Artificial Intelligence in the MedTech Industry: Opportunities and Responsibilities for Manufacturers

In the first blog article of our campaign “Your Challenges, Our Expertise – For More Security, Transparency, and Innovation in Medical Technology”, we highlighted the key market trends for MedTech manufacturers. In this post, we take an in-depth look at another trend: Artificial Intelligence (AI) and show you how you, as a MedTech manufacturer, can successfully leverage AI. 

AI is far more than a buzzword. It is becoming a strategic success factor for efficiency, quality, and competitiveness. Studies show that one-third of MedTech companies already use AI in various areas of their supply chain or as a feature of their products. 

Broad Application Across the Supply Chain 
AI can be applied in nearly all areas of your supply chain – from research and development to sales, procurement, production, quality management, logistics, and customer service. 

Practical Examples of AI Use: 

• Product Development: Simulations of active substances or analysis of large datasets.
• Quality Assurance: Assistance systems support employees in complex decisions, such as risk assessments or interpreting test reports.
• Production: Predictive Quality detects deviations early; Smart Maintenance reduces unplanned downtime and extends machine lifespan. 

Validation as a Cornerstone for AI Deployment 
When implementing AI – such as Predictive Quality or Smart Maintenance – careful validation of your systems is essential. Both computer systems (hardware, software) and other deployed systems (e.g., equipment) along with their interfaces and processes must be tested, documented, and regularly audited. Audit trails and traceable decision paths are critical to ensure compliance. 

Benefits for Your Company 
Integrating AI into your supply chain increases efficiency, transparency, and process security. It reduces costs, optimizes resources, and lays the foundation for data-driven business models. In short: AI makes your entire value chain more agile, resilient, and future-ready. 

Beyond Internal Efficiency – AI as a Product Feature 
AI is not just a lever for internal value creation – it can also be part of your products, as these examples show: 

Examples of AI in MedTech Products: 

• AI-Powered Image Diagnostics: Medical devices such as ultrasound or MRI systems use AI to automatically analyze image data and enable more precise diagnoses.
• AI in Laboratory Analysis Devices: AI-equipped analyzers interpret complex samples faster and more accurately, reducing misinterpretations.
• AI in Therapy Devices: Rehabilitation or physiotherapy devices adjust therapy parameters in real time using AI to match patient conditions. 

Integrating AI makes your products innovative, enhances therapy safety, and creates unique selling points. 

Your Responsibility When Using AI in Products 
By integrating AI into your products, you assume a special responsibility: customers must always critically review and contextualize AI results – AI supports but does not replace human decision-making. As a manufacturer, you should actively inform users about proper handling of AI outputs and provide clear guidelines to prevent misinterpretation. 

Static Models Are Mandatory 
In regulated environments, strict rules apply to AI use by manufacturers. Within GxP processes, only static, validated AI models are permitted. Static AI models are algorithms whose functionality does not change after development and validation. They remain unchanged after approval and are regularly reviewed to ensure compliance with quality requirements. 

Self-Learning Models and LLMs Are Not Allowed 
In contrast, self-learning models adapt autonomously to new data after deployment. This dynamic adjustment is currently prohibited in regulated GxP processes because it complicates traceability and validation. Likewise, Large Language Models (LLMs) cannot currently be used in critical processes without human oversight. 

Keep an Eye on Regulatory Developments 
Annex 22 of the EU-GMP guideline is still under development. Final requirements may change. It is therefore advisable to actively monitor regulatory progress and prepare your systems and processes early for upcoming regulations. 

Summary of Key Points: 

• Your training data must be validated.
• Your AI models must not change autonomously.
• Compliance is mandatory – especially for AI. 

Many mid-sized companies ask: How do I start with AI? The answer: step by step and practically. With our AI consulting approach, Predict / Assist / Connect, we guide you from vision to implementation. Smaller pilot projects deliver quick wins and reduce hesitation – even in fragmented IT landscapes. 

Examples of Quick Wins: 

• AI-supported risk assessment in production
• Automated document comparison for ISO/SOP documents (Standard Operating Procedures)
• Real-time SOP viewer on the shop floor