Ethical AI in Biomedical Devices: A Growing Frontier

Artificial intelligence is transforming medical care in ways that seemed impossible just a decade ago. AI-powered devices can now detect diseases earlier, predict patient outcomes with remarkable accuracy and guide treatment decisions that save lives. These systems streamline healthcare tasks through automatic diagnosis, treatment plan creation and data analysis,¹ giving professionals more time to focus on the deeply human aspects of care—offering a reassuring presence during a difficult diagnosis, exercising nuanced clinical judgment in complex cases or simply listening to a patient's concerns.

With this transformative power, however, comes profound responsibility. As AI systems increasingly influence medical decisions, engineers and healthcare professionals face critical questions:² How do we ensure that these technologies protect patient privacy? How do we prevent algorithmic bias from amplifying existing healthcare disparities? Who is accountable when an AI system makes a mistake? The answers to these questions will shape not only the future of healthcare technology but also the trust patients place in the medical system itself.

This post will explore ethical challenges in biomedical engineering with AI, from data privacy and algorithmic bias to transparency and informed consent. You'll consider how thoughtful engineers are addressing these challenges and learn what practices can help ensure that AI medical devices deliver better, more inclusive care while earning the trust of both patients and healthcare professionals.

The Rise of AI Medical Devices

Devices powered by artificial intelligence can aid in the monitoring, diagnosis, assessment, analysis and treatment of a range of health conditions. Examples of AI medical devices include:

• AI-powered diagnostic imaging
• Robotic surgery systems
• Wearable health monitors
• Biosensors
• Smart stethoscopes
• AI-assisted spirometers
• AI-powered insulin pens

These devices handle much of the legwork that healthcare experts have often managed on their own, making diagnosis and treatment more efficient and enabling medical professionals to help as many patients as possible.³

Furthermore, while human expertise and judgment remain essential in healthcare, AI can help reduce incidents of human error in straightforward tasks. Healthcare professionals often work long, grueling shifts, which can result in profound fatigue. After working 10 hours straight, anyone could overlook a small detail that’s crucial during a diagnosis. AI and robots don’t get tired, so they can ensure that care professionals don’t miss important information.⁴

Ethical Considerations in AI-Powered Healthcare

When developing or using biomedical AI, it is essential to consider the evolving ethical considerations it presents. The primary concerns center on data privacy and security, patient safety, autonomy, informed consent, bias and transparency. However, as AI in biomedical engineering advances, it’s vital to consider new ethical concerns as they arise.

While the AI-powered advancements in the medical field are thrilling, professionals leveraging them must maintain a balance between biomedical innovation and biomedical AI ethics. This isn’t always easy: The pressure to bring AI-powered devices to market quickly can conflict with the need for thorough ethical vetting and comprehensive testing. Healthcare organizations face competitive pressures to adopt AI technologies early, yet premature deployment of inadequately tested systems can put patients at risk. Similarly, the drive to innovate can sometimes overshadow questions not just about whether AI can be used in certain contexts, but whether it should be.

Engineers and healthcare leaders must ask difficult questions throughout the development process: Does this AI system truly improve patient outcomes, or does it simply reduce costs? Are we implementing AI because it serves patients better, or because competitors are doing so? Who bears responsibility when an AI system makes an error—the developer, the healthcare provider or the AI itself? Navigating these tensions requires not only technical expertise but also a strong ethical framework and the courage to prioritize patient welfare over expedience.

Patient Privacy and Data Protection

Many AI devices track and collect data from patients—from X-ray assessments to blood pressure trends and changes in heart rate. A large data set helps AI deepen its understanding of people’s health as a whole, so comprehensive patient data is frequently used to train algorithms.⁵ It’s crucial, however, that patients give consent for their data to be used in any way and that their information is securely stored.

Health records are considered confidential information and any data collected by AI should be treated as such. Physician-patient privilege disclosures do not extend to artificial intelligence yet. Nevertheless, the information should still be kept private unless the patient gives consent otherwise.

