Role of Artificial Intelligence in US Healthcare

After the COVID pandemic, many Americans realized that there were certain areas that the US healthcare system had to improve for patient care. Artificial intelligence (AI) has been a technology that has been exponentially developing the past few years; particularly it has been highly influential in the field of healthcare. In modern times, AI serves as a powerful technical tool that has the capacity to efficiently accomplish complex tasks that traditionally demand excessive human thinking. It is a very crucial technology since if it is utilized appropriately, it can support an American healthcare system that is highly effective, efficient, innovative, and caring for patients. For example, it is able to interpret imaging results (like X-rays) more accurately than physicians and quickly analyze large medical data sets that doctors may take several months to process. Furthermore, AI is currently being used in antimicrobial resistance research, cutting down time and energy spent by medical scientists dedicated to that field. However, despite the numerous benefits that AI displays for healthcare, many people in the American public and patients of the healthcare system share concerns regarding issues like bias, privacy, and lack of trust. Incorporating AI education into medical school curriculum and creating a board of clinicians to supervise AI systems are viable solutions that can mitigate public concern. Although some patients and the public may be against the technology, AI plays a crucial role in supporting an effective healthcare system in the US if properly leveraged and its potential drawbacks are well-mitigated. 

Literature Synthesis

Status Quo of AI in the Healthcare Field

Increase in Healthcare Efficiency

Currently, in the United States, AI is already emerging in certain areas of healthcare. Specifically, it frequently serves as a personal assistant for many clinicians and doctors who apply it to their tasks. On certain days, physicians tend to have an immense logistical workload, resulting in less time that they are able to dedicate towards patient care. On those heavy-workload days, doctors can apply AI technology to their redundant duties, utilizing it to “automate tasks to free up… time” and allowing them “to focus more on their patients” (Ellis, 2024). Moreover, AI technology is able to “automatically capture visit notes” as well as “write letters to patients”, serving as “medical scribe technology” in a way (Ellis, 2024). Hence, by automating mundane logistical and record-keeping tasks, AI displays itself as an essential tool that boosts the efficiency of healthcare professionals. With the excess time that is made available because of AI algorithms, clinicians can ultimately focus on more quality patient care. 

Despite the extensive expertise that physicians possess in their field, especially after years of rigorous medical school education, as human beings they are psychologically prone to error during patient diagnosis and treatment. In order to prevent such errors, clinicians need to spend more time checking/reviewing over their diagnosis based on the medical tests and imaging that was conducted on a given patient. In this case, AI also can be applied as a capable tool that analyzes “medical imaging data, such as X-rays, MRIs, and CT scans, to assist healthcare professionals in accurate and swift diagnoses” (LAPU, 2025). Thus, AI demonstrates that it’s a technology that not only can increase the precision of healthcare workers in their problem-solving, such as patient diagnosis, but can also operate rapidly while retaining such high-level accuracy. Consequently, AI technology allows clinicians to reduce the time and energy they devote to reviewing over imaging results, as well as be more effective in treating their patients. 

Accelerated AI-Based Drug Discovery

As earlier discussed, AI skyrockets the effectiveness and efficiency of the US healthcare system by expediting redundant tasks and improving accuracy in the diagnosis/treatments. However, the same technology also aids specialists in the healthcare field that work in very niche areas of the medical field. One prominent example of a niche area is drug research. Every year, several new rare diseases are found in patients which many times leave clinical drug researchers confused and scrambling for a way to find cures for such novel diseases. By far, AI has been the most useful in combatting rare diseases due to the capacity it boasts to process and analyze large volumes of data in pursuit of identifying patterns or generating discoveries relevant to a curing medicine (Ellis, 2024). Therefore, AI algorithms are certainly effective in improving the US healthcare system due to their versatility and flexibility they provide, aiding both general clinical practitioners and medical specialists in their endeavors. That said, some of the current drug research that has been occurring in the United States is targeted at Antimicrobial Resistance (immunity that viruses develop to antibiotic medications administered on patients, referred to as AMR). Researcher specialists that have been investigating viable solutions for AMR face adversity due to the limit of funding available as well as the pressing factor of time. The more time that passes, the higher the death count due to AMR. Recently, these medical researchers have employed Machine Learning (a type of AI technology) as an aid; particularly, the “machine learning algorithms, ranging from Decision Tree (DT) to Gradient Boosting Machines (GBMs), play a key role in the discovery of genetic markers associated with antibiotic resistance” (Branda & Scarpa, 2024). Essentially, AI technology possesses the ability to analyze and synthesize genomic data that provides the viruses/bacteria their antimicrobial resistance. Machine learning consequently becomes a catalyst for the medical research process, since it is able to accelerate drug discovery, decreasing the time it takes to find a potent medication and maximizing the financial funding provided to drug researchers. Ultimately, the beneficial role that AI plays in efficiently finding cures to rare diseases is another way that the technology ameliorates the efficiency of US healthcare. 

