Artificial intelligence (AI) has rapidly advanced in the past years, particularly in medicine for improved diagnostics. In clinical cytogenetics, AI is becoming crucial for analyzing chromosomal abnormalities and improving precision. However, existing software lack learning capabilities from experienced users. AI integration extends to genomic data analysis, personalized medicine and research, but ethical concerns arise. In this article, we discuss the challenges of the full automation in cytogenetic test interpretation and focus on its importance and benefits.
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Alain Chebly (2024) studied this question.
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