Final document of the IMDRF Medical Device Cybersecurity Working Group on the Software Bill of Materials (SBOM), complementing the medical device cybersecurity principles set out in IMDRF N60. It provides recommendations for medical device manufacturers on creating, maintaining, and distributing SBOMs, and for healthcare providers on receiving and managing them. It also describes how both manufacturers and healthcare providers can use SBOMs in risk management, vulnerability management, and incident response across the total product life cycle.

Final document of the IMDRF Medical Device Cybersecurity Working Group on the cybersecurity of legacy medical devices, complementing IMDRF N60. It divides the total product life cycle for cybersecurity into four stages (Development, Support, Limited Support, and End of Support (EOS)) and sets out the responsibilities and expectations of medical device manufacturers and healthcare providers at each stage in three areas: communication, risk management, and transfer of responsibility. Responsibility shifts from the manufacturer to the healthcare provider as the life cycle progresses, and cybersecurity responsibility for devices used beyond the EOS communicated by the manufacturer rests entirely with the healthcare provider. The document also notes that the end of life or end of support of a software or firmware component can prematurely move the device itself into a later stage, which manufacturers should address in advance.

Edition 1 of the IMDRF Artificial Intelligence Medical Devices (AIMD) Working Group document on key terms and definitions for Machine Learning-enabled Medical Devices (MLMD). It establishes terms and definitions across the total product life cycle to promote consistency, support global harmonization, and provide a foundation for future guidelines on MLMD. Section 5 provides key definitions relevant to machine learning used in medical devices (including the definition of MLMD), Section 6 gives definitions from technical standards (e.g., bias, continuous learning, reference standard, reinforcement learning), and Section 7 discusses common machine learning terms. Most terms were previously defined in GHTF documents or internationally recognized AI standards, while some were developed or discussed by the AIMD Working Group. The document also notes that the term "bias" is used differently in data science and in legal discussions.