Smart Health Technologies: Integrating AI for Predictive Diagnosis and Effective Public Health Management

Abstract

There is enormous potential in implementing artificial intelligence (AI) in smart health technologies to increase predictive or preventive diagnosis and health management among the population. This paper will explain how machine learning, deep learning, wearable sensors, electronic health records, and smart analytical systems can be utilized to help with the early detection of disease, continuous health treatment, clinical decision-making, and planning of population health. Such claims are supported by a systematic literature review investigating the applications of AI in mental health, chronic disease management, respiratory diagnostics, oral health, and tracking a population at risk. Furthermore, an analytical structure written in Python is proposed to benefit the comparison between the Logistic Regression, Decision Tree, and the Random Forest algorithms to classify the disease. Data processing involves data preparation, exploratory analysis, model training and performance analysis (accuracy and precision, recall, F1-score, and receiver operating characteristic area under the curve ROC-AUC). The framework is of special interest in the features that are important, the model that can be interpreted and the implications of predictive analytics as a resource allocation tool in healthcare. The study suggests the opportunities of the individualised interventions, the improved screening of patients, and the evidenced based strategy of the population health. However, privacy of data, bias in algorithms, cybersecurity, interoperability and disproportionate access are the key challenges of implementation. The literature results indicate that integration of AI should be carried out through responsible approaches based on clinical validation, transparent algorithms, ethical governance and professional supervision. All in all, intelligent health technologies based on AI will have the capacity to facilitate more proactive, efficient, accessible and sustainable health systems provided they are implemented in a responsible direction and assessed using empirical evidence.