Legal challenges and regulatory solutions for medical devices based on artificial intelligence

With the rapid growth of artificial intelligence (AI) and machine learning (ML) technologies in the field of medical devices and digital health software, regulatory bodies are trying to develop appropriate standards and regulations to ensure the safety and efficiency of these technologies. In this regard, the United States, Europe, and the United Kingdom have approved and proposed various principles and guidelines for artificial intelligence-based devices independently as well as in the form of international cooperation.

Legal challenges of artificial intelligence based devices

In recent years, the use of artificial intelligence and machine learning in medical devices and digital health software has increased dramatically. By analyzing and learning from vast data, these technologies can improve disease diagnosis and provide more accurate medical recommendations. But determining which types of these programs require licensing and which types do not fall under medical laws has been a major challenge for regulatory bodies. Among the important issues in this field are borderline programs; Applications that require monitoring due to their application in health, but are not recognized as medical devices.

Approval and licensing status of artificial intelligence based devices

Currently, the FDA has approved more than 700 artificial intelligence-based devices in the United States. This high number indicates that the legal path to approval of these devices has gradually become easier, although the field of medical imaging still accounts for the largest share of these devices.

Predefined control programs and possible changes

The FDA in America recently issued a guideline under the title Predefined change control programs (PCCP) has published These programs allow companies to implement specific changes without having to file a new application, which speeds up the process of evaluating and updating devices. Similar programs are being implemented in the United Kingdom and Canada, which shows the common approach of these countries in monitoring new technologies.

Life cycle management and continuous performance evaluation

One of the new ways to reduce the risk of artificial intelligence-based devices is to implement post-launch performance management programs. These programs require manufacturers to test product performance in real-world environments after release. Continuous performance monitoring allows manufacturers to receive environmental feedback, become aware of the potential real-world impacts and risks of their devices, and make the necessary changes.

Looking to the future: Challenges and opportunities ahead

Due to the spread of adaptive artificial intelligence and automatic learning technologies, the need for more comprehensive regulations is felt. Also, issues such as the use of high quality data and the need to respect patients’ privacy will be important challenges in this field.