## 3 Classification Framework for Analysis
### AI and Machine Learning in Remote Patient Monitoring  
The world is currently expanding quickly as new technologies transform many industrial sectors. One of the many scientific tools that has a big impact on the healthcare sectoris AI. One of the divisions of e-health, which has expanded dramatically, is remote patient monitoring [148]. AI-powered remote patient monitoring is a highly effective technique for managing common-to-chronic diseases. In order for remote patient monitoring to function, data must be gathered and sent to healthcare professionals via a linked device. As a result, the majority of healthcare institutions have implemented remote patient monitoring, shifting the traditional method of treatment in that direction. Patients have demonstrated trust, and even for high-risk patients, issues, diagnoses, health improvements, and other patient data may be easily tracked [144]. Depending on the type of device used to get the necessary patient data, the remote patient monitoring procedure may vary. The majority of wireless sensors are used to gather data, which are then transferred to the cloud and other servers for remote patient monitoring. AI algorithms are used in the analysis to provide clinical decision making, which is subsequently communicated to healthcare practitioners. In difficult diagnoses, the patient and doctor may interact personally, communicate by notification, or receive expert input. In recent times, there have been various programs that offer a good user interface for viewing available prescriptions, tracking patient health information, displaying treatment/doctor/hospital suggestions, and sending notifications [144]. There is no shadow of hesitation that in the years to come, the use of AI in the diagnosis and treatment of diseases will be accepted and acknowledged on a widespread scale. The use of a variety of AI methodologies, both structured and unstructured, for the various forms of data is one of the primary reasons for its exceptional capacity for extensive adaptation. Because of this property, the scope for identifying the disease is significantly expanded. When a disease is quickly diagnosed, therapy can begin much sooner and at a lower cost. The quantity of data associated with healthcare is expanding at an astounding rate. The AI-based system aids in understanding the features of a vast volume of medical data that are essential for clinical procedures to be assisted by. In addition to this, these algorithms are equipped with the ability to self-correct, which enhances both their accuracy and efficiency on the basis of feedback. The system that is based on AI approaches can also help clinicians by producing the most up-to-date medical knowledge from various resources such as journals, textbooks, clinical practices, and so on, which will ultimately lead to more effective patient care. The tools of artificial intelligence are able to forecast human genomes and prescribe treatments that are suitable, allowing patients completely individualized care. During the process of developing various functions, they are linked with human intellect in the form of problem-solving capacities, learning, and reasoning, which ultimately results in an increase in the effectiveness and capabilities of medical experts. Rule-based structures, case-based deduction, ambiguous models, computational neural networks, cellular automated processes, algorithms based on genetics, swarm cognitive ability, multi-agent systems, hybrid systems, reinforcement learning, and so on are some examples of the various methodologies that can be utilized [149]. Nucleic acid testing is associated with high levels of specificity and sensitivity and plays an important role in the field of molecular diagnosis, particularly with regards to the diagnosis of infectious diseases, neoplastic diseases, cancer biomarkers, genetic mutations, and genotyping, while also facilitating food safety control and environmental monitoring.