A Detailed Analysis of Many Different Artificial Intelligence-Based Healthcare Systems

Introduction

The healthcare industry has the opportunity for significant transformation through the application of Artificial Intelligence (AI), which can improve patient outcomes, reduce expenses, and enhance operational efficiency (DJ, B. J et.al., 2022). Several AI-based healthcare systems have been created to handle many areas of healthcare delivery, including diagnosis, treatment planning, and drug development (Zheng, L., 2022).

Figure 1.

Representation of model of medical internet of things

Artificial Intelligence (AI) in healthcare shows great promise, particularly in the field of medical diagnosis. Through the use of AI-based diagnostic algorithms, medical imaging like X-rays and CT scans can be analyzed to assist doctors in accurately identifying various diseases and conditions (Hilmizen, N et al., 2020). These algorithms, employing deep learning techniques, analyze images to identify patterns indicative of specific illnesses. Studies have demonstrated the reliability of such systems, which exhibit high sensitivity and specificity in diagnosing diseases like lung cancer and breast cancer. Moreover, AI diagnostic systems can also predict the progression of diseases, facilitating early detection and treatment planning (Chang, K. C et al., 2021). Additionally, AI finds application in treatment planning, as illustrated in Figure 1, which showcases a model of the Internet of Medical Things. On the basis of a patient's medical history, symptoms, and test findings, AI-based solutions have been developed to aid physicians in designing individualized treatment programmes. These systems examine data using machine learning techniques and find patterns that are suggestive of particular situations. These algorithms can accurately identify the optimal therapy options for patients with illnesses such as heart disease and diabetes, according to studies (Bernardini et al., 2021). In addition, the AI-based treatment planning system can be used to track the patient's development and modify the treatment plan accordingly. Moreover, AI has been utilized to increase the effectiveness of drug discovery. The development of AI-based algorithms to aid researchers in finding prospective medication candidates and forecasting their efficacy has occurred. These systems evaluate vast quantities of data on the molecular structure of medications, their interactions with the body, and their adverse effects using machine learning techniques. These systems can reliably estimate the efficacy of drug candidates and identify probable negative effects, allowing researers to create more effective and safer medications (Ghimire et al., 2020).

In the healthcare industry, AI-based healthcare solutions have showed considerable potential for improving patient outcomes, decreasing costs, and enhancing efficiency. These systems were designed to handle various facets of healthcare delivery, including diagnosis, treatment planning, and drug discovery (Firouz et al., 2021). Research have demonstrated that these systems are capable of properly identifying diseases, predicting the most effective treatments, and identifying promising drug candidates. However, it is equally essential to remember that AI-based systems should be used in conjunction with human expertise and should be continuously checked and assessed to guarantee that they produce accurate and trustworthy results. Using AI-based healthcare solutions can be advantageous for improving patient outcomes, decreasing expenses, and increasing industry efficiency. It is essential to stress, however, that the outcomes of AI-based systems must be confirmed by human specialists, and the systems must be continuously monitored and assessed to guarantee they produce accurate and dependable results (Balasubramani et al., 2022).