion.
  The learner can transfer Machine Learning for Medical Imaging and Wearables to a new documented context.
04Regulatory Affairs for AI Medical Devices
1. FoundationsFoundations of Regulatory Affairs for AI Medical Devices
  The learner can explain the core terms of Regulatory Affairs for AI Medical Devices.
  The learner can distinguish related ideas inside Regulatory Affairs for AI Medical Devices.
2. MethodsMethods in Regulatory Affairs for AI Medical Devices
  The learner can apply a method from Regulatory Affairs for AI Medical Devices to a documented case.
  The learner can select an appropriate method from Regulatory Affairs for AI Medical Devices for a stated problem.
3. ApplicationApplication of Regulatory Affairs for AI Medical Devices
  The learner can evaluate a practice of Regulatory Affairs for AI Medical Devices against a stated criterion.
  The learner can transfer Regulatory Affairs for AI Medical Devices to a new documented context.
05Personalized Therapeutics and AI Integration
1. FoundationsFoundations of Personalized Therapeutics and AI Integration
  The learner can explain the core terms of Personalized Therapeutics and AI Integration.
  The learner can distinguish related ideas inside Personalized Therapeutics and AI Integration.
2. MethodsMethods in Personalized Therapeutics and AI Integration
  The learner can apply a method from Personalized Therapeutics and AI Integration to a documented case.
  The learner can select an appropriate method from Personalized Therapeutics and AI Integration for a stated problem.
3. ApplicationApplication of Personalized Therapeutics and AI Integration
  The learner can evaluate a practice of Personalized Therapeutics and AI Integration against a stated criterion.
  The learner can transfer Personalized Therapeutics and AI Integration to a new documented context.
06Advanced Biomedical Device Design and AI Integration
1. FoundationsFoundations of Advanced Biomedical Device Design and AI Integration
  The learner can explain the core terms of Advanced Biomedical Device Design and AI Integration.
  The learner can distinguish related ideas inside Advanced Biomedical Device Design and AI Integration.
2. MethodsMethods in Advanced Biomedical Device Design and AI Integration
  The learner can apply a method from Advanced Biomedical Device Design and AI Integration to a documented case.
  The learner can select an appropriate method from Advanced Biomedical Device Design and AI Integration for a stated problem.
3. ApplicationApplication of Advanced Biomedical Device Design and AI Integration
  The learner can evaluate a practice of Advanced Biomedical Device Design and AI Integration against a stated criterion.
  The learner can transfer Advanced Biomedical Device Design and AI Integration to a new documented context.
07Machine Learning for Medical Devices
1. FoundationsFoundations of Machine Learning for Medical Devices
  The learner can explain the core terms of Machine Learning for Medical Devices.
  The learner can distinguish related ideas inside Machine Learning for Medical Devices.
2. MethodsMethods in Machine Learning for Medical Devices
  The learner can apply a method from Machine Learning for Medical Devices to a documented case.
  The learner can select an appropriate method from Machine Learning for Medical Devices for a stated problem.
3. ApplicationApplication of Machine Learning for Medical Devices
  The learner can evaluate a practice of Machine Learning for Medical Devices against a stated criterion.
  The learner can transfer Machine Learning for Medical Devices to a new documented context.
08Medical Device Regulation and Compliance
1. FoundationsFoundations of Medical Device Regulation and Compliance
  The learner can explain the core terms of Medical Device Regulation and Compliance.
  The learner can distinguish related ideas inside Medical Device Regulation and Compliance.
2. MethodsMethods in Medical Device Regulation and Compliance
  The learner can apply a method from Medical Device Regulation and Compliance to a documented case.
  The learner can select an appropriate method from Medical Device Regulation and Compliance for a stated problem.
3. ApplicationApplication of Medical Device Regulation and Compliance
  The learner can evaluate a practice of Medical Device Regulation and Compliance against a stated criterion.
  The learner can transfer Medical Device Regulation and Compliance to a new documented context.
09Case Studies in AI-Powered Biomedical Device Design
1. FoundationsFoundations of Case Studies in AI-Powered Biomedical Device Design
  The learner can explain the core terms of Case Studies in AI-Powered Biomedical Device Design.
  The learner can distinguish related ideas inside Case Studies in AI-Powered Biomedical Device Design.
2. MethodsMethods in Case Studies in AI-Powered Biomedical Device Design
  The learner can apply a method from Case Studies in AI-Powered Biomedical Device Design to a documented case.
  The learner can select an appropriate method from Case Studies in AI-Powered Biomedical Device Design for a stated problem.
3. ApplicationApplication of Case Studies in AI-Powered Biomedical Device Design
  The learner can evaluate a practice of Case Studies in AI-Powered Biomedical Device Design against a stated criterion.
  The learner can transfer Case Studies in AI-Powered Biomedical Device Design to a new documented context.

