BELGIUM Trends and Developments Contributed by: Benjamin Docquir and Margo Cornette, Osborne Clarke
systems may cover certain digitised clinical activities such as: (a) automated study participant triage or selection tools; and (b) remote patient monitoring algorithms that collect or analyse biometric data, such as heart rate, blood pressure or temperature. • AI systems used in remote patient monitor - ing may also qualify as high-risk AI systems under Article 6, Section 1 of the AIA if (i) they qualify as medical devices under the Medi - cal Device Regulation (MDR) (listed in Annex I as one of the EU product safety regulations) and (ii) to the extent that they are subject to a conformity assessment by the competent authority. The latter is the case for type IIa, IIb and III medical devices. It is therefore important to understand what is covered by the definition of a medical device and when a medical device falls into the type IIa, IIb and III categories. Under Article 2, Section 1 of the MDR, soft - ware can qualify as a medical device where it is intended to be used for specific purposes such as diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of disease. Further, the MDR states that medical devices requiring a conformity assessment, and thus classified as high-risk AI, include all type IIa, IIb, and III devices. In the context of remote patient monitoring, Annex VIII of the MDR describes which medical devices fall under type II–III, including several devices used for diagnosis and monitoring as well as software intended to moni - tor physiological processes. Requirements for Healthcare Professionals as a Deployer Under the AIA Qualifying remote patient monitoring tools as high-risk AI systems and healthcare profession -
als as deployers triggers a cascade of compli - ance requirements. The main requirements, which are listed in Article 26 of the AIA, are explained below. AI literacy As early as February 2025, healthcare profes - sionals that use AI systems must have sufficient knowledge about AI. AI literacy is defined in Recital 56 as the skills, knowledge and understanding that allow pro - viders, deployers and affected persons to make informed decisions regarding AI systems. This also includes awareness about the opportuni - ties, risks and potential harm associated with AI. Article 4 of the AIA provides that deployers, in the same ways as providers, are obliged to ensure, to the best of their ability, a sufficient level of AI literacy of their employees and any - one else who operates or uses these systems on their behalf. In the context of healthcare professionals, this means, for example, that physicians will need to properly inform and educate caregivers about AI systems’ risks and limitations. They should inform them about how to use the AI system, and of its limitations, as well as how and when to monitor data from the AI system. This also means that physicians and caregivers must be aware that AI systems used in remote patient monitoring may contain biases or ignore essen - tial information that could lead to false-positive or false-negative results. False positives could lead to unnecessary anxiety for patients and potentially unnecessary medical interventions. In contrast, false negatives can result in missed diagnoses or delayed treatment, potentially worsening patient outcomes.
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