Public Perspectives and Future Concerns 

Opposing Views of US Healthcare Patients

Despite the clear positive attributes of AI-integrated healthcare, the patients of hospitals in the US hold varied perspectives on what they think of the technology being developed further for future use in healthcare. According to a recent survey taken of 926 patients across the nation, 55.4% of them believe that AI will make US healthcare better to some extent (Khullar et. al., 2022). In other words, it’s estimated that a little more than half of the patients in hospitals across the country are optimistic about a futuristic healthcare system based on AI technology. In contrast, 45.1% and 68.8% of respondents reported being uncomfortable with an AI robot reading their chest X-ray and making cancer diagnosis, respectively (Khullar et. al., 2022). Previously it was discussed how AI systems are, in fact, more precise in interpreting medical imaging and diagnosing diseases compared to the average physician due to the technology lacking the vulnerability that humans have to making mistakes. Nevertheless, a considerable portion of patients feel discomfort with an AI robot completing such tasks due to the fear and lack of trust they express towards a non-human entity. Such issues can be properly addressed if the US applies specific strategies, which will be later elaborated upon. Returning back to the statistics that this national survey provides, the findings are significant since it quantifies the controversy of AI-based healthcare among the patients that it's designed to help. Some patients are hopeful and look forward to a future healthcare system that has more quality due to AI, while others are very uncomfortable and fearful with the thought of such a future. In fact, the fear that hospital patients in the US have are also shared by the greater American public; both the patients and the public have underlying reasons for why they feel that way. 

Fear of US Public Against AI Systems

Many Americans express a sincere fear of AI and robots in general, primarily due to a stigma surrounding such technology. The stigma consists of the ideology that AI technology is untrustworthy and dangerous. One underlying factor to the stigma around AI and robots is the media culture surrounding them, consisting of “Terminator to Alien to 2001: A Space Odyssey”, all of which depict such technology in a negative light (Dickinson, 2020). Even though such movies have been very enjoyable for American audiences, they imply to the watchers that AI innately should not be trusted with a human life and can threaten the existence of mankind if allowed to grow. Despite the vast benefits that the technology provides, as earlier explained, a large portion of Americans feel discomfort towards it due to the media that influences them. Another underlying factor that causes the American public to be anxious about AI’s emergence is the assumption that it “will take away jobs” and “threaten” numerous “forms of employment” (Dickinson, 2020). Such an assumption is quite natural for the public to create since AI boasts the capacity to be highly efficient and effective not only in the field of healthcare, but in other sectors of the US economy as well. However, the truth is that AI’s integration in the healthcare system is meant to be a tool that can be leveraged by clinicians to provide the most quality care they can possibly provide to patients. Rather than overtaking the system, AI technology is meant to be a side-by-side companion for doctors, nurses, and other healthcare workers so that they can be more efficient with their time and direct their efforts towards treating patients over logistical/redundant hospital tasks. Moreover, some may believe that the public’s fear arises out of the AI’s potential for bias based on gender and race, since the data that clinicians train AI technology with consists of bias already ingrained in human society. Due to AI having access to data, US patients and the public feel fear due to data privacy concerns (LAPU, 2023). Overall, the American public is concerned with AI-based healthcare primarily because of their fear of misdiagnosis, the stigma and misconceptions, bias, and data privacy issues. 

Mitigating Potential Drawbacks to AI-Integrated Healthcare

Educating Future Physicians 

There exist multiple strategic approaches that the US can take to mitigate the public concern of AI being harmful for patients. One highly viable solution is the initiative to educate future doctors on how AI is used in the healthcare system. In order for future doctors to be proficient in AI, medical schools need to invest in equipping their “medical students with knowledge in AI” (Grunhut et. al., 2021). Medical school committees need to understand the importance behind funding their school, since in order to properly adapt to the new AI healthcare systems medical students need to enter the workforce with sufficient familiarity with AI algorithms, such as machine learning and neural networks. Additionally, if medical professionals are comfortable with AI usage in hospitals, they will be able to explain to their patients the safety of AI and its high precision in the healthcare setting. Consequently, future physicians will be able to promote greater trust in the American public regarding AI-based healthcare and clear the stigma and misconceptions that were caused by media culture. 