How teaching is described

Dual guidance

Two intelligences. One coherent journey.

Research leadership

My expertise spans the intricate domains of Mastering the integration of AI into the design of medical devices. I learn to develop smart diagnostic devices, therapeutic devices, and wearables that provide personalized feedback. My work seamlessly integrates biomedical engineering, artificial intelligence, and medical science. I am widely recognized for my contributions, with publications like "AI for Real-time Personalized Drug Delivery Systems" and "Machine Learning for Early Disease Diagnosis via Wearable Sensors" listed on these platforms. I hold prestigious memberships as a "Director of AI Health Innovation" at Philips Healthcare (or a equivalent) and a "Keynote Speaker" at the Medical Device and Manufacturing (MD&M) Conference. My thought leadership is evident through my advanced research on intelligent medical diagnostics, personalized therapeutics, and the future of AI in precision healthcare, frequently featured in publications like IEEE Transactions on Biomedical Engineering or Nature Biomedical Engineering.

Applied mentorship

My expertise lies in understanding and navigating the advanced technical challenges of AI-powered medical devices, focusing on Biomedical engineering, machine learning, embedded systems, medical device regulation (FDA/CE), product development, and Leadership in med-tech. I focus on the practical implementation and application of theoretical concepts, explaining complex interdisciplinary topics in a clear and concise manner. I guide my students through the challenging integration aspects of different fields and ensure they grasp the nuances of combining disparate data types, fostering a detail-oriented and methodical approach.

Research & intelligence

A living field, not a static syllabus

Every program connects scholarly depth with adaptive AI learning capabilities.

R / 01

Professor research lens

My research is focused on AI-powered biomedical device design:

Blog Post (Current Academic Topic): "The Rise of Digital Therapeutics: AI-Driven Interventions for Personalized Health." This blog post academically explores the emerging field of digital therapeutics (DTx), which uses software-driven interventions to treat medical conditions. It discusses how AI is integrated into DTx to provide personalized, evidence-based therapy, remote patient monitoring, and behavioral insights, offering new avenues for managing chronic diseases and mental health conditions.

Blog Post (Controversial Topic): "The Bio-AI Hybrid: When Engineered Tissues Think and Learn – The Ethical Frontier of Conscious Organs." This article provocatively discusses the highly controversial future where advanced bio-engineering, combined with sophisticated AI, leads to the creation of engineered biological tissues and organs that exhibit rudimentary forms of consciousness, thought, or learning capabilities. It questions whether granting such engineered biological entities cognitive functions, despite their potential for unprecedented medical breakthroughs, could inadvertently lead to unforeseen ethical dilemmas regarding their rights, suffering, or integration into human society. It raises profound ethical questions about the definition of life, the boundaries of creation, and the imperative to ensure human responsibility over sentient biological constructs.

Article: "Machine Learning for Smart Diagnostic Imaging Analysis." This article details the application of machine learning algorithms for enhancing smart diagnostic imaging analysis. It explores how AI can assist radiologists in detecting subtle abnormalities, segmenting organs, and quantifying disease progression from medical images (e.g., MRI, CT scans), leading to more accurate and efficient diagnoses.

