Abstract
The volume and complexity of big data in healthcare have created opportunities to develop new analytical tools and methods. Artificial intelligence (AI) is increasingly being used to create new technologies that will transform patient care, administrative and delivery processes, and pharmaceutical treatments. Continuous patient monitoring is now possible for any health condition, including patients with diabetes, Parkinson's, and cardiovascular diseases. AI appears to be promising in terms of effectively saving clinicians' time, improving diagnostic and prognosis procedures, and organising and managing healthcare. However, there are serious concerns about the safety and security of electronic health records and patient information. Therefore, AI software engineers and businesses must address a series of issues, including the validation, reliability, and accuracy of machine learning procedures, the collection of big data via the internet of things, data security, privacy breaches, and encryption.
TopBackground
Biomedical research and healthcare systems are gradually changing because of breakthrough technologies and software programs. The creation of AI in health aims to assist clinical experts in identifying the nature of diseases and distinguishing them from other overlapping illnesses. As a result, AI is emerging as a significant contributor to diagnostics and monitoring of disease progression. An AI technology relies upon algorithms to identify patterns in large data sets and is either a conventional or integrative decision support system based on whether it uses electronic patient records to make a decision (Yu et al., 2018). Even though AI technologies have primarily relied upon correct decision-making via machine learning methodologies, this branch of computer science focuses on authorisation updates to function appropriately (Yu et al., 2018). These processes eliminate most mistakes made in healthcare systems and increase the software’s memory with layers of knowledge (Yu et al., 2018). In addition, an AI system can assist clinicians with updated medical information from books, journals, and clinical practice guides about patient care and medical needs (Jiang et al., 2017). However, AI in healthcare applications may encounter difficulties differentiating between chronic medical conditions, clinical signs and symptoms, screening, and diagnostics in old and elderly clinical groups (A. Choudhury et al., 2020; Jiang et al., 2017).
Key Terms in this Chapter
Diabetes: Is a disease that affects insulin production in the pancreas and causes clinical signs and symptoms in the patients.
Internet of Things: refers to physical objects such as sensors, software and online networks that collect data.
Artificial Intelligence: refers to human-like machine intelligence capable of learning, problem-solving and decision making.
Mental Health Disorders: This is a term to declare a wide range of mental state issues manifested in behavioural, psychological, emotional and social well-being disturbances that affect individuals’ daily lives.
Cardiovascular Disease: Is a term that describes medical health conditions related to impairment in heart and blood vessel function.
Parkinson’s Disease: Is a progressive nervous system disorder characterised by loss of neurons that produce dopamine in the brain, abnormal brain activity and deficits in body movements.
Big Data: Are large and complex data sets used for product development, health management, customer experience and predictive techniques.