Dedicating a Board of Clinicians to Frequent Review of AI Hospital Systems

Another practical solution to the patient/public concern of AI-supported hospitals is creating a board of clinicians, ideally 5-10 of them, who are dedicated to the frequent review of AI technology in the hospital building. During hospital hours, it would be beneficial for a group of clinicians, who are trained in AI technology, to be occupied with “regulatory approval and proper validation of algorithms” and for consistent weekly meetings conducted where the AI’s decisions are discussed (Khan et. al., 2023). By utilizing this approach, patient’s fears of AI possibly misdiagnosing their condition or threatening their data privacy no longer becomes an issue because there is always human supervision behind every one of the algorithm’s actions. Furthermore, a board of clinicians will serve as an entity that will continue to update and revise AI systems in a hospital, ensuring that the healthcare institution is always evolving and growing to be more efficient and effective in patient treatment. As for the final concern that many Americans held regarding AI, which was bias, the board of clinicians will also be able to minimize such an issue. Aforementioned that AI can be trained with pre-existing data, biases generally stem from “underrepresented, or missed entire attributes” that an AI may overlook if it’s not trained with representative data (Khan et. al., 2023). Creating a clinician board allows for that particular group to modify the AI’s training data to be more representative of the diverse human population. Provided that healthcare is a field where it’s important to carefully consider a patient’s background when treating them, instating a board of clinicians in hospitals/medical institutions across the United States to oversee AI operations ensures that the technology doesn’t overlook patients due to bias and instead delivers patient care based on equality. 

Funding Regulatory Efforts 

The approaches of incorporating AI education into medical school curriculum and creating hospital clinician boards are both practical measures to alleviate patient and public concern of AI. However, they will be rendered ineffective if adequate funds aren’t properly allocated to such efforts. It’s vital that the US government “provide grants to fund” such regulatory initiatives, as well as “nongovernmental organizations” in the nation (Angus et. al., 2025). If both the federal government and private investors collectively sponsor and financially support the regulatory approaches for AI technology, it can be assured that the AI-based healthcare system is safe, fair, robust, and optimized for quality patient care because the collaborative funding provides a strong financial base for medical schools to add AI to their curriculum and pay for the addition of a clinician board in each hospital; ultimately, the new curriculum and board of clinicians will be able to serve their function and ensure a safer, unbiased, efficient AI-healthcare system. 

Discussion

Summary

As of currently, AI is being used in multiple aspects of healthcare to boost its efficiency and effectiveness in treating and caring for patients. A few examples include its streamlining clinician logistical work, increasing accuracy of medical imaging, and accelerating medical drug research. Nevertheless, many patients in US hospitals and the American public in general express fear and distrust towards AI technology being further incorporated into the healthcare system. Their primary reasons for being against AI-based healthcare include their fear of potential misdiagnosis that may occur, stigma and misconceptions due to American media culture, racial/gender bias, as well as poor integrity of data privacy. Fortunately, many practical strategies to mitigate public concerns exist: educating medical students and instating a board of clinicians in every US hospital. Through these approaches, future doctors will be highly knowledgeable in AI-based medicine, ensuring that patients can trust such a healthcare system and will understand the falsehood of AI stigma/misconceptions. Furthermore, creating a board of clinicians to supervise AI systems will prevent misdiagnoses from occurring, minimize the technology’s bias, and protect patient data privacy. Finally, in order to support such regulatory efforts for AI systems, it’s paramount that the federal government and private investors collaborate to fund them. 

Analysis of Sources

Two sources that were highly valuable to this research were the article from Harvard Medical School, published in 2024, and the article about AI addressing Antimicrobial Resistance, also published in 2024. Both of these articles were current, which ensured that they were relevant to my topic and provided contemporary information. Moreover, they are database sources published in extremely reputable journals, one of them being Harvard Medical School. Due to their high-tier credibility, they built a strong foundation for my research paper by explaining the current usage of AI in healthcare and equipping me with the crucial background knowledge that I needed to produce my argument. However, two sources that were not as valuable to my research were “Artificial Intelligence in Healthcare: Transforming the Practice of Medicine” and “Artificial Intelligence in Healthcare… Friend or Foe?”. Both of these articles were from databases/journals and were relatively current, but they didn’t align well with the purpose of my research and focused too deeply on the technical aspects of AI rather than the public concern surrounding it or solutions to mitigate such concerns. Hence, they weren’t used in my actual research paper. 