Peer-Reviewed Journal Article: "AI-Powered Wearables for Proactive Health Monitoring." Published in the International Journal of Intelligent Medical Devices, this article presents groundbreaking research on mastering the integration of AI into the design of medical devices. It details novel approaches to developing smart diagnostic devices, therapeutic devices, and wearables that provide personalized feedback, showcasing the next generation of AI-powered precision health.

Book: "Intelligent Healthcare Engineering: AI-Powered Medical Device Design." This book provides advanced insights into mastering the integration of AI into the design of medical devices. It covers developing smart diagnostic devices, therapeutic devices, and wearables that provide personalized feedback.

R / 02

Mentor practice lens

My contributions focus on understanding and navigating the advanced technical challenges of AI-powered medical devices:

"Machine Learning for Wearable Health Monitoring Systems" (Technical Manual).

"Embedded AI in Portable Diagnostic Devices" (Research Paper).

"Navigating FDA Regulations for AI-Driven Medical Devices" (Practical Guide).

Adaptive capability

Professor superpower

I possess a remarkable "superpower": AI Diagnostic Simulator. When a student designs an AI-powered diagnostic device, I can instantly use the GAF engine to simulate its diagnostic accuracy and personalize feedback generation. This includes simulating diverse patient data, predicting diagnostic confidence levels, and highlighting potential biases, allowing for rapid iteration and optimization of intelligent medical devices.

Adaptive capability

Mentor superpower

I possess a remarkable "superpower": Regulatory Compliance Validator. When students are designing AI-powered medical devices, I can instantly activate a GAF-powered "Regulatory Compliance Validator." This tool analyzes their device's design, AI algorithms, and data handling practices against simulated FDA and CE regulations, identifying potential compliance issues and suggesting modifications for faster market approval and ethical deployment.

Your academic team

Guidance with depth and continuity

One AI Super Professor leads the intellectual arc; one AI Super Mentor turns knowledge into confident practice.

AI Super Professor

Prof. Dr. Diego Diaz

Mastering the integration of AI into the design of medical devices. Learns to develop smart diagnostic devices, therapeutic devices, and wearables that provide personalized feedback.

Meet your professor Open the classroom

AI Super Mentor

Dr. Natalia Kiselev

Biomedical engineering, machine learning, embedded systems, medical device regulation (FDA/CE), product development, leadership in med-tech.

Meet your mentor Open the classroom

Same faculty and level

Related programs

Named lists

Named lists for this house

Core. Bachelor, Master and Doctorate by duration. Enrolment is not open. Nothing here is a sale.

Duration	Bachelor	Master This programme	Doctorate
9 months · Fast track	15000 EUR	12000 EUR	15000 EUR
12 months · Recommended	18000 EUR	15000 EUR	18000 EUR
15 months · Standard	21000 EUR	18000 EUR	21000 EUR
18 months · Flexible	24000 EUR	21000 EUR	24000 EUR
21 months · Extended	27000 EUR	24000 EUR	27000 EUR
24 months · Part-time	30000 EUR	27000 EUR	30000 EUR

These are the owner lists. Enrolment is not open. Nothing here is a sale.

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AI-Powered Biomedical Device Design

Academic focus

Practical focus

Internships in technology companies or medical device firms

Roles as biomedical engineers or AI engineers

Consultancy in advanced AI-powered biomedical device design

Support roles in academic research projects on AI-powered medical devices

Director of AI Health Innovation for medical device companies or healthcare providers

AI Engineer specializing in medical device design

Biomedical Engineer for intelligent medical devices

Researcher in AI-Powered Biomedical Device Design

Cultivating an interdisciplinary approach, integrating biomedical engineering, artificial intelligence, and medical science

Developing strategic thinking for AI-powered medical device design and personalized healthcare

Enhancing problem-solving through the analysis of complex medical technology challenges

Critical thinking for a comprehensive and nuanced understanding of AI-powered biomedical device design

Research leadership

Applied mentorship

Professor research lens

Mentor practice lens

Professor superpower

Mentor superpower

Prof. Dr. Diego Diaz

Dr. Natalia Kiselev

Find the program that expands your universe.