Evidence map›Paper›PMID 41078038›Full record

ReviewActa haematologica2026

Artificial Intelligence in Haematologic Diagnostics: Current Applications and Future Perspectives.

Annatina Sarah Schnegg-Kaufmann, Ulrike Bacher, Alicia Rovó, Martin Andres, Gertrud Wiedemann, Naomi Porret, Bijan Moshaver, Nicolas Kaufmann, Joëlle Tchinda, Sara C Meyer and 1 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Acta haematologica, 2026. 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

11 authors.

Annatina Sarah Schnegg-KaufmannDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland, annatina.schnegg-kaufmann@insel.ch.
Ulrike BacherDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Alicia RovóDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Martin AndresDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Gertrud WiedemannDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Naomi PorretDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Bijan MoshaverDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Nicolas KaufmannISS AG, Integrated Scientific Services, Bern, Switzerland.
Joëlle TchindaDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Sara C MeyerDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.
Anne Angelillo-ScherrerDepartment of Hematology and Central Hematology Laboratory, Inselspital Bern, University of Bern, Bern, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

<p>Background: Clinical researchers and laboratory specialists are striving to explore artificial intelligence (AI) to facilitate and optimize haematological diagnostics in response to the growing demand for more efficient and accurate diagnoses. Summary: This review summarizes current approaches integrating AI into blood and bone marrow cytomorphology, flow cytometry (FC), genetics, and haemostasis. Efforts include automated cell differentiation in peripheral blood and bone marrow aspirates, algorithms for identifying causes of anaemia, tools for rapid diagnosis of acute leukaemia, and other haematological entities. AI in FC may reduce subjectivity and variability, while in genomics, machine learning is increasingly implemented for processing high-throughput sequencing data and may enable automated detection of karyotypes in the future. In haemostasis, AI allows for automation in quality control, the establishment of personalized reference ranges, and potentially automated result interpretation. AI has, however, limitations such as cross-platform compatibility and often lacks sufficient validation. Ethical concerns include risks of bias and regulations are lagging behind the rapid developments. Key Messages: AI shows promise for automating and improving many steps in haematological diagnostics, though final interpretation still needs expert haematologists. </p>.

Indexed as

Artificial IntelligenceHematologic DiseasesHematologyFlow CytometryHumansArtificial intelligenceCytomorphologyFlow cytometryGenomicsHaemostasis

Identifiers

PMID41078038
PMCPMC13645335

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.