Conclusion

Ultimately, the position taken on the original thesis was upheld. AI provides clear benefits to American society due to its ability to skyrocket the productivity and quality of its healthcare operations. It serves as an efficient assistant for clinicians and consequently its integration into hospitals will be inevitable due to its necessity in current times (especially with the threat of dangerous diseases such as COVID). Opposing viewpoints do exist among patients and the public of the US, with many believing that AI poses bias, privacy issues, untrustworthy, etc. Nevertheless, such concerns can be addressed with strategies such as educating medical students in AI systems, clinician board supervision, and increasing funding for regulatory efforts. 

Next Steps

One area of research that could use further research is regarding the implementation of the board of clinicians in hospital systems. How can they be seamlessly incorporated into US healthcare? Should the transition be gradual or immediate? How will the federal government consolidate funds to sponsor the effort? In order to address these questions and clarify the solution’s parameters, more research should be conducted into where the US government’s interests lie with such a regulatory initiative since their funding will be needed to actually materialize the solution. 

Personal Reflection

Overall, this research paper strengthened the optimism I had for AI to be used in healthcare. Based on my prior knowledge, I already understood that AI was cutting-edge technology that would revolutionize not only healthcare but many other fields as well. However, I didn’t know the patient and public perspectives that existed surrounding the issue, and my research allowed me to gain a better understanding of those viewpoints. Ultimately, my research revealed to me that even when some may have concerns regarding AI implementation into healthcare, there exist completely feasible solutions to such concerns that will aid in making AI-based healthcare a reality. The human race constantly seeks to progress and improve, and AI provides a means for that to happen in healthcare. Observing the positive implications of AI strengthened my positive perception of AI. As long as proper ethical guardrails and human supervision are in place (which exist in multiple areas of society), there should be no reason why AI shouldn’t be incorporated into hospitals. 

References

Angus, D. C., Khera, R., Lieu, T., Liu, V., Ahmad, F. S., Anderson, B., Bhavani, S. V., Bindman, A., Brennan, T., Celi, L. A., Chen, F., Cohen, I. G., Denniston, A., Desai, S., Embí, P., Faisal, A., Ferryman, K., Gerhart, J., Gross, M., & Hernandez-Boussard, T. (2025). AI, Health, and Health Care Today and Tomorrow. JAMA. https://doi.org/10.1001/jama.2025.18490

Branda, F., & Scarpa, F. (2024). Implications of Artificial Intelligence in Addressing Antimicrobial Resistance: Innovations, Global Challenges, and Healthcare’s Future. Antibiotics, 13(6), 502–502. https://doi.org/10.3390/antibiotics13060502

Dickinson, D. (2020). People Fear Artificial Intelligence. In AI, Robots, and the Future of the Human Race (1st ed., Ser. Introducing issues with opposing viewpoints, pp. 61-66). Essay, Greenhaven Publishing. 

Ellis, L. (2024, August 30). The Benefits of the Latest AI Technologies for Patients and Clinicians | Harvard Medical School Professional, Corporate, and Continuing Education. Harvard.edu. https://learn.hms.harvard.edu/insights/all-insights/benefits-latest-ai-technologies-patients-and-clinicians

Grunhut, J., Wyatt, A., & Margues, O. (2021). Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes. Journal of Medical Education and Curricular Development, https://pubmed.ncbi.nlm.nih.gov/34778562

Khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023). Drawbacks of Artificial Intelligence and their Potential Solutions in the Healthcare Sector. Biomedical Materials & Devices, 1(36785697), 1–8. https://doi.org/10.1007/s44174-023-00063-2

Khullar, D., Casalino, L., & Qian, Y. (2022, May 4). Perspectives of Patients about Artificial Intelligence in Health Care. JAMA. https://jamanetwork.com/journals/jamanetwork.open/fullarticle/2791851

LAPU. (2023, December 21). How is AI Being Used in the Healthcare Industry. LAPU - Los Angeles Pacific University. https://www.lapu.edu/post/ai-health-care-industry