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Artificial Intelligence in Healthcare and Its Application in Brain Stroke Diagnosis
Published in Rishabha Malviya, Pramod Kumar Sharma, Sonali Sundram, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Bioinformatics Tools and Big Data Analytics for Patient Care, 2023
Ambarish Kumar Sinha, Gaurav Kumar
In 2018, the global AI market was worth approximately $20.7 billion; however, it is expected to reach over $200 billion by 2026 [8]. As PricewaterhouseCoopers (PwC) estimates, by 2030, AI is going to add more than $15.7 trillion to the global economy [9]. As per a Fortune Insights forecast, the North American AI market is set to grow at 33.1 percent compound annual growth rate (CAGR) to 2026 with global AI revenues set to increase 12-fold to $118 billion [10] in the North American market. Growth will be driven by: increased prevalence of vascular diseases, rising diagnoses, and treatment rates with the wider use of imaging technologies; increased patient affordability; better access to vascular therapies; and continued innovation leading to new product launches [11]. Business process automation such as robotic process automation, natural language processing, and ML are going to be the major areas that will witness major investment and growth. Other growth drivers will be the utilization of big data and the use of robotics in manufacturing processes, the Internet of Things (IoT), inter-industry collaboration, and increased capital investment. According to another report, AI’s growth in the manufacturing sector will be driven by bigger players on the market such as IBM, Oracle, Microsoft, NVIDIA, and Intel. The report also states that the AI manufacturing sector will be dominated by North America until 2024; however, the Asia Pacific region will be the fastest-growing due to continued investment in AI-enabled solutions [12] (Figure 6.1).
Conceptualizing and Defining Digitalization
Published in Sergey V. Samoilenko, Digitalization, 2023
First, there is pretty much a consensus that digitalization works on different levels. For example, it can function within a stratum of: Society, where digitalization could allow for changes in socio-economic structures and arrangements. This is done by elimination and creation of jobs and job types, and the introduction of new (and elimination of old) decision-making structures. E-government initiatives offer an illustrative example of digitalization functioning at the level of society.Industry, where digitalization is capable of re-shuffling the existing hierarchies, eliminating or restructuring old, and creating new value nets and value chains. Primary tools of change are new digital interfaces and information channels that allow for disposing the less effective and efficient ones. Extranets and digitalized supply chains are examples of digitalization working at the level of the industry, or even of cross-industries.Organization, where digitalization enables the offering of new products/services in novel ways while eliminating the less effective and efficient practices. Such initiatives as Business Process Re-engineering and the introduction of Enterprise Systems are good examples of digitalization operating at the level of a firm.Process, where digitalization is responsible for automating the previously “analog” sub-processes and task via the adaptation of new digital tools and technologies. A prime example of such impact is a successful Business Process Automation initiative that results in the improvements in the process effectiveness, efficiency, and safety.
Robotic process automation - a systematic mapping study and classification framework
Published in Enterprise Information Systems, 2023
Judith Wewerka, Manfred Reichert
In our continuously changing world, it is indispensable that business processes are highly adaptive (Reichert and Weber 2012) and become more efficient and cost-effective (Lohrmann and Reichert 2016). As a consequence, companies demand for an increasing degree of business process automation to stay competitive in their markets. In this context, business process management (BPM) plays a crucial role in the digital lifecycle support of business processes involving multiple participants and software systems (Weber, Sadiq, and Reichert 2009). Currently, BPM is enhanced by the use of process mining and Robotic Process Automation (RPA). While the former gives companies objective and data-driven insights into the actual flow of their business processes (van der Aalst 2011), the latter describes software robots (bots for short) mimicking human interaction (Asatiani and Penttinen 2016). RPA constitutes a ‘highly promising approach’ (Cewe, Koch, and Mertens 2017) and more and more companies rely on this cutting edge technology (Asatiani and Penttinen 2016) to optimise, implement, and automate selected process tasks.