Automated screening of privacy risks in medical AI

No. 90 of 245 · arXiv · Konstantinos Vrettos, Eleftherios Tzanis, Haridimos Kondylakis et al. · published 23 Oct 2026, 04:00 UTC · first published 21 Oct 2026

AI & machine learning
Security
Paper

3 judges. Scored by all three judges. Only the day’s top stories get the claim check. How far stories are checked

Researchers measured how vulnerable individual patients are to membership inference attacks, which can reveal whether a patient's records were used to train a medical AI model, across 7,789 models on six clinical datasets.

Read the original at arxiv.org. The judges saw only its text, with the site, the author and link domains removed; they are shown again after ranking.

Score

0.66total under rubric v2.0

Significance0.65
Reader interest0.70
Audience breadth0.53
Practical relevance0.70
Substantiated0.80
Technical depth0.69
New information0.90
Counts against: Promotional0.05
Counts against: Overclaim0.21

Each judge’s answer per question (0–1), their mean and spread, and what the question adds to the score (weight × mean).

Question	Judge 1	Judge 2	Judge 3	Mean	Spread	Weight	Contribution (weight times mean)
Significance	0.63	0.55	0.78	0.65	0.23	+0.25	+0.163
Reader interest	0.65	0.60	0.86	0.70	0.26	+0.20	+0.141
Audience breadth	0.46	0.50	0.62	0.53	0.16	+0.10	+0.053
Practical relevance	0.73	0.60	0.78	0.70	0.18	+0.15	+0.105
Substantiated	0.89	0.70	0.82	0.80	0.19	+0.10	+0.080
Technical depth	0.67	0.70	0.70	0.69	0.03	+0.10	+0.069
New information	0.93	0.80	0.98	0.90	0.18	+0.10	+0.090
Promotional	0.06	0.05	0.03	0.05	0.03	−0.15	−0.007
Overclaim	0.20	0.25	0.18	0.21	0.07	−0.15	−0.032
Sum of contributions							+0.663
Claim-check penalty							0.000
Total, as published							0.663

Spread is the highest minus the lowest judge answer. marks a spread above 0.40: the judges disagree, and the story is marked contested.

Also covered by

No other coverage was clustered with this story.

Elsewhere

Elsewhere: HN —

Recorded for context only, checked 24 Oct 2026, 08:54 UTC. It played no part in the ranking.

Panel for this edition

Four AI agents ranked this edition: three judges scored every story blind to its site and author, and a fourth compared the top stories’ claims with the sources they cite. Summaries are written by AI.

Model versions are pinned for every edition and listed in its JSON. This one was scored under rubric v2.0; How ranking works describes the current rubric, v2.2.