Privacy risks from medical AI tools are not shared equally
NEWS AND VIEWS 04 August 2026 Privacy risks from medical AI tools are not shared equally Privacy attacks can reveal whether someone’s medical data was used to train an AI model. People who differ from the majority are the most vulnerable to such attacks. By Haoran Zhang0 & Marzyeh Ghassemi1 Haoran Zhang Haoran Zhang is in the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
- ▪NEWS AND VIEWS 04 August 2026 Privacy risks from medical AI tools are not shared equally Privacy attacks can reveal whether someone’s medical data was used to train an AI model.
- ▪People who differ from the majority are the most vulnerable to such attacks.
- ▪By Haoran Zhang0 & Marzyeh Ghassemi1 Haoran Zhang Haoran Zhang is in the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Nature |
| Canonical URL | https://www.nature.com/articles/d41586-026-02288-9 |
| Publication time | Tue, 04 Aug 2026 18:07:07 +0000 |
| Retrieval time | 2026-08-04T18:15:44.306Z |
| Last seen | 2026-08-04T18:15:44.306Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | _FdnQrYBc1Cu · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
NEWS AND VIEWS 04 August 2026 Privacy risks from medical AI tools are not shared equally Privacy attacks can reveal whether someone’s medical data was used to train an AI model. People who differ from the majority are the most vulnerable to such attacks. By Haoran Zhang0 & Marzyeh Ghassemi1 Haoran Zhang Haoran Zhang is in the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA. View author publications Search author on: PubMed Google Scholar Marzyeh Ghassemi Marzyeh Ghassemi is in the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Nature.