AI tools typically generate an output using simplified inputs—injury description, age, and a few selected care needs. The problem is that spinal cord injury value in real cases depends heavily on documentation that AI tools can’t reliably access:
- Neurological findings and whether impairment is improving, stabilizing, or worsening
- Medical causation (how clinicians connect the injury to the crash/incident)
- Functional limitations that affect mobility, self-care, breathing, and skin integrity
- A credible life-care plan tying recommended treatment to costs
In Half Moon Bay, many cases involve collisions on busy corridors, worksite incidents, or summer/holiday traffic patterns. Those fact details shape fault and causation—and they also shape whether future medical needs are treated as “possible” or “reasonably certain.”


