Artificial Intelligence in Health Care

Picture this: you show up to a job interview, full of excitement, only to discover that the person you’re meeting with isn’t a representative at all—it’s an Artificial Intelligence (AI) system. As it asks you detailed personal and behavioral questions, you realize you’re conversing with an artificial assistant rather than a human being. This almost dystopian reality isn’t as far off as it seems. As AI continues to infiltrate more aspects of our daily lives, including job interviews, it is understandable that many people are hesitant. Artificial Intelligence is a rapidly evolving field, filled with robust new technologies and advancements aimed at addressing various societal challenges. However, many of these developments are also seen as invasive, threatening, and disruptive to societal norms, potentially increasing technological dependency and accelerating unemployment. Yet, amidst these concerns, there remains a strong optimism for AI’s transformative potential, particularly in the healthcare sector. Among its most promising benefits are improving diagnostic accuracy, reducing administrative burdens, expanding access to care—and ultimately saving millions of lives.

Artificial intelligence is proving to be a highly valuable tool, especially when integrated and developed thoughtfully. AI can help diagnose patients and analyze symptoms, sometimes spotting abnormal results and suggesting personalized treatments. For instance, AI is currently being used to interpret medical machinery outputs, such as electrocardiograms (ECGs), assisting in the diagnosis of various heart conditions. These predictions are then reviewed and revised by senior cardiologists, creating a collaborative model that helps enhance hospital efficiency and accuracy. Artificial intelligence is also used to analyze CT scans and identify strokes early before it can cause irreparable damage. AI use to analyze scans is especially promising in urgent care settings, which are both understaffed and overworked, serving as the initial scan for patients coming in. Certain generative AI systems are also able to ask patients questions about their symptoms and recommend appropriate next steps based on their responses. In some cases, AI is able to detect diseases before they become visible to doctors, such as early signs of Alzehimer’s or kidney disease. One study showed that AI was able to find 64% of brian lesions that radiologists had missed. In another case, an AI program gave specific treatment advice that ended up saving a patient’s life. Most interestingly, tools like Buoy Health have been used to help healthcare systems triage patients with COVID-19, reducing pressure on hospitals. The ability of AI in assessing symptoms, prioritizing patient needs, and directing individuals to appropriate levels of care is no short of incredibly impressive. This triage capability not only helps streamline hospital admissions but also ensures critical cases are identified and treated faster. Another study found that in more than three-quarters of cases, AI was able to correctly predict which patients needed to be moved to a hospital, based on information such as pulse, oxygen levels, and mobility (World Economic Forum). Intelligent tools can work alongside physicians to elevate care and improve patient outcomes. All of these initiatives lead to a powerful truth: when developed responsibly and used alongside healthcare professionals, AI holds the power to revolutionize medicine—transforming how we diagnose, treat, and triage patients, and paving the path to a future where millions more lives can be saved.

Another key application of AI within healthcare is in administrative support. AI medical scribes, such as those already implemented at Kaiser Permanente and various other hospitals, have significantly alleviated the workload for overworked physicians. According to a recent study done on Kaiser medical professionals, AI scribes have saved the equivalent of five full work years (The Permanente Medical Group) by simply using a scribe for note-taking. Using machine learning, ambient scribes are able to summarize patient visits and generate accurate, comprehensive documentation, drastically reducing time spent on mundane note taking and charting. This not only improves overall efficiency, but plays an important role in minimizing physician burnout.

Artificial intelligence is also playing an invaluable role within medical research. It is able to analyze large datasets and identify relative studies which help doctors choose more effective treatments, thus improving patient outcomes. This is especially valuable when creating therapies tailored to a patient’s unique genetic makeup or when trying to speed up the process of drug development. Development of new medications can take decades before even reaching clinical trials, which means potential cures for many serious conditions such as certain cancers or hearing loss are often years away. AI is able to help shorten this timeline by quickly sorting through vast amounts of data and even generate new, innovative ideas. Furthermore, sometimes a drug developed for one condition can be repurposed for another. Deep learning models, which are capable of processing large volumes of information, can help identify these possibilities. In fact, newer models have already found thousands of potential treatments using this repurposing method, accelerating drug discovery and improving patient care. These tools are also being used in biomedical research, such as in designing new molecules and predicting how they interact with biological systems. For example, at the Baker Lab located in the University of Washington, researchers are using generative AI to design proteins to help be used in disease detection and treatment. There are countless more applications of AI within the medical research industry, which are invaluable in generating new insights and accelerating discoveries.

