ReviewThe Journal of molecular diagnostics : JMD2025
A Perspective on Artificial Intelligence for Molecular Pathologists.
Review in The Journal of molecular diagnostics : JMD, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- From variants to answers: The evolution of genetic counseling in IEI.Journal of human immunity · 2026Review
- An overview of CRISPR-artificial intelligence theranostics: Current and emerging applications.Biomaterials translational · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The widespread adoption of next-generation sequencing technology in molecular pathology has enabled us to interrogate the genome as never before. The huge quantities of data generated by sequencing, the enormous complexity of human and microbial genetics, and the need for fast answers demand increasing use of automation as we diagnose disease and guide patient treatment. Much of this automation is based on tools that fall under umbrellas that have come to be known as machine learning and artificial intelligence. This review outlines some of the broad ideas that underpin these complex computational methods. It discusses the roles of pathologists and data scientists in generating new tools and factors to keep in mind when adopting these systems for use in molecular pathology. It pays special attention to regulatory and professional society guidance for validating them in individual institutions and to possible sources of bias. Finally, it briefly discusses ongoing efforts in computer science that may dramatically impact artificial intelligence in the future.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.