Revista de diagnóstico del cáncer

Abstracto

Enhancing Leukemia Diagnosis with Artificial Intelligence and Machine Learning

Ashok Raj*

Leukemia, a heterogeneous group of hematologic malignancies, poses a substantial health burden worldwide. Timely and accurate diagnosis is paramount for effective management and improved outcomes. This research paper presents a comprehensive overview of leukemia diagnosis, including laboratory tests, imaging techniques, and molecular profiling approaches. The challenges in distinguishing leukemia symptoms from non-malignant conditions are discussed, alongside emerging technologies like liquid biopsies, next-generation sequencing, and artificial intelligence. Furthermore, the paper explores personalized medicine’s potential and the integration of diagnostic information with genomic profiling for tailored treatment strategies. Advancements in leukemia diagnosis hold promise for early detection and individualized care, heralding a new era in leukemia management.

Leukemia is a complex and life-threatening haematological malignancy that requires accurate and timely diagnosis for effective treatment planning. Traditional diagnostic methods often rely on the expertise of haematologists and pathologists, leading to subjectivity and potential errors. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown great promise in revolutionizing medical diagnostics. This research article explores the potential of AI and ML algorithms in enhancing leukemia diagnosis, focusing on their applications in automating detection, classification, and prognosis prediction. We review the current state of AI and ML technologies, discuss their integration into clinical workflows, address challenges, and highlight opportunities for future research. The implementation of AI-powered diagnostic tools has the potential to significantly improve the accuracy and efficiency of leukemia diagnosis, ultimately benefiting patient outcomes.