phd research proposal

phd research proposal

The Impact of Artificial Intelligence on Healthcare

1. Introduction

AI is defined as the capacity to do things that would need intelligence if done by men. Instead of brilliant human thought and action, artificial intelligence has simulated human thought and action. Every aspect of AI, from machine to deep learning, has led to drastic changes in technology over time. AI has become a huge agent of change in healthcare, perhaps leading to a drastic alteration of what it currently means to see a doctor. Machine learning is an AI application that has been very useful in healthcare. A popular machine learning application is predictive analytics. Predictive analytics is a process to determine potential future outcomes by analyzing patterns in an already existing database. An example of predictive analytics is using given patient information such as age, sex, BMI, and blood work to determine the likelihood of developing a certain disease. This information can help determine the best course of action. Predictive analytics can also be used to identify potentially high-risk patients such as a patient likely to be readmitted to a hospital within 30 days or a patient with a high likelihood of developing a hospital-acquired infection. This information allows for resources to be focused on patients who would be expected to have suboptimal outcomes, thus preventing higher costs and decreased patient well-being. A study conducted by a PhD of Harvard claims that predictive analytics can save a hospital around $25,000 per sepsis patient treated and up to $30,000 for a pneumonia patient, with the possibility of saving many lives. This just goes to show how a relatively small use of AI can help bring large-scale change to healthcare outcomes. It is important to weigh the implications and outcomes of such a drastic change to the healthcare industry before fully embracing AI. While AI likely has the potential to improve many healthcare practices, there are also potential downsides and not all are without controversy. With the world of machine learning and AI, there comes trepidation about the potential to substitute in machine for man in certain tasks. This could lead to implications in taking away jobs and perhaps limitations on the true healing ability of human medical practitioners. In time, AI may work so efficiently to reduce morbidity and mortality that we have fewer patients to care for overall. AI could lead to limitations on training or difficulty for medical students to shadow real cases of disease. Predictive analytics could develop such accurate models to determine patient disease severity that patients are sorted into comprehensive and unchangeable treatment plans based on their predicted outcomes. This ever-growing world of machine into healthcare may lead to a point of no return or turning back, and it is important to determine how much machine is too much for the sake of patient well-being to ensure a better future for both patients and healthcare workers with minimal potential unintended consequences.

2. Literature Review

Major advances in AI have opened up a multitude of avenues for revolutionary change in healthcare. The integration of AI into healthcare is underway and is proving to be potentially life-saving. It is a widely known fact that the demand for healthcare services will increase as a result of the aging population and the global increase in chronic diseases. It is also no secret that our current global healthcare system is in need of radical change. On a global scale, access to adequate healthcare varies greatly. Many are denied access to healthcare due to a lack of health coverage and availability of services. Others may receive medical care that is fragmented and non-evidence-based. In short, global healthcare is in desperate need of an overhaul. AI may be the solution. By far the largest impact of AI will be felt in the ability to provide “precision medicine”. The potential of AI and, in particular, machine learning, to analyze complex medical data and provide a recommended action has been a much sought-after holy grail of health IT. In many ways, machine learning is ideally suited to the field. Medicine is highly complex with many influencing factors on a single parameter of health. Current best evidence is often not clear for the best course of action for a given disease. Lastly, the rate of growth in medical knowledge and care guidelines is so fast that it is difficult for the human mind to keep up. All of these conditions are ideal for machine learning. A world in which machine learning is taking vast amounts of medical data and providing guidance on treatment plans is not so far off. This would greatly enhance the physician’s ability to sort through today’s deluge of medical information and provide the best possible treatment available.

3. Research Methodology

To find the evidence to back these claims, the most suitable methodology to acquire this information is to read published articles online and studies. We will filter out the irrelevant information and select the most recent findings on AI technologies in healthcare from medical journals and online publications. This wide range of information will cover the different types of AI in healthcare, ranging from simple data processing to more complex technologies. Due to the prevalence of the internet and technology, there is an abundance of sources to obtain quality information. This helps with the validity of the information because it can be compared between numerous sources.

Lacking proactive research on artificial intelligence in healthcare, this paper specifically seeks to find out the evident consequences that AI will have on the medical field. The research done on this topic is a plan to prove what impact AI will have on healthcare as it evolves, and what it will bring to the table when comparing to the human touch. AI has already been implemented into numerous other industries, and healthcare is one of the many on the list. Current studies have shown AI’s long-term potential to analyze complex medical data and deliver precision medicine. It’s also been proposed that utilizing AI technologies will help free up healthcare providers’ time from electronic health record clerical work to having more patient-doctor engagement (Jiang F. et al.). AI can also provide patients with virtual health assistants that can simulate human interactions.

4. Findings and Analysis

In the last decade, there has been rapid advancement in the field of AI. As a result, several applications of AI have been proposed and are becoming a reality. One of the most significant areas in which these applications are taking place is healthcare. Although it might sound futuristic, there are several AI implementations in healthcare that are currently being used or developed. The purpose of this paper is to explore some of these AI applications in healthcare and discuss the potential benefits and drawbacks that they may bring. In this paper, we are focusing on several specific areas, each with different applications of AI. Following is a brief of what was found in each area. In the first area, diagnostic and predictive medicine, we looked at the possible future use of AI for clinical laboratory tests and coming up with differential diagnoses for patients. In the second area, we examined medical management and production. Thirdly, we looked at AI applications for patients with chronic disease and the elderly. In the fourth area, we discussed AI applications for issues surrounding death and dying. Finally, we looked at how AI in healthcare will impact society as a whole. Although there are several more areas in which AI will be used, we feel that these are currently the most relevant to today’s society and will continue to be so in the coming years.

5. Conclusion and Recommendations

Mentioned previously, implementing AI in healthcare will have many implications for the current healthcare workforce. It is likely that automation of simple tasks will begin in the near future through the use of robotics. More advanced AI systems will begin to interpret complex data, being the most cost-effective solution. A hybrid intelligence workforce involving human professionals and machines must be considered in order to ensure the quality of patient outcomes. Governments must regulate AI innovators to ensure that health gains are prioritized over financial gains. The current AI culture is based on “moving fast and breaking things,” whereby iterative learning from trial and error is the norm. This culture cannot be tolerated in healthcare. AI implementers must be held accountable for any mistakes made in an attempt to ensure continual improvement in AI systems.

In conclusion, the potential impacts of AI on healthcare are wide-ranging. AI has the potential to transform healthcare by automating tasks currently done by humans. It can potentially reframe the work of healthcare professionals, enabling them to focus more of their time on patient care. Patient care could also be improved with the implementation of AI. Through a better ability to manage and analyze data, AI could provide a more accurate method of diagnosing. Patients could also obtain information and make appointments with AI chatbots, reducing the time taken to see a professional. The current healthcare workforce may struggle to adjust to these upcoming changes, and it is important that governments and policymakers take note of the potential displacement of workers, as it may exacerbate current shortages of certain health professionals. Overall, AI has the potential to greatly improve the current healthcare system. However, there needs to be caution to ensure that the positives outweigh the negatives.