Kann KI seltene Krankheiten aus elektronischen Patientenakten diagnostizieren ?
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Background
Over the past few years several groups have built transformer-based models that read longitudinal EHR sequences and flag patients whose symptom trajectories match curated rare-disease cohorts. In 2023 a system trained on more than 30,000 US patient records achieved a positive predictive value above 0.7 for four lysosomal storage disorders but fell below 0.5 for a rarer glycogenosis subtype, illustrating uneven performance across disorders. A multi-centre study published the same year compared two proprietary LLMs fine-tuned on anonymised records from specialist clinics and found they recovered 79 % of previously missed cases of Niemann-Pick type C while introducing one false positive per ten true positives. Workflows that combine structured billing codes with unstructured clinician notes have shown the biggest gains, yet they remain brittle when applied to centres whose documentation styles diverge from the training corpora. At least one large health-system rollout was paused after an audit revealed clinically significant drift when ICD-10 codes were updated, underscoring the maintenance burden of keeping rare-disease models current.
SOURCE: BMJ, 2024
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Kann KI seltene Krankheiten aus elektronischen Patientenakten diagnostizieren?
Es gibt eng begrenzte Demos — die Geschworenen waren jedoch nicht einstimmig.
After weighing the evidence, the jury found AI capable of diagnosing certain rare diseases from electronic health records under constrained conditions, but not yet universally reliable—a distinction both jurors captured with their "Almost" verdicts. They agreed AI excels in narrow domains but falters when asked to generalize across the vast landscape of rare conditions. The bench’s ruling: "AI can spot the needles in the haystack, but it hasn’t yet learned to name every needle—just the ones it’s been trained to hold.
But the data is real.
The Case File
Across 21 sessions, 56 jurors have heard this case. Combined tally: 9 YES · 44 ALMOST · 3 NO · 0 IN RESEARCH.
Note: cumulative includes older juror opinions. The current session tally above is the live verdict.
By a vote of 0 — 2 — 0, the panel returns a verdict of FAST, with verdict confidence of 82%. The court so orders.
"Narrow AI models have demonstrated rare disease detection from EHR in limited studies, but not general, reliable across all rare conditions."
"Specialized AI models diagnose rare diseases from EHRs in narrow domains with partial reliability."
Die einzelnen Geschworenenaussagen werden im englischen Original gezeigt, um die Beweisgenauigkeit zu wahren.
Was das Publikum denkt
Nein 6% · Ja 91% · Vielleicht 3% 236 votesDiskussion
no comments⚖ 21 jury checks · aktuellste vor 43 Minuten
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