AI incident #1426 · 5 January 2026

Perplexity AI Reportedly Misstated CLL Research, Allegedly Contributing to Delayed Treatment and Prolonged Suffering

Open on the AI Incident Database 1 news report Snapshot 7 Sep 2026

What happened

Joseph Neal Riley reportedly used Perplexity AI to self-diagnose a rare complication of chronic lymphocytic leukemia (CLL) and, based on that output, allegedly delayed an oncologist-recommended Ven-Obi treatment for about a year. His son, Benjamin Riley, alleges that Perplexity misstated the medical research it cited, a conclusion he purportedly later confirmed with the study's authors.

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News reports (1)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

25 Jan 2026
The role of AI in the death of my father
buildcognitiveresonance.substack.com · Benjamin Riley · AIID #7031

Who was involved

Alleged deployer: Perplexity Ai
Alleged developer: Perplexity Ai
Alleged harmed party: Joseph Neal Riley, Benjamin Riley, Epistemic Integrity, People Seeking Medical Advice

Classification (MIT AI Risk Repository taxonomy)

Risk domain: Misinformation
Risk subdomain: 3.1 False or misleading information
Causal entity: AI
Intent: Unintentional
Timing: Post-deployment
Harm level: —
Sectors: —
Countries: —

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 3.1.

Pursuing Consistent Context
"LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Knowledge Gaps
"Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Hallucinations
"LLMs generate nonsensical, untruthful, and factual incorrect content"
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Faithfulness Errors
"The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Defective Decoding Process
In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Untruthful Content
"The LLM-generated content could contain inaccurate information"
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Factuality Errors
"The LLM-generated content could contain inaccurate information" which is factually incorrect
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)
Noisy Training Data
"Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....
Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

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