How to use AI in medical devices, dive-in

Posted: Mar 7, 2025

AI AI development AI in healthcare Artificial intelligence Device integration Healthcare Healthcare development company Healthcare regulations Medical device integration Software development

The FDA has cleared 730 AI-enabled medical devices – more than ten times the number being available in 2020. This surge is reshaping healthcare delivery – initial assessment, diagnostics, treatment, daily monitoring.

But with great innovation comes complexity – implementing AI-empowered medical devices means balancing computational power, cloud accessibility, security, compliance, and other setup factors.

Another factor – in the United States, due to the complexity of federalism, there’s no single policy regarding AI. The closest is the National Artificial Intelligence Initiative Act (NAII) of 2020, inducted during the first term of the previous administration.

This article is our step-by-step breakdown on implementing artificial intelligence to re-think medical devices from concept to deployment.

The importance of AI in medical devices

Why would you implement artificial intelligence to support medical devices?

• Improve diagnostics and treatment through accuracy, predicted outcomes, and personalization
• Enhance efficiency through minimized manual labor & errors
• Reshape decision-making by harnessing real-time insights

Recent studies clearly confirm the feasibility of using artificial intelligence in integrated medical devices:

• AI algorithms from researchers in Iraq and Australia can detect different diseases with a 98% accuracy by analyzing color changes in tongues and identifying COVID-19 cases, diabetes, cancer, and more
• MRI and CT scanners extended through AI algorithms can enhance diagnostic speed and accuracy, thereby reducing manual workload and improving patient care
• Modern networked medical devices can support early detection and better post-surgical monitoring, with integrated AI-driven solutions, in particular lymphoedema detectors and smart knee implants

The implementation of AI in medical devices

To start, let’s discuss common categories of integrated medical devices that benefit from AI.

1. Diagnostic devices

• Imaging systems: AI can analyze medical images (X-rays, CT scans, MRIs, ultrasounds) to detect abnormalities like tumors, fractures, or organ anomalies with high precision.
• Point-of-care testing devices: AI-powered analysis of blood tests or biomarkers can provide rapid and accurate diagnostics in decentralized healthcare settings.
• Dermatology devices: AI can identify skin conditions, such as melanoma or psoriasis, by analyzing high-resolution images.

2. Monitoring devices

• Wearable health monitors: devices like smartwatches and fitness trackers can use AI to detect arrhythmias, track sleep patterns, and monitor vital signs, providing early warnings for potential health issues.
• Continuous glucose monitors (CGMs): AI can predict blood sugar trends and suggest insulin doses for diabetes management.
• Cardiac monitoring devices (CMDs): AI can detect irregular heart rhythms or early signs of heart failure from ECG data.

3. Therapeutic devices

• Implanted devices: AI can optimize the performance of pacemakers, insulin pumps, or neurostimulators based on real-time data.
• Rehabilitation devices: AI-powered prosthetics and exoskeletons can adapt to patients’ movements, improving functionality and comfort.
• Rehabilitation assistants: AI serves as a virtual therapist, tracking performance metrics (e.g., range of motion, repetitions) and providing real-time feedback to ensure proper execution of rehabilitation exercises.

4. Home healthcare devices

• Smart pill dispensers: AI can remind patients to take medications, adjust doses, or alert caregivers about missed doses.
• Connected medical devices: devices like blood pressure monitors or pulse oximeters can transmit data to cloud-based platforms, where AI can analyze trends and flag anomalies.
• Sleep apnea devices: AI can adjust airflow dynamically during sleep and provide insights into treatment effectiveness.

5. AI for early detection and prevention

• Cancer screening devices: AI-enabled systems can identify early-stage cancers by analyzing biopsy results or non-invasive tests.
• Predictive health devices: AI algorithms can assess genetic or lifestyle factors to predict disease risks and guide preventive measures.

6. Assistive and accessibility devices

• Hearing aids: AI can filter out background noise and enhance speech recognition for better auditory experiences.
• Vision aids: AI-powered devices can assist the visually impaired by identifying objects or reading text aloud.

7. Emergency response devices

• Automated external defibrillators (AEDs): AI can analyze heart rhythms more accurately and provide step-by-step instructions for untrained users.
• Portable ultrasound devices: AI can guide users in capturing high-quality images and suggest preliminary interpretations in remote areas or emergencies.

