# Research advances in the adjunctive diagnosis of acute myeloid leukemia

## TL;DR

This paper reviews how artificial intelligence is being used to improve the diagnosis of acute myeloid leukemia, focusing on image analysis, flow cytometry, and genetic data.

## Contribution

The paper systematically categorizes and evaluates recent AI-based approaches for AML diagnosis based on data modality.

## Key findings

- AI improves diagnostic efficiency and reduces subjective bias in AML diagnosis.
- AI-based methods show promise in identifying novel biomarkers for AML.
- Current AI models lack generalizability and clinical interpretability.

## Abstract

Acute myeloid leukemia (AML) is a highly heterogeneous malignant hematological neoplasm. Although standard diagnostic procedures have been established, traditional methods still face limitations with regard to efficiency, accuracy, and standardization. In recent years, artificial intelligence (AI) has demonstrated notable advantages in medical image analysis, flow cytometry interpretation, and genetic data modeling, offering new approaches for adjunctive diagnosis of AML. This review systematically summarizes recent research advances in adjunctive diagnosis of AML, categorizing current AI-based approaches based on data modality into three groups: blood smear image analysis, flow cytometry data interpretation, and genetic data modeling. We focus on the application strategies, diagnostic performance, and limitations of these approaches. Studies have shown that AI not only enhances diagnostic efficiency and reduces subjective bias, but also holds promise in identifying novel biomarkers. Nevertheless, current models still suffer from limited generalizability and insufficient clinical interpretability. Future efforts should prioritize data standardization, improve model transparency, and facilitate the seamless integration of AI systems into clinical workflows to support precision diagnosis and treatment of AML.