Safety of AI in Medical Devices

AI in Medical Devices: Safety Questions the Industry Can’t Afford to Ignore

By Pujitha Gourabathini
Safety of AI in Medical Devices

Artificial intelligence is moving quickly into mainstream medical devices, and the industry has become fluent in a familiar set of concerns: bias, transparency, and cybersecurity. These topics matter, but they don’t capture the risks most likely to shape patient safety in the coming decade. The deeper challenges lie in the interactions between algorithms, clinical workflows, data pipelines, and human decision making. Those interactions are where safety is won or lost, and they remain the least examined part of AI adoption.

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Balancing Cyber Risk

The FDA Raised the Bar on MedTech Cybersecurity: Are Companies Ready?

By Justin Kozak
Balancing Cyber Risk

In the current Cyber Threat environment, companies must have strong cyber liability insurance. Policies must specifically account for cyber-physical risks and the substantial costs of post-market remediation, including mandatory software patches and patient notifications. Insurers are well aware of the 524B mandate and are increasingly requiring proof of compliance before issuing coverage or offering favorable rates. How do you assure your cyber risk plan insurable?

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Smart Critical Care

Using AI and LLMs to fill in fragmented data gaps within complex critical care

By Dimitar Baronov, PhD
Smart Critical Care

AI and large language models (LLMs) are revolutionizing critical care by integrating scattered data from various sources to deliver real-time insights. This allows for prompt escalation or de-escalation of treatment and enhances patient outcomes. These technologies also help reduce staffing issues and prevent clinician burnout by continuously monitoring patient risk and supporting decision-making in busy ICU settings.

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