As organizations introduce additional technologies, including AI-enabled tools and CRM platforms, these gaps become more pronounced. Those that are seeing meaningful improvements are taking an end-to-end view, evaluating how systems interact within a unified workflow and ensuring that data flows seamlessly and actions are triggered in a coordinated manner, thereby reducing the need for manual intervention and follow-up.
In this part 2 of a 4-part series, Melissa highlight how the moment AI begins to influence decisions or workflows, it is operating within a regulated environment. Aligning with documentation standards, keeping data within established pathways, and ensuring actions are consistently recorded are all ways to support that reality early, rather than adapting to it later. Seen this way, integration is not just a technical milestone.
Human factors engineering plays a critical role in the design of AI-enabled medical devices and whether they might improve care or introduce new risks.
What we’re reading on LinkedIn… Review of leading healthcare AI reports illustrate several conditions creating fertile ground for applied AI in healthcare operations.
Noah Labs Vox,™ a voice-based algorithm that detects worsening heart failure weeks before hospitalization, has received US Food and Drug Administration (FDA) Breakthrough Device Designation (BDD).
Rene Zoelfl, Global Industry Advisor for PTC’s MedTech practice, shares how intelligent product lifecycle at Fresenius Medical Care connects cross-discipline teams through a digital fabric built on a shared data foundation.
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.
A recent survey of 2,000 UK patients found one in four patients (24%) already use AI for health guidance, and nearly one in three (30%) would be willing to consult AI or social media rather than wait to see a clinician. Despite this growing reliance on digital tools, the same survey revealed a disconnect in patient confidence, with almost 80% reporting that they do not feel fully in control of their healthcare. How might ChatGPT Health accelerate this shift, as well as what needs to happen to ensure AI adoption genuinely empowers patients rather than adding further complexity?
How is software-enablement — connected devices and artificial intelligence — reshaping how products are conceived, developed, and deployed in clinical practice? Fragmented operations in large MedTech organizations are not keeping pace with the complexity of products in their pipelines.
The initial fear that the artificial intelligence and machine learning evolution will replace humans is shifting. A new narrative recognizes the potential for an AI-enabled workforce — one where the technology is a jobs creator, enabling us all to be more productive rather than making millions of people redundant or obsolete — actually giving rise to the multi-disciplinary, power employee.
