The integration of artificial neural networks (ANNs) into clinical decision support systems based on health information systems represents a transformative shift in healthcare technologies that improves clinical decision making using advanced machine learning techniques. This evolution has arisen in response to the growing complexity and volume of medical data, which requires more sophisticated decision support tools that can provide personalized recommendations and improve patient outcomes. Neural networks, characterized by their ability to learn from large data sets, have played a critical role in developing predictive models that identify patient risks and suggest solutions, optimizing clinical workflows and improving the quality of care. The use of ANNs has sparked debates about the effectiveness, usability, and ethical implications of artificial intelligence (AI)-based decisions in healthcare. Research has shown significant improvements in predictive accuracy compared to traditional rule-based systems, but challenges in their implementation, including data quality, algorithmic bias, and the need for transparency in AI decision-making processes remain. The shift from traditional decision-making approaches to neural network-based systems is intensifying the debate around trust and explainability in healthcare technologies. While ANNs offer promising advances in medical decision-making, their black-box nature raises concerns about the reliability and transparency of the recommendations they generate among healthcare providers. Addressing these issues is essential to ensuring the integration of ANNs into healthcare information systems, ultimately aiming to provide equitable and effective patient care. As the field continues to evolve, ongoing and emerging research is critical to improving, mitigating potential biases, and enhancing the functionality of ANN-based clinical decision support systems. The convergence of AI and healthcare heralds a new era that has the potential to revolutionize clinical practice, but also requires careful consideration of the ethical implications and adherence to fundamental principles of patient care.

Keywords: clinical decision support systems, neural networks, artificial intelligence, electronic medical records, medical information systems