Artificial intelligence is reshaping how insurers and law firms handle personal injury claims. From initial triage to settlement offers, algorithms now analyze medical codes, treatment timelines, and historical verdicts to recommend ranges. While this can speed up decisions, it also creates risks of undervaluation and bias. This article explores how AI appears in claims, what lawyers and claimants should ask for in discovery, and the strategies available in 2025 to push back against unfair results.
What “AI in Claims” Actually Means
Insurers use AI across several stages of a claim:
- Triage and routing: Models score severity and assign adjusters.
- Liability and damages estimation: Algorithms process medical codes, treatment timelines, and venue factors to suggest settlement ranges.
- Fraud analytics: Tools detect patterns that signal possible misrepresentation.
Across the industry, AI adoption continues to accelerate in 2025, with insurers and law firms integrating these tools into daily workflows.
How Algorithms Can Lower Offers
While insurers argue that AI brings efficiency and consistency, many claimants face low offers when algorithms dominate decision-making. Common issues include:
- Garbage in, garbage out: Missing physical therapy records or miscoded entries can depress valuations.
- Historical bias: If past data undervalued certain injuries or demographics, algorithms repeat those errors.
- Over-weighting averages: Outlier facts that justify higher awards may be ignored.
Without proper oversight, AI-driven claim valuations may reduce fairness and transparency in personal injury negotiations.
Discovery Requests That Matter
When litigation begins, targeted discovery can shine light on the algorithms insurers rely on. Consider requesting:
- Model provenance: Vendor, version, and supporting documentation.
- Input logs: Data fields used for this specific claim.
- Feature importance summaries: The factors most influential in the valuation.
- Human oversight policies: Escalation protocols and override rules.
- Validation evidence: Benchmarks, error rates, and accuracy claims.
These requests help ensure that valuation tools are not black boxes shielded from accountability.
Practical Ways to Raise Valuation
Lawyers and claimants can take proactive steps to challenge low valuations:
- Fix the inputs: Audit medical codes, provide missing reports, and clarify treatment timelines.
- Context packets: Include wage loss data, venue-specific comparisons, and personal impact statements.
- Expert critiques: Retain specialists to analyze algorithmic methodology or present alternative calculations.
- Timeline visuals: Present a clear chronology of treatment and recovery to highlight overlooked evidence.
- Escalation: Request review beyond the frontline adjuster when outputs appear flawed.
Ethical and Regulatory Notes
Bar associations stress that AI must be treated as a tool, not a decision-maker. Attorneys remain responsible for oversight, accuracy, and fairness. Transparency, competency, and accountability should guide any reliance on algorithms in the personal injury context.
Key Takeaways
- AI is now embedded in personal injury claims, influencing settlement ranges.
- Algorithms can undervalue claims when data is incomplete or biased.
- Discovery can expose inputs, policies, and oversight procedures.
- Correcting inputs, adding context, and pushing for human review can help raise valuations.
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FAQ
- Can insurers refuse to reveal how AI valued my claim?
Once litigation begins, you may seek discovery of inputs, documentation, and oversight policies, even if proprietary source code is withheld. - Do law firms use AI tools too?
Yes. In 2025, many firms employ AI for research and drafting, but ethical rules require careful supervision and accountability.
Call to Action: If your claim feels undervalued by an algorithm, contact us today. Our team can review the data, identify errors, and prepare strategies to ensure fair compensation.