AI tools typically work by turning your answers into broad categories, then comparing them to patterns from other cases. That approach can be helpful for thinking about possible damages, but burn injuries are uniquely complex. In Michigan—where people work across manufacturing, automotive supply chains, construction, agriculture, healthcare, and hospitality—burn mechanisms vary widely, and so do the injuries and the proof needed.
A scald from a commercial kitchen may look similar on the surface to a steam burn, but the medical documentation, the likely burn depth, and the expected recovery can differ substantially. The same is true for electrical burns, chemical exposure, and contact burns from hot equipment. AI cannot reliably measure the severity of tissue damage, the likelihood of grafting, or whether complications like infection, hypertrophic scarring, or loss of range of motion will develop later.
In practice, adjusters often focus on whether the medical records support the story, whether treatment was timely and consistent, and whether the burn pattern matches the alleged cause. If your estimate is based on incomplete inputs, it may understate or overstate the value of your claim. That’s one reason you should treat AI output as a prompt for what to gather—not as a forecast of what you will receive.


