AI tools typically ask for basic details—injury type, body part, treatment dates, and missed work—and then generate a broad range based on patterns from other claims. The problem is that two injuries that look similar on the surface can have very different legal results.
In Hialeah, common real-world factors that can throw off AI estimates include:
- Shift work and wage structure: overtime, shift differentials, and variable schedules can make wage loss harder to calculate if the record isn’t complete.
- Fast-return pressure: employers and insurers may push for earlier work release. If you return while restrictions aren’t clearly documented, the evidence can become inconsistent.
- Injury reporting timing: if symptoms weren’t reported promptly (or were reported in a way that conflicts with later medical notes), insurers may dispute the injury narrative.
- Medical documentation detail: AI doesn’t “see” the nuance in your treatment notes—like objective findings, functional limits, or why certain restrictions are necessary.
An AI range can feel helpful, but it can also create false confidence—leading you to accept an offer before you know what’s missing from your medical and wage proof.


