**1. Introduction**

Artificial intelligence (AI) technique is the most effective technology used in the modern healthcare area. The rapidly growing accessibility of healthcare medical data and also the advances of big data diagnostic techniques has completed the potential of the current successful uses of artificial intelligence (AI) in healthcare system. With the help of important medical questions, potential artificial intelligence (AI) techniques can disengage healthcare-appropriate information secreted in the huge quantity of data, which can maintain healthcare decision-making. Modern healthcare technology in various medical areas has spread to the several pioneering startups in the world, which helps people in healthier and longer lives. The advances have initially been determined by the beginning of mobility and software, permitting the health sector to digitize several of the pen- and paper-based processes and operations that are presently held up service release. Nowadays, computer software has become far more intelligent and autonomous. These new abilities are discussed under the same cover of machine learning (ML) and artificial intelligence (AI), which are accelerating the tempo of improvement in healthcare. The applications

of machine learning (ML) and artificial intelligence (AI) in healthcare region have allowed the area to employ some of its major challenges in particular domains like drug discovery, personal genetics, and disease identification and management. Every time an innovative technical tool comes into the healthcare system, it also faces several challenges. Most of the common issues of artificial intelligence (AI) technique in healthcare system are regulatory compliance requirements, patient and provider adoption, and also lack of data exchange. The Artificial intelligence (AI) has moved from all of these concerns, reducing the areas in which it can accomplish something. The purpose of artificial intelligence (AI) and machine learning (ML) in healthcare system is redesigning the industry and creating what was once impracticable into a real truth. For artificial intelligence (AI)/machine learning (ML) to take its place in the healthcare system, sustained access to appropriate data is necessary to succeed. Artificial intelligence (AI) can be used to analyze and identify patterns in large and complex datasets faster and more precisely than has previously been possible. It can also be used to search the scientific literature for relevant studies and to combine different kinds of data, for example, to aid drug discovery. Artificial intelligence (AI) health apps have the potential to empower people to evaluate their own symptoms and care for themselves when possible. Artificial intelligence (AI) systems that aim to support people with chronic health conditions or disabilities could increase people's sense of dignity, independence, and quality of life, and enable people who may otherwise have been admitted to care institutions to stay at home for longer. Artificial intelligence (AI) depends on digital data, so inconsistencies in the availability and quality of data restrict the potential of artificial intelligence (AI). Also, significant computing power is required for the analysis of large and complex datasets. Clinical practice often involves complex judgments and abilities that artificial intelligence (AI) currently is unable to replicate, such as appropriate knowledge and the ability to read social cues. With the help of machine learning process, structured data like genetic data, electro physical data (EP), and imaging data are properly investigated. Machine learning makes the information analytical algorithms to extract characteristics from the input data. Input data generally in machine learning algorithms involve with patient's natures as well as the intermittently apprehension healing effects. A patient's nature generally includes bottom line data, such as gender, disease history, age, gene expressions, electrophysiological data (EP) test, analytical imaging, idea test results, and medicinal symptoms. Support vector machine was also applied in cancer diagnosis. Even supposing complicated data, machine learning represents the support for artificial intelligence (AI). At this moment in time, an innovative advancement is happening in the subfield of neural networks. This has created notable interest in various domains of healthcare science, in addition to drug analysis and also the area of public health. Deep neural networks can implement in addition to the most exceptional human clinicians in specific diagnostic tasks. Also, artificial intelligence techniques are already promising in healthcare-based apps, which can be performed by any network machine like modern smart mobile phone. Artificial intelligence has the ability to address imperative health challenges, but it is limited due to the unavailability of good health data. Employing artificial intelligence (AI) involves some ethical issues including the probable for artificial intelligence (AI) to make mistaken assessments and then the question of responsibility occurs.

### **2. Artificial intelligence (AI) devices**

Basically, artificial intelligence (AI) devices are categorized by two main types: the first one is machine learning (ML) category [1], which generally analyses the

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**Figure 1.**

*and machine learning (ML) data investigation.*

*Application of Artificial Intelligence in Modern Healthcare System*

structured data, for example, electrophysiological data (EP), genetic data, and imaging data. For healthcare applications, the machine learning (ML) processes try to gather patients' individuality or understand the possibility of the disease effects [2]. The second type of artificial intelligence (AI) device is the natural language processing (NLP) technique [3], which can take out the information from free or unstructured data such as medical observations or health journals to enhance structured health check data. The natural language processing (NLP) processes objects at revolving contents toward the machine-understandable structured records and can then be considered by machine learning (ML) procedures [4]. **Figure 1** explains the road plan from medical data making, during natural language processing (NLP) data improvement and machine learning (ML) data investigation, to medical judgment creating. In this figure, the road plan starts and ends with medical activities. As dominant as artificial intelligence (AI) procedures, they can be inspired by medical/healthcare troubles and also be practical to help out the

