Automated machine learning (AutoML) and Explainable AI (XAI) are proving to be transformative in the bio-medical and healthcare sectors. These developing technologies are necessary as they tackle some of the most critical challenges. The medical field is known for its complicated, disorganized data - information that varies greatly in terms of format, quality, and relevance. Traditional machine learning methods often find it hard to extract valuable insights from such high-dimensional, diverse information. However, AutoML can deal with that complexity, as it automatically experiments with several algorithms and optimizes the models to find the best fit.

Equally crucial, XAI provides the clarity required in order to establish trustworthiness in such models in critical medical scenarios. It is essential that clinicians be able to understand how an AI arrived at its predictions or recommendations. XAI supplies them with that understanding. That builds trust and allows informed decision-making. Combining the capabilities of AutoML and XAI enables us to accelerate developing and deploying reliable, trustworthy AI solutions in health care abe have high potential.

We present, in this paper, the practical benefits of this hybrid approach by demonstrating its use with recent tools, such as Auto-Sklearn and PyCaret, on a medical dataset. It presents an exciting view of the future, wherein AI-driven healthcare becomes commonplace. As these revolutionary technologies advance and gain broader acceptance, we can anticipate a shift in how medical professionals utilize data to foster innovation and enhance patient outcomes. Health care organizations are urged to study and embrace the benefits of integrated AutoML and XAI solutions in order to remain ahead of the curve and provide the best possible care to their patients.