![\"Medical](\"https:\/\/www.jamasoftware.com\/media\/2026\/08\/ai-in-medical-device-development.png\")
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Conversations around\u00a0AI in medical device\u00a0development\u00a0have shifted from\u00a0<\/span>whether organizations should adopt AI to\u00a0how\u00a0they can use it responsibly.<\/span>\u00a0<\/span><\/p>\n Medical device manufacturers see enormous potential for AI to accelerate\u00a0requirements\u00a0development, improve documentation,\u00a0identify\u00a0inconsistencies, generate test cases, and reduce repetitive engineering work.\u00a0<\/span>\u00a0<\/span><\/p>\n At the same time, quality and regulatory leaders are asking equally important questions:<\/span>\u00a0<\/span><\/p>\n These are all valid concerns.\u00a0In fact,\u00a0regulatory developments suggest these are exactly the questions organizations need to be asking.\u00a0<\/span>\u00a0<\/span><\/p>\n A recent FDA warning letter highlights the risks of using AI without\u00a0appropriate human\u00a0oversight or validation. In the warning letter, the FDA cited a pharmaceutical manufacturer for relying on AI-generated compliance guidance\u00a0<\/span>without appropriate human review or validation<\/span><\/a>.<\/span>\u00a0<\/span><\/p>\n According to the FDA’s findings,\u00a0the\u00a0organization\u00a0failed to\u00a0adequately verify AI-generated content before incorporating it into quality activities.\u00a0These observations, in addition to\u00a0other\u00a0systemic\u00a0issues,\u00a0resulted\u00a0in significant compliance\u00a0deficiencies\u00a0and\u00a0ultimately halted\u00a0production.\u00a0<\/span>\u00a0<\/span><\/p>\n The lesson\u00a0is\u00a0clear.\u00a0Organizations\u00a0remain\u00a0responsible for validating AI-assisted work and\u00a0maintaining\u00a0effective quality oversight.\u00a0<\/span>\u00a0<\/span><\/p>\n Ultimately,\u00a0AI\u00a0is becoming another engineering tool. Like every other tool used in regulated product development, it must\u00a0operate\u00a0within a framework of governance, accountability, and traceability.<\/span>\u00a0<\/span><\/p>\n The conversation around AI in medical device\u00a0development\u00a0often focuses on automation. While automation is certainly part of the story, it misses the larger opportunity.\u00a0The greatest value of AI is\u00a0allowing engineers to spend more time doing engineering.<\/span>\u00a0<\/span><\/p>\n For decades, development teams invested countless\u00a0hours\u00a0writing requirements, reviewing documentation, updating specifications,\u00a0<\/span>creating traceability matrices<\/span><\/a>, and preparing compliance evidence. These activities are essential, but they are also repetitive and\u00a0time consuming.<\/span>\u00a0<\/span><\/p>\n AI has the potential to accelerate many of these tasks by helping teams:<\/span>\u00a0<\/span><\/p>\n None of these activities\u00a0eliminate\u00a0the need for engineering\u00a0expertise. Instead, they shift where engineers spend their time.<\/span>\u00a0<\/span><\/p>\n One way to think about AI is through the 80\/20 rule.\u00a0Historically, engineers completed\u00a0nearly every\u00a0step themselves.\u00a0They gathered information,\u00a0drafted\u00a0requirements,\u00a0reviewed documentation,\u00a0created supporting artifacts, and\u00a0performed\u00a0critical\u00a0analysis.\u00a0With modern AI tools, the workflow changed.\u00a0<\/span> This new AI workflow shifts where engineers spend their time, allowing them to\u00a0maintain\u00a0human oversight\u00a0required\u00a0in regulated development\u00a0while\u00a0focusing\u00a0on\u00a0activities that create the greatest value, including:<\/span>\u00a0<\/span><\/p>\n\n
What the FDA Warning Letter Teaches Us About Responsible AI<\/span>\u00a0<\/span><\/h2>\n
Where Humans Fit in\u00a0With\u00a0AI in Medical Device\u00a0Development<\/span>\u00a0<\/span><\/h2>\n
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The New 80\/20 Rule for Engineering Teams<\/span>\u00a0<\/span><\/h2>\n
\n<\/span><\/p>\nThe New 80\/20 Workflow With AI in Medical Device Development <\/span><\/h3>\n
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\n Step<\/span><\/b>\u00a0<\/span><\/td>\n Who Owns It<\/span><\/b>\u00a0<\/span><\/td>\n Activities<\/span><\/b>\u00a0<\/span><\/td>\n<\/tr>\n \n 1. Define the work (10%)<\/span><\/b>\u00a0<\/span><\/td>\n Engineer<\/span>\u00a0<\/span><\/td>\n Set\u00a0objectives, provide context, define constraints, craft prompts<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 2. Accelerate execution (80%)<\/span><\/b>\u00a0<\/span><\/td>\n AI-Enabled Workflows<\/span>\u00a0<\/span><\/td>\n Draft requirements, summarize documentation, generate test cases,\u00a0identify\u00a0inconsistencies<\/span>\u00a0<\/span><\/td>\n<\/tr>\n \n 3.\u00a0Validate\u00a0and decide (10%)<\/span><\/b>\u00a0<\/span><\/td>\n Engineer<\/span>\u00a0<\/span><\/td>\n Review outputs, assess risk, verify accuracy, approve changes, apply engineering judgment<\/span>\u00a0<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n \n