An N-of-1 Artificial Intelligence Ecosystem for Precision Medicine.

P Fard, A Azhir, N Rezaii, J Tian, H Estiri

Abstract readPreprint

In one paragraph

Article in ArXiv, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

P FardClinical Augmented Intelligence, Massachusetts General Hospital, Boston, MA 02114, USA.

A AzhirClinical Augmented Intelligence, Massachusetts General Hospital, Boston, MA 02114, USA.

N RezaiiDepartment of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA.

J TianClinical Augmented Intelligence, Massachusetts General Hospital, Boston, MA 02114, USA.

H EstiriClinical Augmented Intelligence, Massachusetts General Hospital, Boston, MA 02114, USA.

Funding

Temporal Phenotypes and Risk Models for the Post-COVID Syndrome and its sub-typesR01AI165535 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Hossein Estiri, SHAWN N MURPHY · 2022 to 2026

$4.1M

The confluence of extreme heat and cold on aging and longevityR01AG074372 · NIA · HARVARD MEDICAL SCHOOL · PI Francesca Dominici, Hossein Estiri · 2025 to 2026

$1.5M

NIAID NIH HHS R01 AI165535NIA NIH HHS R01 AG074372

6 · The paper itself

Abstract

Artificial intelligence in medicine is built to serve the average patient. By minimizing error across large datasets, most systems deliver strong aggregate accuracy yet falter at the margins: patients with rare variants, multimorbidity, or underrepresented demographics. This

Identifiers

PMID41281209

PMCPMC12636765

What OpenQuestion holds

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LicenceCC BY-NC-SA

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.