Improving Prospective Healthcare Outcomes by Leveraging Open Data and Explainable AI

The use of Artificial Intelligence (AI) in healthcare can improve diagnosis and clinical workflows, advancing standards and improving outcomes. AI for healthcare raises significant ethical and legal challenges. AI model development and deployment require transparency and interpretability. Transparency is the capacity to comprehend how an AI model makes recommendations, whereas interpretability is the ability to understand why. One such issue is the lack of transparency and interpretability of AI models, which might lead to a lack of trust in its decisions. Explainable AI (XAI) is an emerging research field that tries to solve this problem by creating AI models that can explain their recommendations. AI models need enough data to be accurate and reliable, thus they must be trained on various and extensive data sets. Open data and data sets (ODDS) can aid AI model development and training. Medical images, lab reports, and patient data are used to train algorithms to identify trends and make predictions in healthcare. Open data can help XAI in healthcare to enable AI models to be transparent, accountable, and interpretable. Open data makes possible AI model validation and replication. This is especially crucial in healthcare, where AI model flaws or biases can have serious implications. This paper discusses the use of ODDS and XAI to improve healthcare. ODDS's capability to promote trust and openness and best practices for using XAI in healthcare are also explored. The benefits and challenges of employing XAI and ODDS in their current forms are also discussed.