# kaal:claim:5095633-015

**Claim.** In healthcare, biased or stale training data produces algorithms that misdiagnose underrepresented populations and thereby reinforce existing health disparities instead of reducing them.

**Type.** failure  **Support.** argued

**Holds when.**

- clinical deployment on populations underrepresented in training data

**Source quote.**

> In healthcare, for instance, biased or stale training data could lead to algorithms that misdiagnose underrepresented populations, reinforcing existing health disparities rather than alleviating them.

**From.** Wulf A. Kaal, *Artificial Intelligence The Final Frontier* (2025), 2.2. Implications for Society and the Future of Humanity, page 5

**Cite as.** Wulf A. Kaal, Artificial Intelligence The Final Frontier (2025). SSRN: https://ssrn.com/abstract=5095633

**Verify.** sha256 of source PDF `cbb484711f89bcefc9fc6a5730a1ed0a3f764d7999ad9b6f7d8ea05634c26c63` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202025%20-%20Artificial%20Intelligence%20The%20Final%20Frontier.pdf

**Failure mode.** clinical bias amplification  (family: ai-model-and-training-failure)

**Topics.** ai-and-agents, empirical-evidence

**Keywords.** healthcare-ai, dataset-bias, health-disparities, misdiagnosis

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