Artificial Intelligence and Reliability Metrics in Medical Image Analysis

Artificial Intelligence and Reliability Metrics in Medical Image Analysis

Yamini G. (Bharathidasan University, India) and Gopinath Ganapathy (Bharathidasan University, India)
DOI: 10.4018/978-1-7998-3591-2.ch011
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Abstract

Artificial intelligence (AI) in medical imaging is one of the most innovative healthcare applications. The work is mainly concentrated on certain regions of the human body that include neuroradiology, cardiovascular, abdomen, lung/thorax, breast, musculoskeletal injuries, etc. A perspective skill could be obtained from the increased amount of data and a range of possible options could be obtained from the AI though they are difficult to detect with the human eye. Experts, who occupy as a spearhead in the field of medicine in the digital era, could gather the information of the AI into healthcare. But the field of radiology includes many considerations such as diagnostic communication, medical judgment, policymaking, quality assurance, considering patient desire and values, etc. Through AI, doctors could easily gain the multidisciplinary clinical platform with more efficiency and execute the value-added task.
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Introduction

AI plays a vital role in the field of medical imaging systems that deal with the interpretation, image processing, data mining, data storage, diagnosis, image acquisition and many more applications. Several techniques are involved in the field of AI, which refers to the field of computer science that makes the system to perform the task without the necessity of human intelligence by the integration of several technologies. A sub-branch of AI is said to be the Machine Learning (ML) technology, which involves the system to learn from the occurred data that does not need any explicit programming applied in the field of imaging. An example of AI stages undergone throughout the entire medical imaging system is shown in figure1.

Figure 1.

Stages underwent through the medical imaging process through Artificial Intelligence

978-1-7998-3591-2.ch011.f01

Certain industries should learn from certain platforms that follow their rule in order to maintain their person’s safety (e.g. Aviation). Therefore, continuous improvements should be met by the organizations, which are found to be the key for maintaining the reliability in healthcare. This roadmap is followed and being experimented with the Stanford University Hospital, Thedacare and Stanford Children’s. This manages and leads several ways by applying several principles and tools, which were operated in the field of healthcare manufacturing process. This relies on dealing with everyday problem by relying the development of frontline staff and then the frontline worker gets connected with their purpose of the organization(Sharma et al. 2018).

Highly reliable playbook had been designed as a principle that becomes an outcome for the field of Indian health care organizations with the quality and the cost problems in the clinic and in the hospitals. At last, the cost effective with the safest system could be the result for every person. The question arises whether the health care system could be more reliable than the airline industry? In recent years, stakeholders, payers, providers and healthcare consumers have demanded better business outcomes and patients care by achieving better reliable performances and organization status. This industry should catch with other consumer-based industries and invest with several efforts and resources that could maintain a solid track to get operated with the reliable organization model to enhance the patient’s care outcomes and business result performance as shown in table 1.

Table 1.
High Reliability principles for Healthcare
Important principlesCare takers behaviourExamples
Failed preoccupationsAttitudePhysicians and other health care professionals marking the correct surgical site
Operational sensitivitySystem based value practicesMaintaining a good record of the team with the incoming and outgoing information and their present situation status to enhance the accuracy of the team
Simplification disinclinationMeta-cognitive skillsPatients admitted at the critical situation and the fellow residents should know their roles and responsibilities
Determination for the organizationEmotional Intelligence and assertionNurses have the right to promote their advice to the physician regarding their allergies or any other physical information, which they might have known before.
Respect the relevant and the qualified expertsCompetency skills and leadershipPatient’s healthcare should be monitored at the regular basis by the nurse who had been promoted to monitor their regular activities.

Key Terms in this Chapter

Rational Decision Making: Rational decision making is said to be a crucial process unlike several other sectors, which offers certain provisions to the community.

Ambient Assisted Living: AAL provides an effective IoT platform governed by artificial intelligence algorithms, thereby satisfying the reliability metric in monitoring patient’s health in their place of living in a safe manner. The AAL system includes activity monitoring of patients which is important for patient suffering from Alzheimer’s disease, bedsore, diabetes, and osteoarthritis.

Intelligent Information System: An intelligent information system is said to be the set of software and hardware that involves the skilled people for the process of decision making and co-ordination among the organization.

Machine Learning: A sub-branch of AI is said to be the Machine Learning (ML) technology, which involves the system to learn from the occurred data that does not need any explicit programming applied in the field of imaging.

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