I. Context

Artificial intelligence (AI )and digital technologies are increasingly shaping the U.S. healthcare system, transforming how patients are treated, monitored, and diagnosed. From past innovations such as X-rays and antibiotics to today’s advanced AI-powered diagnostic systems, technology advancements continue to reshape modern medicine. 

Despite technological advancements, the U.S. healthcare system continues to face significant systemic challenges. The country spends over $12,000 per person each year on healthcare, outspending every other developed nation. Even so, this level of spending is not creating better outcomes, as shown through how life expectancy remains lower and maternal and infant mortality are higher in the U.S. when compared to similar countries. Individuals who get care and the quality of said care are heavily dependent on race and income, while communities with the fewest resources face the greatest health risks. Additionally, the healthcare system itself is increasingly strained. Its workforce is stretched thin, with projections indicating a shortage of more than 120,000 physicians by 2030, rural hospitals closing at an alarming rate, and millions of Americans uninsured or underinsured. These issues are unlikely to be resolved in the near future. 

In response to these pressing issues, healthcare systems are continuing to turn to digital technology to improve efficiency and medical care. In most hospitals and clinics, tools such as electronic health records, digital imaging, remote monitoring, and telemedicine are simply part of how care is delivered to patients every day. They help providers manage care for millions of people, and they increasingly rely on AI to do so. About 80% of hospitals already report using some form of AI, with many more exploring generative tools. More than 340 FDA-approved AI systems are now used for diagnostics, helping doctors detect strokes, brain tumors, and breast cancer faster and more accurately than before. Hospitals adopting these technologies have reported a return of $3.20 for every dollar invested, with improved decision-making and cost savings in just 14 months. 

However, the rapid rise of AI technology raises concerns about equity and equality. Only 56% of rural hospitals use advanced AI tools, while over 80% of urban hospitals do. The difference shows the extent to which where you live can affect the technology available in hospitals. This means some communities are not modernizing, and there is a possibility that AI could deepen existing health disparities. Privacy is another concern. When sensitive medical data enters algorithms, risks are posed to security and patient vulnerability, and only 30% of hospitals report having robust data security protocols in place. And while AI can automate many tasks, it is not perfect. Studies show that some diagnostic algorithms are up to 30% less accurate for patients from underrepresented racial groups because of the biases in the data they are trained on. 

Many Americans worry that AI will replace doctors. Right now, it gives them smarter tools to do their jobs. The challenge is making sure everyone can actually benefit from those resources. 

II. AI Technology

Early instances of artificial intelligence in healthcare were documented in the early 1970s, when simple computer programs were used to help identify blood infections. These underdeveloped platforms served as a way to support doctors rather than replace their work. Half a century later, that has changed, not because healthcare suddenly adopted technology, but because the system started to break. Numerous challenges regarding workforce shortages and cost strains simply could not be fixed by advertising for more staff positions or building more facilities to spin a profit. As a result, investors turned to emerging, promising AI, which had the capabilities to run more efficiently at scale. In 2025, an unprecedented expenditure of 46% (more than $18 billion) of all healthcare investment went directly to AI, with this value projected to grow to $77 billion by 2035. Healthcare organizations and professions have increasingly invested into AI to address prevalent internal problems.

AI is now used in radiology and diagnostic imaging, helping doctors identify abnormalities faster and more accurately. AI algorithms can analyze X-rays, MRIs, and CT scans, performing an initial screening that highlights potential abnormalities. In 2025, 74% of U.S. hospitals used AI-powered diagnostic tools in their radiology departments, and many of those tools are regarded as reliable, having received FDA approval. AI-assisted imaging can match and even exceed the performance of experienced radiologists in detecting conditions such as lung cancer and tuberculosis, while also reducing time required for image reading by up to 33%.

AI also plays a role in administrative healthcare tasks, which often take up a significant part of clinicians’ time. Automated medical scribes use natural language processing to generate clinical notes during patient visits, reducing documentation workload by approximately 40%. Additional tools handle scheduling, billing, and insurance verification. By handling repetitive administrative tasks, AI frees up a main source of burnout for doctors and allows them to focus more on patient care. 

Finally, AI has the capabilities for predictive analytics and patient safety monitoring. Machine learning algorithms can analyze large datasets to predict disease outbreaks and anticipate high-risk patient populations. For example, AI models have been used to forecast influenza activity and identify regions at higher risk for hospital strain. In pharmacology, AI checks drug interactions and potential adverse effects, improving medication safety and reducing preventable errors. Across these areas, AI is helping to support a burdened healthcare system by taking on the background work that keeps doctors and hospitals afloat.

III. Benefits of Artificial Intelligence for Doctors and Patients

Artificial intelligence is becoming increasingly integrated into healthcare settings. As the U.S. healthcare system faces ongoing challenges like high costs and staff shortages, AI can be used as a tool to help address these issues. In healthcare, AI refers to computer systems that can analyze medical data and assist with tasks like documentation, imaging analysis, and more. Organizations such as the World Health Organization note that digital tools, such as AI, can improve efficiency and support healthcare systems when they have limited resources.

