AI-Assisted Causality Support

Causality assessment is exactly the kind of task where AI assistance is genuinely useful and genuinely risky in roughly equal measure, which is why CIOMS Working Group XIV addresses it directly rather than leaving it to the same general principles covering MedDRA coding or narrative drafting. A model trained on structured case data — timing, dechallenge/rechallenge information, alternative explanations, prior reports — can suggest a WHO-UMC category or compute a Naranjo score mechanically, and can flag cases where the suggested causality looks inconsistent with how similar cases have historically been assessed, directing a reviewer’s attention toward cases that most need a careful second look.

That triage function is genuinely valuable, especially at scale — a safety database processing thousands of cases benefits enormously from a system that can pre-sort which cases are straightforward and which are genuinely ambiguous or unusual, so limited medical reviewer time gets spent where it matters most. Research published in 2026 describes exactly this pattern: an automated causality assessment model paired with interactive visual exploration tooling, where the model flags anomalous or uncertain assessments and a human works through the flagged subset rather than every case equally.

What CIOMS WG XIV is explicit about, and what this module’s earlier lessons on AI in MedDRA coding and narrative drafting already established as a consistent pattern across this entire course, is that final sign-off on causality, seriousness, and expectedness remains a qualified human’s responsibility, always. This isn’t a formality — it reflects a genuine risk specific to this task: causality assessment is a judgment call, and a model’s suggestion, presented confidently, can anchor a reviewer’s own reasoning before they’ve independently worked through the WHO-UMC or Naranjo criteria themselves. A reviewer who reads "AI suggests: Probable" before forming their own impression is at real risk of confirming that suggestion rather than genuinely re-deriving it.

The practical discipline this implies is the same one that’s run through every AI-related lesson in this course: use the suggestion as a starting point or a triage signal, never as the final answer, and be specifically alert to the anchoring risk that causality assessment carries more than most tasks — because unlike a MedDRA code, where a reviewer can check the suggestion against a fixed dictionary, causality is exactly the kind of judgment where "does this sound plausible" is a weaker check than "did I independently work through the criteria."