All the aforementioned previous AI pursuits have all been focused on integration within the industry, however, one ambitious AI initiative aimed to further rescue the burden on healthcare workers in completely eliminating the human element through their introduction of virtual doctor visits. Forward Health was one company who took on a great challenge and introduced the idea of a CarePod—an AI medical box designed to have zero human elements involved except the patient. The CarePod was designed to take vitals such as blood pressure, draw blood, and pulse, and furthermore was hopefully to replace the primary care physician visits. Despite the innovative concept, the CarePOD proved to be disastrous as the company filed for bankruptcy in early 2024 following a range of setbacks such as accidentally trapping users inside the pod and errors in vital recording. Moreover, public skepticism and a lack of trust in fully automated care proved to be its downfall. I believe that the speed in which Forward Health implemented the CarePods is a learning point for many companies in the future. The rise of AI across industries has sparked a wave of investment, with many companies eager to capitalize on its potential and board the booming train. However, without proper investigation, it is more likely that the company will end up decimated by the AI train instead. The CarePOD is a revolutionary concept that has the potential to replace traditional doctors visits in the future, but will require extensive testing and reworking the p0ubic stigma to ensure trust and safety. The technological shortcomings and poor functionality of this early AI innovation proved to be detrimental for Forward Health and overzealous deployment without thorough testing and regulation can lead to reputational damage and real world harm. Therefore, it is crucial to address AI’s risks early on, with strong safeguards, constant testing, and human oversight, before allowing these systems to operate independently.

Artificial intelligence is advancing rapidly and holds great potential across multiple industries, but widespread skepticism remains. This is particularly evident as AI continues to expand into various sectors raising concerns that it may render many jobs obsolete. Additionally, many have voiced concerts over an overreliance on technology and the unintended consequences this brings such as contributing to declining literacy rates amongst younger generations (USA Today).

In healthcare, caution is especially concerning and important to take into consideration. AI hallucinations, instances where systems generate inaccurate or absurd outputs unrelated to the input data, remain a serious concern. If such a hallucination occurs during an ECG reading or within an autonomous setting like the CarePOD, the consequences could be life threatening. Additionally, AI may worsen pre-existing racial disparities in healthcare outcomes. For example, a tool used to predict the likelihood of a successful vaginal birth after cesarean (VBAC) incorporated race-based correction factors into its model, which then assigned lower success rates to Black and Hispanic women. Consequently, the tool encouraged more cesarean deliveries for these groups, conveying how algorithm decisions can reinforce structural inequalities. In another example, Microsoft’s chatbot “Tay,” which was developed on user post data, began generating racist content on the very day it was launched. Similarly, “Ms. DEWEY,” a misogynistic and sexist chatbot, reflected problematic stereotypes and demonstrated the clear issue behind an AI development team that is simply not diverse. One of the most critical questions to ask is: Who comprises the teams developing these AI tools, and what data are they being trained on? If the team behind these technologies lack diversity or rely on biased datasets, how can we expect the outcomes to be equitable? Without thoughtful oversight, AI will not reduce disparities and instead only amplify them. In response to such concerns, the U.S. Department of Health and Human Services issued a rule in July 2024 requiring organizations to take meaningful steps to prevent discrimination in AI tools. These efforts, along with specialized facilities to test and validate AI systems for safety and fairness, offer small hopes in addressing this bias. Ultimately, these risks highlight the importance of ensuring that all AI generated outputs are reviewed and validated by qualified medical professionals—especially during the early stages of AI integration into healthcare systems.

Ultimately, artificial intelligence holds tremendous potential to transform healthcare by reducing hospital workloads, increasing efficiency, advancing research, reducing physician burnout, and most importantly in saving lives. Understandable concerns remain about the ethical and practical implications of widespread AI use. Therefore, within healthcare, AI’s implementation must be guided by rigorous testing, oversight, and physician collaboration to ensure patient safety. The reality is artificial intelligence is here to stay, and embracing continuous innovation can bring about several benefits while still maintaining control over its potential negatives.

Works Cited

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The Permanente Medical Group. “Analysis: AI Scribes Save Physicians Time, Improve Patient Interactions and Work Satisfaction.” Permanente Medicine, 13 Jan. 2025, https://permanente.org/analysis-ai-scribes-save-physicians-time-improve-patient-interactions-and-work-satisfaction/.

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World Economic Forum. “6 Ways AI Is Transforming Healthcare.” World Economic Forum, 13 Jan. 2025, https://www.weforum.org/stories/2025/03/ai-transforming-global-health/.

National Institutes of Health. “All of Us and Artificial Intelligence Help Speed Up the Search for Promising Medicines.” All of Us Research Program, 13 Jan. 2025, https://allofus.nih.gov/article/all-of-us-artificial-intelligence-help-speed-up-search-for-promising-medicines.

Penn Medicine. “AI Tool Helps Find Life-Saving Medicine for Rare Disease.” Penn Medicine News, 13 Jan. 2025, https://www.pennmedicine.org/news/news-releases/2025/february/ai-tool-helps-find-life-saving-medicine-for-rare-disease.

Harvard Medical School. “Researchers Harness AI to Repurpose Existing Drugs for Treatment of Rare Diseases.” Harvard Medical School News, 13 Jan. 2025, https://hms.harvard.edu/news/researchers-harness-ai-repurpose-existing-drugs-treatment-rare-diseases.

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