# A systematic review on the generative AI applications in human medical genetics

## TL;DR

This paper reviews how generative AI, especially large language models, are being used in medical genetics to improve disease diagnosis and data analysis.

## Contribution

The paper provides a systematic review and classification of generative AI applications in human medical genetics, highlighting their potential and challenges.

## Key findings

- Transformer-based models show strong performance in tasks like molecular diagnosis and genetic variant interpretation.
- Integration of multimodal data remains a challenge due to limitations in generalizability and clinical implementation.
- LLMs are being applied in knowledge navigation, clinical data analysis, and patient-professional interaction in genetics.

## Abstract

Although traditional statistical techniques and machine learning methods have contributed significantly to genetics and, in particular, inherited disease diagnosis, they often struggle with complex, high-dimensional data, a challenge now addressed by state-of-the-art deep learning models. Large language models (LLMs), based on transformer architectures, have excelled in tasks requiring contextual comprehension of unstructured medical data. This systematic review examines the role of generative Artificial Intelligence (AI) methods in human medical genomics, focusing on the genetic research and diagnostics of both rare and common diseases. Automated keyword-based search in PubMed, bioRxiv, medRxiv, and arXiv was conducted, targeting studies on LLM applications in diagnostics and education within genetics and removing irrelevant or outdated models. A total of 195 studies were analyzed, highlighting the prospects of their applications in knowledge navigation, analysis of clinical and genetic data, and interaction with patients and medical professionals. Key findings indicate that while transformer-based models perform well across a diverse range of tasks (such as identification of tentative molecular diagnosis from clinical data or genetic variant interpretation), major challenges persist in integrating multimodal data (genomic sequences, imaging, and clinical records) into unified and clinically robust pipelines, facing limitations in generalizability and practical implementation in clinical settings. This review provides a comprehensive classification and assessment of the current capabilities and limitations of LLMs in transforming hereditary disease diagnostics and supporting genetic education, serving as a guide to navigate this rapidly evolving field, while outlining application use cases, implementation guidance, and forward-looking research directions.