Evidence map›Paper›PMID 39165642›Full record

ArticleFrontiers in molecular biosciences2024

Patient safety and healthcare quality of U.S. laboratory developed tests (LDTs) in the AI/ML era of precision medicine.

Emma L Kurnat-Thoma

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2024. 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

1 author.

Emma L Kurnat-ThomaGeorgetown Institute for Women, Peace and Security, Walsh School of Foreign Service, Georgetown University, Washington, DC, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This policy brief summarizes current U.S. regulatory considerations for ensuring patient safety and health care quality of genetic/genomic test information for precision medicine in the era of artificial intelligence/machine learning (AI/ML). The critical role of innovative and efficient laboratory developed tests (LDTs) in providing accurate diagnostic genetic/genomic information for U.S. patient- and family-centered healthcare decision-making is significant. However, many LDTs are not fully vetted for sufficient analytic and clinical validity via current FDA and CMS regulatory oversight pathways. The U.S. Centers for Disease Control and Prevention's Policy Analytical Framework Tool was used to identify the issue, perform a high-level policy analysis, and develop overview recommendations for a bipartisan healthcare policy reform strategy acceptable to diverse precision and systems medicine stakeholders.

Indexed as

CMS clinical laboratory improvement amendments (CLIA)FDA proposed rulegenomic testshealth care qualitylaboratory developed tests (LDTs)patient safetyregulatory compliancereturn of results (RoR)

Identifiers

PMID39165642
PMCPMC11334219

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.