Data leaks and cyberattacks are significant risks whenever you have large, valuable data sets, even when you’re managing data with AI.⁶ There are several steps to take to safeguard patient data privacy and security:

• Routinely assess AI models for security holes
• Encrypt confidential data
• Build firewalls and secure all entries and endpoints in the system
• Regularly update all software systems to prevent vulnerabilities in security
• Feed AI modern information on cyberattack strategies

If you focus on creating human-centered designs with privacy at the forefront, you can reap the benefits of AI’s power while keeping patients and their information safe.

Transparency and Trust

AI can play a significant role in medical decision-making, providing healthcare professionals with comprehensive insights, that go beyond simple diagnosis, into patients' conditions. It can spot trends and make predictions that would otherwise go unnoticed. This allows medical experts to personalize care and make it more effective.

However, AI’s involvement in recommending a treatment plan must be disclosed to patients. Healthcare professionals must be transparent about AI’s role in a patient’s experience, so they can make autonomous decisions about their care.

This is where explainable AI (XAI) comes into play. XAI is a branch of AI that focuses on understanding how AI models make their decisions. Deep AI models can be so complex that humans can’t understand how they arrive at their conclusions. Understanding the decision-making processes helps hone AI and offers transparency for patients and medical professionals alike.⁵ This transparency builds trust between AI and the medical industry and between patients and health professionals. Furthermore, transparent AI processes make it easier to identify and remedy biases within algorithms, which helps ensure fairness for everyone.⁴

Ensuring Diverse and Representative Data Sets

Artificial intelligence and machine learning models can only operate with the information they’re given. If they’re trained on incomplete, inaccurate or unrepresentative data sets, their algorithms and decision-making will have biases. You must give these models diverse, representative data sets to generate the most accurate and helpful outputs.⁷

Even the smallest bias in a data set can be magnified in AI outputs, so it’s important to prioritize using inclusive data sets. To collect diverse data, engage with diverse communities, utilize outliers and perform data augmentation. The key is to cast a wide net rather than limiting your data set.⁴

Biomedical Quality Control and Device Testing

The future of bioinstrumentation will likely be heavily focused on AI. Many of the most exciting innovations in biomedical engineering are possible because of AI’s ability to process large amounts of data quickly.

AI models can rapidly assess a device’s potential and effectiveness, speeding up medical device development so biomedical engineers can deliver solutions to the healthcare industry sooner. AI can run scenarios on a device to see how it performs, before it’s ever used on a patient.

We cannot, however, rely solely on AI for quality assurance and safety checks. AI isn’t perfect and can still make mistakes, especially when biases are present. You should always double-check AI’s outputs, especially if a patient’s safety is at risk.⁵

Lead the Future of Ethical AI in Biomedical Engineering

The intersection of AI and biomedical engineering requires professionals who can navigate complex ethical challenges while driving innovation forward. In the online Master of Science in Biomedical Engineering program from Case Western Reserve University, you'll gain the expertise to become one of those leaders.

For more than 50 years, CWRU's biomedical engineering program has been at the forefront of cutting-edge research, with real-world impact. You'll learn directly from world-class faculty whose innovations are shaping the technologies healthcare professionals use every day— faculty like Dr. Umut Gurkan, whose point-of-care diagnostic technology is now available in 30 countries and has enabled more than 500,000 tests, saving thousands of lives in the process.

This 100% online program can be completed in as few as 18 months and consists of 10 comprehensive courses covering ethical challenges in biomedical engineering, device development, the intersection of medicine and technology and more. The curriculum is built to prepare you for leadership roles in which you'll balance innovation with ethics, ensuring that AI-powered medical devices serve all patients equitably and safely.

As a student, you'll join a powerful network of alumni and benefit from the resources of an R1 research institution that ranks among the nation's leading private universities. The program’s flexible online format allows you to advance your expertise while maintaining your current professional commitments.

Review our tuition and financial aid options and schedule a call with an admissions outreach advisor to explore how this program can position you as a leader in the evolving field of biomedical AI ethics.