Machine learning (ML) builds the data investigative algorithms to extort characteristics from the data. Inputs to machine learning (ML) algorithms consist of patient 'characters' and occasionally therapeutic effects of concern. A patient's characters generally contain bottom line data, for example, gender, age, disease history, and also disease explicit data, for instance, gene expressions, analytical imaging, electrophysiological data (EP) test, objective test results, medication, and medical symptoms. In addition to the attributes of the patients medical results are frequently composed for medical investigation. These contain syndrome pointers, patients' endurance periods, and quantitative syndrome stages such as the size of

is representing the effect of concern. Regarding whether to integrate the results, machine learning (ML) algorithms can also be separated into two main types: supervised learning and unsupervised learning. One more type is also available

*The road plan from generation of medical data, during natural language processing (NLP) data improvement* 

th numbers of patient is denoted by *Pij* and *Qi*

*DOI: http://dx.doi.org/10.5772/intechopen.90454*

medical performance at the end.

tumor. Here *j*

**2.1 Machine learning (ML) processes**

th characteristic of the *i*

#### *Application of Artificial Intelligence in Modern Healthcare System DOI: http://dx.doi.org/10.5772/intechopen.90454*

*Alginates - Recent Uses of This Natural Polymer*

of machine learning (ML) and artificial intelligence (AI) in healthcare region have allowed the area to employ some of its major challenges in particular domains like drug discovery, personal genetics, and disease identification and management. Every time an innovative technical tool comes into the healthcare system, it also faces several challenges. Most of the common issues of artificial intelligence (AI) technique in healthcare system are regulatory compliance requirements, patient and provider adoption, and also lack of data exchange. The Artificial intelligence (AI) has moved from all of these concerns, reducing the areas in which it can accomplish something. The purpose of artificial intelligence (AI) and machine learning (ML) in healthcare system is redesigning the industry and creating what was once impracticable into a real truth. For artificial intelligence (AI)/machine learning (ML) to take its place in the healthcare system, sustained access to appropriate data is necessary to succeed. Artificial intelligence (AI) can be used to analyze and identify patterns in large and complex datasets faster and more precisely than has previously been possible. It can also be used to search the scientific literature for relevant studies and to combine different kinds of data, for example, to aid drug discovery. Artificial intelligence (AI) health apps have the potential to empower people to evaluate their own symptoms and care for themselves when possible. Artificial intelligence (AI) systems that aim to support people with chronic health conditions or disabilities could increase people's sense of dignity, independence, and quality of life, and enable people who may otherwise have been admitted to care institutions to stay at home for longer. Artificial intelligence (AI) depends on digital data, so inconsistencies in the availability and quality of data restrict the potential of artificial intelligence (AI). Also, significant computing power is required for the analysis of large and complex datasets. Clinical practice often involves complex judgments and abilities that artificial intelligence (AI) currently is unable to replicate, such as appropriate knowledge and the ability to read social cues. With the help of machine learning process, structured data like genetic data, electro physical data (EP), and imaging data are properly investigated. Machine learning makes the information analytical algorithms to extract characteristics from the input data. Input data generally in machine learning algorithms involve with patient's natures as well as the intermittently apprehension healing effects. A patient's nature generally includes bottom line data, such as gender, disease history, age, gene expressions, electrophysiological data (EP) test, analytical imaging, idea test results, and medicinal symptoms. Support vector machine was also applied in cancer diagnosis. Even supposing complicated data, machine learning represents the support for artificial intelligence (AI). At this moment in time, an innovative advancement is happening in the subfield of neural networks. This has created notable interest in various domains of healthcare science, in addition to drug analysis and also the area of public health. Deep neural networks can implement in addition to the most exceptional human clinicians in specific diagnostic tasks. Also, artificial intelligence techniques are already promising in healthcare-based apps, which can be performed by any network machine like modern smart mobile phone. Artificial intelligence has the ability to address imperative health challenges, but it is limited due to the unavailability of good health data. Employing artificial intelligence (AI) involves some ethical issues including the probable for artificial intelligence (AI) to make mistaken assessments

**122**

and then the question of responsibility occurs.

Basically, artificial intelligence (AI) devices are categorized by two main types: the first one is machine learning (ML) category [1], which generally analyses the

**2. Artificial intelligence (AI) devices**

structured data, for example, electrophysiological data (EP), genetic data, and imaging data. For healthcare applications, the machine learning (ML) processes try to gather patients' individuality or understand the possibility of the disease effects [2]. The second type of artificial intelligence (AI) device is the natural language processing (NLP) technique [3], which can take out the information from free or unstructured data such as medical observations or health journals to enhance structured health check data. The natural language processing (NLP) processes objects at revolving contents toward the machine-understandable structured records and can then be considered by machine learning (ML) procedures [4]. **Figure 1** explains the road plan from medical data making, during natural language processing (NLP) data improvement and machine learning (ML) data investigation, to medical judgment creating. In this figure, the road plan starts and ends with medical activities. As dominant as artificial intelligence (AI) procedures, they can be inspired by medical/healthcare troubles and also be practical to help out the medical performance at the end.
