The role of Explainable AI in Digital Health using Explainable Artificial Intelligence and Machine Learning Techniques
Keywords:
Digital health, Explainable AI, Machine Learning and Deep Learning, Precsion medicalAbstract
Systematic literature reviews are crucial for understanding the current state of research and identifying gaps or areas for improvement. Machine learning methods hold great promise for predicting comorbidities and enhancing precision medicine. Using the PRISMA framework for our systematic review is an excellent approach as it provides a standardized method for conducting and reporting systematic reviews and meta-analyses, ensuring transparency and reproducibility. Searching multiple databases like Ovid Medline, Web of Science, and PubMed also helps ensure comprehensive coverage of relevant literature. Given the broad scope of our search terms, we will likely capture a wide range of studies focusing on disease coexisting conditions prediction using various machine learning techniques and traditional predictive modeling methods. This inclusivity can provide a comprehensive understanding of the current research landscape in this field. The advancement of explainable machine learning in coexisting conditions prediction holds immense potential for identifying previously unrecognized health needs. By leveraging sophisticated ML techniques alongside enhanced interpretability and explainability, healthcare professionals can gain deeper insights into the complex relationships between diseases and their comorbidities. These predictive models not only have the capability to identify known comorbidities but also have the potential to uncover novel associations and patterns that might have been overlooked using traditional methods. This means that patient groups previously not recognized as at risk for specific comorbidities could be identified, allowing for early intervention and personalized treatment strategies.
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