This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. The integration of artificial intelligence with digital health data is crucial as healthcare increasingly tailors treatments to individual characteristics. Using a topic-modeling approach, the paper distills key themes from 27 journal articles, focusing on data-driven medicine, predictive modeling, deep learning in biomedical data, and machine learning in medicine. The review emphasizes the importance of explainable artificial intelligence in fostering transparency, accountability, and trust within the healthcare domain. It highlights the need for further development and validation of explanation methods to advance precision healthcare delivery. The review also discusses the ethical challenges and the role of explainable AI in addressing these issues, particularly in the context of deep learning and its applications in precision medicine. The findings underscore the potential benefits of explainable AI in enhancing the trustworthiness and reliability of AI-driven decisions in healthcare.This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. The integration of artificial intelligence with digital health data is crucial as healthcare increasingly tailors treatments to individual characteristics. Using a topic-modeling approach, the paper distills key themes from 27 journal articles, focusing on data-driven medicine, predictive modeling, deep learning in biomedical data, and machine learning in medicine. The review emphasizes the importance of explainable artificial intelligence in fostering transparency, accountability, and trust within the healthcare domain. It highlights the need for further development and validation of explanation methods to advance precision healthcare delivery. The review also discusses the ethical challenges and the role of explainable AI in addressing these issues, particularly in the context of deep learning and its applications in precision medicine. The findings underscore the potential benefits of explainable AI in enhancing the trustworthiness and reliability of AI-driven decisions in healthcare.