Abstract
Medical technology powered by artificial intelligence is quickly developing into useful clinical practice solutions. Deep learning algorithms can handle the growing volumes of data produced by mobile monitoring sensors found in wearables, smartphones, and other medical devices. Currently, only a very limited number of clinical practice settings, such as the detection of atrial fibrillation, epilepsy seizures, and hypoglycemia, or the diagnosis of disease based on histopathological examination or medical imaging, benefit from the application of artificial intelligence. Patients have been waiting for the deployment of augmented medicine since it gives them more autonomy and more individualized care, but doctors have been resistant because they weren't ready for such a change in clinical practice. The purpose of this study is to glance over recent scientific material and offer a perspective on the advantages, potential benefits, and potential concerns of established artificial intelligence applications in the modern healthcare sector.
Top1. Introduction
The core of evidence-based medicine is using historical data to inform current treatment decisions (Buch et al., 2018). By characterizing data patterns as mathematical equations, statistical methods have traditionally handled this issue. For instance, linear regression offers a “line of best fit.” AI offers methods for ‘machine learning' (ML) that reveal complicated correlations that are difficult to sum up in an equation (Datta et al., 2019). In a manner analogous to the human brain, neural networks, for instance, encode data using a massive number of interconnected neurons. As a result, ML systems can approach difficult problem solving in the same way that a doctor could by carefully analyzing the available data and drawing valid judgments (Barua et al., 2023). These systems, as opposed to a single physician, may observe and process a virtually infinite amount of inputs concurrently. These algorithms can also learn from each incremental case and can be exposed to more cases in a short period of time than a physician could ever view in their whole career (Barua et al., 2022). This explains how an AI-driven tool can diagnose suspicious skin lesions more accurately than dermatologists’ can (Esteva et al., 2017), or why AI is trusted to handle jobs where specialists frequently disagree, such categorizing pulmonary tuberculosis on chest radiographs (Lakhani et al., 2017). Despite the fact that AI is a broad area, this article only focuses on ML approaches due to their widespread use in significant biomedical and clinical applications (Barua et al., 2022). The most widespread applications of AI and ML in medicine have been in the disciplines of radiology, dermatology, cardiology, and mental health (Sabry et al., 2022). Figure 1 shows the AI used in various modern healthcare system. Given their effectiveness in certain domains of medicine, where they can occasionally surpass human doctors, AI and ML are attracting more and more attention (Barua et al., 2022).
Figure 1. Application of AI in various healthcare sectors
Key Terms in this Chapter
Artificial Intelligence: AI stands for “Artificial Intelligence,” which refers to the ability of machines and computer programs to perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and natural language processing.
Digital Twins: Virtual replicas of physical objects, processes, or systems that are created using digital technologies, such as computer-aided design (CAD), simulation, and data analytics. In the context of engineering and manufacturing, digital twins are used to model and simulate products, equipment, and manufacturing processes, allowing for optimization, prediction, and analysis of performance and behavior.
Medical Devices: Medical devices are instruments, machines, implants, or other similar products used in the diagnosis, treatment, monitoring, or prevention of diseases and other medical conditions. Medical devices can range from simple devices, such as thermometers or blood glucose meters, to complex ones, such as MRI machines or robotic surgical systems.
Deep Learning: Basically a subset of machine learning, which is a branch of artificial intelligence. It involves training artificial neural networks with large datasets to identify patterns and make predictions or decisions based on the input data.
Healthcare: The healthcare industry involves numerous professionals, such as doctors, nurses, pharmacists, therapists, and technicians, as well as organizations, such as hospitals, clinics, and insurance companies that provide and manage healthcare services.
Machine Learning: Machine learning is essentially the analysis of computer algorithms that automatically correct themselves through repetition. It is also referred to as an AI application.