8. Data-driven devices

• Telemedicine devices: AI can support virtual consultations by analyzing symptoms and assisting with preliminary diagnoses.
• Clinical decision support systems: Devices integrated with AI can provide healthcare professionals with actionable insights, improving decision-making.

9. Laboratory and pathology devices

• Automated lab equipment: AI can streamline sample analysis, reducing errors and increasing throughput.
• Digital pathology scanners: AI can identify patterns in tissue samples to detect diseases like cancer more accurately.

As we can see a variety of devices and cases is wide so let’s try change the point of view and see what hardware is necessary to run AI.

The categorization by AI for medical devices: setup requirements

Categorizing AI applications in medical devices based on computation power requirements, cloud accessibility, and other AI setup factors provides a structured view for implementation. Here’s how we can organize them:

1. Low computation power devices (Edge AI)

Characteristics:

• AI processing happens on-device with minimal computation resources.
• Often uses microcontrollers or compact processors.
• Designed for portability, low power consumption, and real-time feedback.
• Limited or no dependence on cloud connectivity.

Examples:

• Wearable health monitors (e.g., smartwatches, fitness trackers): basic analytics for heart rate or activity recognition.
• Rehabilitation devices: AI tracks motion and provides feedback locally.
• Hearing aids: on-device AI filters noise and enhances speech clarity.

Use cases:

• Continuous monitoring in low-bandwidth environments.
• Real-time data processing without privacy concerns (e.g., no cloud data transfer).

2. Moderate computation power devices (Hybrid AI)

Characteristics:

• Combines edge processing with occasional cloud integration.
• AI tasks requiring higher computational resources are offloaded to the cloud.
• Balances real-time performance and advanced analytics.

Examples:

• Point-of-care testing devices: local analysis for rapid diagnostics, with cloud support for complex pattern recognition.
• Connected medical devices (e.g., blood pressure monitors, glucose meters): local data aggregation with cloud-based trend analysis.
• Emergency response devices (e.g., portable ultrasounds): initial guidance processed locally, detailed analysis offloaded to the cloud.

Use cases:

• Situations requiring both real-time decisions and advanced insights.
• Areas with intermittent internet access but a need for robust offline functionality.

3. High computation power devices (Cloud-dependent AI)

Characteristics:

• Relies heavily on cloud infrastructure for AI processing.
• Ideal for computationally intensive tasks like deep learning model inference.
• Requires constant internet connectivity for operation.

Examples:

• Medical imaging systems: cloud AI analyzes X-rays, CT scans, or MRIs to detect abnormalities.
• Telemedicine platforms: cloud AI interprets patient data and provides remote consultation insights.
• Genomic analysis devices: AI processes massive datasets for genetic testing in the cloud.

Use cases:

• Advanced diagnostics need significant computational power.
• Applications where large-scale data storage and sharing are crucial.

4. Custom, specialized AI configurations 

Characteristics:

• AI setups tailored to unique device or application requirements.
• Includes federated learning, privacy-preserving AI, or AI with specific hardware accelerators (e.g., GPUs, TPUs).

Examples:

• Robotic surgery systems: real-time precision guidance using dedicated AI hardware.
• Continuous glucose monitors: privacy-focused AI models trained locally or federated across devices.
• Lab diagnostics platforms: AI models deployed on-premises in high-performance data centers.

Use cases:

• Applications with strict data privacy, high accuracy, or latency constraints.
• Devices requiring high customization or compliance with local regulations.

Key factors for AI based medical devices: setup categorization

• Computation power:
  • Low (edge AI) vs. Moderate (hybrid AI) vs. High (cloud-dependent AI).
• Connectivity:
  • Fully offline vs. intermittent connectivity vs. always-connected cloud devices.
• Latency sensitivity:
  • Real-time response needed (rehabilitation devices, wearables) vs. delayed processing acceptable (genomics, imaging).
• Data privacy and security:
  • Local data processing for privacy concerns (e.g., GDPR, HIPAA) vs. centralized cloud solutions for scalability.
• Cost and scalability:
  • Low-cost edge AI for mass deployment vs. high-cost cloud setups for advanced analytics.
• Use environment:
  • Portable or home-use devices vs. hospital-based, high-performance systems.

