Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and can lead to severe complications such as stroke. Artificial intelligence (AI) has emerged as a vital tool in predicting and detecting AF, with machine learning (ML) models trained on electrocardiogram (ECG) data now capable of identifying high-risk patients or predicting the imminent onset of AF. Precision medicine aims to tailor medical interventions for specific sub-populations of patients who are most likely to benefit, utilizing large genomic datasets. Genetic studies have identified numerous loci associated with AF, yet translating this knowledge into clinical practice remains challenging. This paper explores the potential of AI in precision medicine for AF and examines its advantages, particularly when integrated with or compared to genomics. AI-driven ECG analysis provides a practical and cost-effective method for early detection and personalized treatment, complementing genomic approaches. AI-based diagnosis of AF allows for near-certain prediction, effectively relieving cardiologists of this task. In the context of preventive identification, AI enhances the accuracy of predictive models from 75% to 85% when ML is employed. In predicting the exact onset of AF—where human capability is virtually nonexistent—AI achieves a 74% accuracy rate, offering significant added value. The primary advantage of utilizing ECGs over genomic data lies in their ability to capture lifetime variations in a patient’s cardiac activity. AI-driven analysis of ECGs enables dynamic risk assessment and personalized adaptation of therapeutic strategies, optimizing patient outcomes. Genomics, on the other hand, enables the personalization of care for each patient. By integrating AI with ECG and genomic data, truly individualized care becomes achievable, surpassing the limitations of the “average patient” model.

This research provided an in-depth analysis of endoscopic capsules as an innovative application of the Internet of Things (IoT) in healthcare. The study revealed the importance of these systems in advancing gastrointestinal diagnostics due to their non-invasive nature and ability to provide comprehensive internal imaging. The work systematically investigated the device’s technical design, power management strategies, communication protocols, and how it performs its secure and efficient operations. Findings from this analysis highlighted the transformative impact of these capsules despite current constraints, such as battery limitations and procedural costs. Ultimately, this wide review confirmed that endoscopic capsules redefine medical diagnostics, fusing patient comfort with innovative technology. Moreover, as developments continue, these devices have promising potential to shape the future of intelligent, interconnected healthcare solutions.