To begin, AI reduces the amount of time doctors spend on paperwork and administrative tasks, allowing them to focus more on patient care. According to the Cleveland Clinic, the use of AI tools such as automated documentation and transcriptions can take over routine tasks like writing clinical notes from patient visits. This could give doctors more time to spend with patients instead of needing to type directly into electronic records. In addition, AI systems could help schedule appointments and pull up patient history quickly, allowing staff to devote more attention to patient care.

AI also has the potential to save hospital systems money. By streamlining processes and reducing waste, AI can help to lower healthcare costs for both providers and patients. By using AI to automate billing, insurance checks, and documentation, costly errors (like unnecessary tests or treatments) and administrative costs can be reduced. In a survey conducted by the Center for Data Innovation, physicians believe AI will help with workflow efficiency and clinical outcomes, which indirectly reduces costs by saving time and avoiding mistakes. 

AI tools can help detect diseases earlier and improve clinical decision-making in regards to a patient's health. AI image analysis can detect certain diseases faster and more accurately than traditional methods, which helps doctors catch health problems earlier. Specifically with the most widely used medical imaging modalities, computed tomography scans (CT), image quality can be enhanced and more patients can be seen, overall helping departments do more with the resources they have. Faster imaging times and higher accuracy of scans makes advanced medical imaging more accessible and less stressful for both providers and patients (Philips). 

Lastly, AI tools give patients easier access to healthcare information. AI powered tools such as virtual assistants or chatbots can answer basic healthcare questions and overall make the healthcare process more convenient for patients. According to Cleveland Clinic and Lara Jehi, MD., AI tools can analyze large amounts of patient data to develop more personalized care plans based on previous patients. This saves patients and providers time and money, and overall makes the healthcare process smoother for both parties. 

AI technology has the potential to make meaningful improvements to healthcare for all. By reducing administrative tasks for doctors like scheduling and documentation, providers can save time and focus more on patient care. Increased efficiency can lower healthcare costs by reducing errors and wasted resources. In addition, AI supports more personalized healthcare by detecting diseases earlier and improving diagnostic accuracy. While AI is not a replacement for medical professionals, it has the ability to be used as a valuable tool when used responsibly and with proper oversight. As healthcare systems continue to evolve and face different issues, integration of AI can help to strengthen care delivery and improve patient experiences.

IV. Drawbacks of AI Technology in Healthcare

Although AI has the potential to improve healthcare delivery and public health systems, several limitations raise concerns regarding equity, reliability, and feasibility. As artificial intelligence begins to play an increased role in American medical delivery operations across the United States, several limitations associated with the technology must be considered.

Another issue that has been a consistent problem with AI technology in the healthcare industry is accuracy and reliability. While AI technology has been shown to pick up patterns in data sets and be very effective, it is not perfect and can be very damaging to patients if it is not reviewed for error. This can be a problem for AI technology that is trained on data sets that are not representative or diverse and may end up being less effective in an actual healthcare facility. A study conducted nationally found that although many hospitals across the U.S. have implemented predictive models of AI technology, fewer than half regularly review them for bias and that approximately 61 percent review them for accuracy before implementation (Association of Health Care Journalists).

Tied to accuracy is the concern of bias in algorithms. The way in which artificial intelligence works relies on past medical data, and historically, these data sets contain existing bias in medical access and outcomes. It takes particular care and attention to prevent algorithms from perpetuating and even escalating this inequality. Studies in the Journal of American Medical Association confirm that some algorithms in the medical field underestimated the medical needs of Black patients and provided them with poorer access to medical treatment compared to Caucasian patients with the same medical condition (Ratwani et al.). Other studies confirm algorithms are not always accurate in their diagnosis in women and minorities when there is underrepresentation in the medical data (Chinta et al.).

Cost effectiveness and accessibility signify another major challenge of adopting AI technology, especially for smaller rural hospitals that are not well-equipped. Implementing AI technology often requires advanced digital infrastructure as well as regular software updates, which would pose a challenge for many smaller rural health care facilities. A scoping review published in the Journal of the American Medical Informatics Association emphasizes that there are challenges faced by rural health care organizations such as limited data available for AI adoption as well as lack of technical expertise and budget (Huang et al.).

Economic assessments add to the complexity. Although certain AI technologies may theoretically decrease costs, economic assessments often do not consider all long-term costs, such as the maintenance of the system, cybersecurity, regulatory matters, and training of the workforce. A systematic review, published in npj Digital Medicine, finds the cost-effectiveness of AI in healthcare to be unclear, especially for low-resource environments, in which the startup costs may currently have a higher impact than the short-term gains (“Systematic Review of Cost Effectiveness”).

Lastly, the integration of AI brings forth major concerns in the workforce as well as in ethics. While the utilization of AI is seen as a complement to decision-making rather than a substitute for human healthcare practitioners, concerns have been voiced about the potential displacement of health practitioners or the undermining of professional autonomy. Brookings Institution scholars argue that the overuse of automated decision tools may undermine the capacity of health practitioners to detect errors, which may affect patient outcomes (“Risks and Remedies”).

