What does this research mean for the field?

Although artificial intelligence and machine learning applications span all stages of acute myeloid leukemia care, current models are clustered at low clinical-readiness levels and require targeted pragmatic trials to transition into clinical practice. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate the current landscape and clinical readiness of artificial intelligence and machine learning applications across the clinical management trajectory of acute myeloid leukemia.

August 28, 2026CellsOpen Access

Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia

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Authors

FMFederico De Marchi GCGiulia Ciotti AAAlessandro Atanasio

Discussion

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Overview

Review reveals pervasive diagnostic and prognostic artificial intelligence tools in acute myeloid leukemia, highlighting that most models lack prospective clinical trial evaluation.

Key Points

To evaluate the current landscape and clinical readiness of artificial intelligence and machine learning applications across the clinical management trajectory of acute myeloid leukemia.
Categorized existing artificial intelligence and machine learning applications by acute myeloid leukemia clinical decision points rather than technical modalities.
Graded identified computational models using a standardized five-tier clinical-readiness level framework (CRL-AML 1–5).
Demonstrated that algorithms successfully automate flow-cytometry gating, read mutations from bone-marrow smears, and predict venetoclax–azacitidine response across external cohorts.
Revealed that hundreds of published acute myeloid leukemia models cluster at early development stages (CRL-AML 1–2), with none currently undergoing prospective clinical evaluation.
Defined a targeted three-year implementation agenda detailing consortia, curated datasets, and pragmatic clinical trials required to move algorithms into routine practice.

Cite This Study

Marchi et al. (2026) studied this question.

synapsesocial.com/papers/6a9145c5d15324a1df3a8f8a https://doi.org/10.3390/cells15171539

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August 28, 2026CellsOpen Access

Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia

View Full Paper

Ask AI

Bookmark

Authors

FMFederico De Marchi GCGiulia Ciotti AAAlessandro Atanasio

Discussion

Member takes

Overview

Review reveals pervasive diagnostic and prognostic artificial intelligence tools in acute myeloid leukemia, highlighting that most models lack prospective clinical trial evaluation.

Key Points

To evaluate the current landscape and clinical readiness of artificial intelligence and machine learning applications across the clinical management trajectory of acute myeloid leukemia.
Categorized existing artificial intelligence and machine learning applications by acute myeloid leukemia clinical decision points rather than technical modalities.
Graded identified computational models using a standardized five-tier clinical-readiness level framework (CRL-AML 1–5).
Demonstrated that algorithms successfully automate flow-cytometry gating, read mutations from bone-marrow smears, and predict venetoclax–azacitidine response across external cohorts.
Revealed that hundreds of published acute myeloid leukemia models cluster at early development stages (CRL-AML 1–2), with none currently undergoing prospective clinical evaluation.
Defined a targeted three-year implementation agenda detailing consortia, curated datasets, and pragmatic clinical trials required to move algorithms into routine practice.

Cite This Study

Marchi et al. (2026) studied this question.

synapsesocial.com/papers/6a9145c5d15324a1df3a8f8a https://doi.org/10.3390/cells15171539

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