Artificial Intelligence in Healthcare and environmental science: opportunity, challenges and ethical considerations

Abstract

Artificial Intelligence (AI) became a revolution in the field of healthcare and environmental science offering an opportunity to analyze data intelligently, to make predictions using intelligent methods and to make automated decisions. In this paper, opportunities, challenges and ethics that come with the use of AI are discussed and the four popular algorithms, which include Artificial Neural Network (ANN), Random Forest (RF), Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost), are rated in terms of their performance. The combined healthcare and environmental data were handled and divided into training and test data to assess the predictive performance based on accuracy, precision, recall, F1-score, and effectiveness in computation. The experimental findings showed that the Convolutional Neural Network had the highest accuracy of 95.3, precision of 95.0, recall of 94.8 and F1-score of 94.9 which was better than the XGBoost (94.4% accuracy), ANN (93.6% accuracy), or RForest (91.9% accuracy). Though CNN generated better predictive output, XGBoost with the random forest have been found to have fewer computational complexity and higher interpretability levels thus suitable in practice where transparency is deemed necessary. Ethical concerns, such as the privacy of data and the bias of algorithms, their explainability, accountability, or compliance with regulations, prove to be vital concerns in the findings and need to be considered to achieve responsible use of AI. Altogether, the study finds that AI can significantly enhance healthcare services and environmental management provided with a well-developed governance, ethical protection and clear-cut decision-making models.