XAI in Genomics with LLM Generated Explanations in Oncology

The amount of genetic data available for analysis is rapidly growing, and the importance of analyzing this type of data is paramount to the field of precision medicine, in which diagnostic and prognostic tasks rely on genetic data analysis in many fields, including in oncology. Given the high dimensionality of genomic data, AI or machine learning in genomics requires more sophisticated methods to help clinicians interpret their results, as gene-level explanations may not be sufficient to make sense of these results. Given the growing importance of XAI, a rapidly growing number of AI papers in genomics refer to XAI methods. In this paper, we present general methods for XAI in genomics and focus on how LLMs can contribute to explanations at the case level in the domain of oncology. The preliminary results obtained in this study are encouraging about the role LLM chatbots can play in the user interface to help explain machine learning results to users at the case level in conversational or visual interaction modalities.