Overall, although AI has the potential to be a major transformative agent in both health care and public health, the current limitations of AI in terms of its ability to be more accurate, free from bias, more affordable, its impact on the health care workforce, and how AI will be governed should not be ignored.

V. Future Impact

Artificial Intelligence (AI) holds significant potential for transforming medicine, and professions across healthcare, technology, and policy spheres see it as a tool to augment clinical practice rather than replace clinicians. Experts predict that AI will improve diagnostic accuracy, accelerate data analysis, and reduce the administrative burdens that currently occupy a large portion of clinicians’ time, enabling more direct patient care. However, leading professionals emphasize that successful integration would require robust evidence, ethical oversight, and collaboration between developers and healthcare providers to ensure that AI supports clinical decision-making in ways that are safe and effective.

Beyond theoretical claims, real implementations around the world already offer lessons for the U.S. healthcare system. In the United Kingdom, the National Health Service has deployed AI tools across various care settings to assist with diagnostics, improve workflow efficiency, and support earlier detection of serious conditions. AI is being used in imaging and screening to enhance the speed and accuracy of disease detection and is being embedded into routine clinical workflows to relieve clinicians of manual documentation tasks. The United Kingdom has also positioned itself as a leader in safe AI deployment, with its regulatory bodies helping to shape frameworks that can help scale trusted technologies in healthcare settings. 

Beyond the National Health Service, countries like China have integrated AI into tertiary hospitals to assist with diagnostics, clinical decision support, and workflow optimization at scale. These systems illustrate how AI can handle complex data loads and support precision medicine, although they also highlight the importance of clear regulatory and ethical guidance when deploying such tools in patient care. 

While adoption varies by region and institution, many U.S. health systems are already experimenting with artificial intelligence through pilot programs focused on areas such as prescription renewals, clinical documentation, and decision support. These early real-world applications highlight both the promise of AI to improve efficiency and access to care, and the challenges that must be addressed before the technology can be scaled safely and effectively across the healthcare system.

VI. Political Action, Legislation, and Youth Impact on Artificial Intelligence in Healthcare

With the advances of generative models, machine learning, imaging, and disease detection software, artificial intelligence has become increasingly common in American clinics and screening centers. However, as young patients become aware of AI and its effects on diagnoses, they have developed their own perspectives on the boundaries of its ethical usage.

Much of the youth population nationwide has become wary of the potential negative effects of generative AI on healthcare management and diagnoses, urging current healthcare systems to acknowledge these concerns and restructure their health assessment tools. A 2025 study by the University of Michigan surveyed youth aged 14 to 24 years, spanning various ethnic and socioeconomic backgrounds. Their perspectives have been overwhelmingly critical of AI use in the hospital. 13% of respondents agreed that AI could cause fatal errors, suggesting that its use puts patients’ lives at risk in life or death situations. An additional 29% believe it has yet to be refined enough to provide reliable legal or medical advice, as it occasionally presents misinformation in an assertive, factual tone. Respondents of the survey who discussed artificial intelligence in the context of healthcare primarily focused on the authority of decision-making and medical errors, highlighting the increasing level of importance the youth places on transparency and disclosure when using these AI tools for treatment.

In recent years, health policy on AI has surged at the state and federal levels. Just in 2025, 47 out of the 50 states have collectively introduced over 250 bills on AI in healthcare. 33 of those bills were passed and enacted into law in 21 states. It’s no surprise that AI has caught the attention of policymakers. Making ethical decisions on such a service is difficult as AI becomes increasingly prominent. One major theme that has emerged from these collections of bills is the focus on rural healthcare. During the second quarter of the legislative year, the U.S. Congress had advanced a draft of H.R. 1 (“One Big Beautiful Bill”) that includes the Rural Health Transformation Fund. The enacted version of H.R. 1 focuses on investment in consumer-facing software that enriches delivery of care, such as remote monitoring systems, telehealth, and AI-enhanced technology. Bureaucratic agencies are implementing these changes to promote AI usage policies, notably in their Centers for Medicare and Medicaid Services (CMS). The CY2026 Proposed Medicare Physician Fee Schedule issues a request for AI and even seeks public feedback on digital tools that could improve Medicare beneficiary access, system compatibility, and reduce administrative burden. Medicare and Medicaid are known to serve underrepresented and financially disadvantaged communities in rural areas. By establishing a loop connecting public input to their own healthcare, AI can become more accountable, responsive, and fulfilling to the rural population it aims to serve through policy.

The concerns expressed by young people about artificial intelligence in healthcare highlight a crucial, diverging intersection between technological innovation and public safety and accountability. As U.S. legislation rapidly expands through federal initiatives and becomes stabilized and validated by the states, youth perspectives must integrate into policy discussions. Participating in public comment periods as mentioned above and providing input on AI systems during pilot testing sessions can alter the momentum of AI’s rapid proliferation into everyday clinical practice. As always, legislative lobbying and contributing to advocacy coalition initiatives are everlasting pathways, always accessible and functional. Youth advocacy can encourage clearer disclosure standards, stronger oversight, and more rigorous ethical safeguards on AI systems used in clinical settings. In doing so, they help ensure that technology reshaping healthcare also reflects transparency and relevant, youth-centered values.

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