The next dimension we will try to highlight here before we go to some step-by-step recommendations will be AI techniques and their specifics:

If you’re interested in using AI to reshape healthcare delivery, check out our blog:

• Using AI to expand telemedicine capabilities
• Accelerating MSK injury recovery with AI: physical therapy techniques
• Motion recognition to improve public health
• Pose detection for online Achilles tendon rupture rehabilitation
• Virtual care – benefits, challenges, and prospects
• Digital therapeutics and remote therapeutics monitoring

The implementation of AI: essential prerequisites 

Finally, let’s explore some key business considerations to address before diving into the implementation of an AI-powered healthcare device:

• Market vs technology approach
  • Market pull to solve existing problems
  • Technology push to leverage new capabilities
• Product strategy
  • Build AI-first medical devices to meet current needs
  • Add AI to existing medical devices
• Cost-benefit balance
  • Evaluate AI integration investment against potential
  • Prioritize simplicity for more cost-sensitive markets
• Regulatory compliance & ethics
  • Ensure privacy and accountability
  • Plan for pre-market approvals and subsequent, post-market monitoring
• Business model
  • Explore recurring revenue models vs one-time sales strategies
  • Match with target segments
• Competitive edge
  • Prioritize differentiation
  • Go for unique scenarios 

The implementation of AI: business guide

Now, after exploring key dimensions, use cases of AI-driven devices, and essential business prerequisites, we are ready to present a step-by-step algorithm tailored for healthcare CTOs and entrepreneurs:

Step 1: define goals and prerequisites

Identify whether the goal is market-driven or technology-driven (which means to leverage AI advancements) and don’t forget about HIPAA, GDPR, FDA, CE, and other legal standards.

Step 2: identify category

• Classify the medical device considering functionality
• Determine the preferred platform (on-premises, cloud)

Step 3: determine needs

Evaluate whether you need a solution for low-power, privacy-sensitive applications, hybrid or cloud models.

Step 4: choose techniques

Select methods based on main purpose – ML, DL, NLP, RL?

Step 5: evaluate the existing vs. new product

Decide whether to extend existing products or develop new ones for emerging healthcare opportunities.

Step 6: plan development and deployment

Develop a data strategy by collecting training datasets, selecting a best-suitable framework, training models, and testing the prototypes.

Step 7: address security, regulatory compliance, and ethics

Implement encryption, enforce privacy, ensure meeting legal standards, and establish AI accountability.

Step 8: market testing and iteration

Conduct trials with both healthcare professionals and patients, gather feedback, and refine AI models.

Step 9: grow further

Optimize for large datasets, integrate modern cloud capabilities, and implement a lucrative revenue strategy through subscriptions or partnerships.

Key considerations 

• Regulatory compliance: overview of FDA guidance on AI in medical devices market and other regulatory standards.
• Data security and privacy: emphasize the importance of patient data protection under HIPAA and other privacy regulations when using generative AI in medical devices.
• Data quality and accessibility: importance of high-quality, annotated data for model training and access to large datasets.
• Integration with existing systems: consideration of interoperability with current healthcare infrastructure.

Key challenges and limitations

• Bias and fairness: addressing potential biases in AI models and their impact on patient care.
• Interpretability of AI models: discussing the need for explainable AI in healthcare applications.
• Technical limitations: hardware limitations, latency issues, and computational demands of AI on devices.

How we can help

AI implementation fitted perfectly

Is it edge AI for real-time data processing, hybrid AI for balanced operational performance, cloud-supported AI? We help you choose the optimal business strategy.

Custom solutions

No two medical devices are ever the same, that’s why we design AI architecture that meets your requirements. We deliver robust solutions seamlessly integrated into unique business ecosystems.

AI integration handled professionally

Whether integrating AI into medical devices or optimizing data pipelines, we guarantee smooth transitioning. We don’t just build AI models – we ensure they work within your specific infrastructure.

From concept to deployment

AI implementation goes beyond building algorithms:

• We assist in defining accurate requirements
• Selecting the AI technique (ML, DL, NLP, CV)
• Optimizing strategies
• And ensuring future readiness through scalability, and more

Ready to take the next step and go for AI in your healthcare journey?

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• Artificial intelligence, in particular advanced analytics
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