We worked together on my research which will be attached in the attachments.
I would like you to make me a PowerPoint presentation  containing the research details like the introduction, the objectives, references, and the prisma and other details.
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Master of Healthcare Administration
HCM 600 Research Project
The Challenges and Opportunities of Implementing Artificial Intelligence in Prehospital
Emergency Medical Services: A Systematic Review Study
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Chapter One
Introduction
Integrating AI in frontline emergency medical services (EMS) is an innovative advancement that is, at the same time, a cornerstone of the healthcare industry’s latest evolution. A pivotal integration, with prospective breakthroughs never seen before in emergency medical treatment, will be heralded as it immerses the world into a new dimension of relief. The application of AI innovations in the very EMS structure where the most important thing happens promises to introduce new levels of diagnostic precision, therapeutic interventions, and patient outcomes (Fernandes et al., 2020). Such a revolutionary change allows for viewing the tech area as a driving power behind the future evolution of emergency medical services, holding it up as the benchmark that all should follow. The welding of AI and prehospital EMS is conversely shape-shifting the speed and accuracy of decision-making in critical situations while giving birth to a banner of innovative concepts set to further optimize multiple spheres of emergency care (Piliuk & Tomforde, 2023). This means that the AI-driven EMS is not only a breakthrough in using sophisticated technology to provide improved services but also highlights a spirit of innovation, dedication, and determination to revolutionize everything in healthcare and whose primary goal is to optimize results and productivity in urgent medical situations.
1.1 Background of the Study
The emergence of AI amidst the ever-changing landscape of healthcare has signified a revolution that has the potential to significantly transform the very basics of diagnosis and treatment in addition to overall patient care (Kirubarajan et al., 2020). This research is intimately interwoven with the fabric of recognizing an inconvenient fact- the integration of AI into the 3 complex network of prehospital emergency medical services (EMS) is a gaping hole not sufficiently explored, which seems to undermine its immense capability to exponentially augment the efficiency and effectiveness of delivery of emergency medical care. The figures talk
a lot, as these show that only a part, about 20%, of prehospital EMS systems that had integrated
AI into their services is a stark illustration of the gap this research seeks to close (Cimino &
Braun, 2023). Based on this thorough understanding of the paramount role of the prehospital
phase in coordinating seamless and impactful medical processes, our research orientations arises
as a strategic bridge, carefully crafted to fill up the current gap on the part of AI technologies and
lead to the enhancement of a broad spectrum of emergency medical protocols. A dissection of
the statistical context articulates that places with higher AI uptake in prehospital EMS benefit
from a significant reduction of 15% in response times, validating the practicality of AI
amalgamation into critical care delivery (Tang et al., 2021). In addition, the study coincides with
the global trend that depicts cubic growth in the employment of AI across different healthcare
sectors, which has drawn attention and highlighted the need to explore EMS, prehospital area, as
a new direction where AI could be used even more and contribute to the change that leads to
more effective, data-driven, and patient-oriented emergency medical services.
1.2 Problem Statement
The healthcare industry is experiencing a significant increase in the applications of AI,
yet the specific application of AI focused on prehospital EMS needs to be better understood.
There needs to be more attention to the critical step, which is the implementation of AI during
healthcare temporarily until the potential of AI can be fully accessed, which restricts AI to
improve patient outcomes during emergencies (Clark & Severn, 2023). The research was