phd research proposal example

phd research proposal example

The Impact of Artificial Intelligence in Healthcare

1. Introduction

The country’s healthcare system is being confronted by an unprecedented increase in the scope and scale of data. This data growth is driven by rapidly evolving technologies, regulations that require the capture of more data, and the broad movement to improve quality and efficiency. While the availability of data presents an enormous opportunity to advance clinical care and quality of life, the ability of the healthcare system to actually use this data is at risk. The past decade has seen a growing awareness of the gap between the knowledge that can be derived from the data and the ability to actually implement that knowledge to improve the safety, quality, and efficiency of patient care. This is where Artificial Intelligence (AI) can play a key role in healthcare. AI can have a positive impact on healthcare. The adoption of AI in healthcare is on the cusp of a major transformation. In the not so distant future, healthcare delivery and AI will be synonymous. Health data is growing at a 48% growth rate annually and is predicted to grow even faster with the onset of increased usage of personal health devices and sensors. According to IBM, the data volume is doubling every 73 days. By the time a piece of health-related research is published and put into practice, the information is outdated. High quality, real-time data is what will make AI systems successful. The strongest argument for AI in healthcare is the potential to provide significant clinical decision support. Did you know that up to 30% of the world’s health data is “unstructured”? It is estimated that $300 billion is spent on storing and deciphering this unstructured data. The proposed Watson system is designed to understand, correlate, and evaluate this unstructured data to then generate a hypothesis. Primordial approach will provide insights to the healthcare provider that would have taken them a lifetime to comprehend, offering concise and concrete information, promoting evidence-based decision-making.

2. Literature Review

In conclusion, we will evaluate the potential effects of AI in healthcare, both positive and negative, and make recommendations for future research.

Next, we will conduct a review of the opportunities to apply AI in healthcare, with a focus on recent developments in IT and the care of older people. This review will require careful consideration of the current limitations and contradictions in AI programs. The literature reviewed will range from academic sources to mainstream medical journals.

The properties of fitness and the theory behind intelligent programs can be found in the well-known work by McCulloch et al. Typically, healthcare AI programs have been limited in their function and efficacy. However, this is soon to change as modern computer hardware advances. There are already AI programs that outperform humans in certain simulations.

In this work, we examine how artificial intelligence (AI) can improve patient outcomes, lower costs, or accelerate results compared to existing methods. We also identify potential drawbacks of applying AI. AI is defined as the science and engineering of making intelligent computer programs. An intelligent program is a flexible program that can learn from past experiences, adjust to new inputs, and perform tasks similar to humans.

3. Methodology

We conducted a review of the literature over the past few months and gained a better understanding of where AI in medicine draws its influence. In many cases, articles were informative with background information, but lacked the depth needed in assessing the actual impact. Initially, we had to compile all relevant information and then refine the sources to show only the most pertinent. At this point, it was easier to discern useful information from irrelevant data and draw our own conclusions on the topic’s features. Information in this step was mostly comprised of early-twentieth century court cases and first-person medical studies to establish the framework and function of healthcare as a work industry. Later information concerning AI and medical data was easier to compile and we were able to establish a more logical pattern. Data from the 70s and 80s began to show an actual shift in the machines being utilized with the establishment of computerized axial tomography and the MRI machine. More recent than historical data, our group was able to find information from first-person accounts and studies on the usage of AI in diagnosis and treatment formulation. With a clear transition of medical techniques to data manipulation, we were able to locate numerous articles with an in-depth analysis on how AI is utilized to process medical information. Finally, during our later weeks, our group was able to find information on the impact of AI in medical data from sources such as medical journals and medical news. This step would later be essential in understanding the overall impact of AI on medicine both in the present and future.

4. Findings and Analysis

But how do we know that AI can model quality care, and in what manner does it reach the same level of care at a lower cost? AI can be a great tool in that it can provide current evidence on a treatment’s relative benefit and potential substitutes of cheaper cost. This is exactly the current goal of comparative effectiveness research. An example of AI doing this would be another study on the treatment of a chronic disease using simulation. In every medical condition, there are best evidence-based practices to improving health status. The study created a strategy refinements tool to find alternative strategies with equal effectiveness and less cost. The AI simulation first defined a strategy as the mixture of diagnostic and treatment choices over time, then ranked the strategies by their expected utility to objective measures of patient health status. If a strategy’s expected utility was less than the best strategy, it would then define a new strategy by making small changes and continuously do so towards strategies of higher utility. The research found that this method was able to save costs without sacrificing health compared to traditional clinical practice.

Unfortunately, when using traditional econometric methods of predicting the increase in spending over time due to increased prevalence and increased utilization per patient, the results were not adjustable to the current status of healthcare in the U.S. and were too broad to implement specific changes to lower costs. This was when they created a micro-simulation to model the progression of treatment and cost at the individual provider level. The simulation created artificial patients, each having varied characteristics of illness that would change over time. These characteristics were defined as age, gender, type and severity of disease, and existing prescription medications.

The field for utilizing AI in healthcare is enormous, but research on its use in cutting costs for the healthcare industry is first rate. We should establish that we have already accepted the necessity of AI in healthcare, but whether or not it can be an effective tool in cutting healthcare spending is the next big leap. A recent study by Health Affairs came at the topic by researching the cost of caring for patients with chronic disorders in the United States. The topics discussed within chronic disorder included rheumatoid arthritis, coronary artery disease, chronic obstructive pulmonary disease, and high cost multiple chronic disorders.

5. Conclusion and Recommendations

Overview of Impact AI has in Healthcare Physician Error AI Based Prediction and Treatment Suggestion Personalized treatment and Behavioral modification Administrative work and cost of Healthcare AI for better Healthcare of Future As we have seen in the six substantial portions of this essay, AI has made a huge positive impact in the field of healthcare. With the potentially enormous benefits of AI, it is clear that the integral role for AI will continue to evolve and grow in the healthcare system and throughout its many subfields. AI can be used with its array of technologies to attempt to solve these problems and provide improvements creating an optimized healthcare system. This will be accomplished implementing the current care continuum, that is to deliver optimal medical care across the globe, and channel additional funds to foster research and development of AI. AI applied across global healthcare systems can ensure best practices are followed and delivering significant and safe medical treatment. AI could automate and administer repeatable tasks requiring an increasing amount of data in order to reduce the cognitive load of human physicians, it could be a virtual assistant for personal health maintenance, or aid in a clinical setting and monitoring/tracking disease. AI has the potential to provide both artificial general intelligence and ANI health care solutions to make strides on the path of full coverage in accomplishing maintenance of human health. Despite the current relatively small penetration of AI in healthcare, and at times the overhype and underdelivery, it has the potential to provide disproportionate benefits to the health of society. AI is poised to affect the quality, efficacy, and delivery of healthcare across the globe, especially as the role of AI technologies become integral in everyday life. It is important to realize that AI is not a panacea, and the current trajectory of AI development is not guaranteed to be net positive. AI disrupts and displaces, and at times these components have negative consequences. Society and the healthcare industry must be agile and have specific strategies to mitigate undesirable changes and to ensure development and use of AI produce net benefits to all.

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