Post authored by: Juan Sanchez (Limitless Foundation Researcher)
As artificial intelligence (AI) continues to evolve and broaden access to information on a global scale, a myriad of important questions arise regarding its effectiveness and role in daily life. The influence of AI is being felt across a diverse range of fields, from entertainment to education, and it has recently found a significant foothold in the rapidly changing landscape of healthcare. The Nuffield Council on Bioethics highlights that contemporary AI is increasingly refining its capabilities to interpret “varied and unstructured kinds of data,” a vital development that is reshaping how we perceive and manage health-related issues across the globe. This enhancement in AI’s capabilities is primarily attributed to advancements in machine learning. This cutting-edge technology enables AI to uncover intricate patterns during data analysis, effectively transforming raw data into actionable insights (Nuffield Council on Bioethics 2).
By actively learning from the data it receives, machine learning facilitates adaptive learning and nuanced data interpretation. This capability proves invaluable as it finds applications across four distinct categories in healthcare: healthcare organizations, medical research, clinical care, and consumer-facing applications (Nuffield Council on Bioethics 3). Each of these categories significantly contributes to not only improving patient outcomes but also enhancing operational efficiencies within healthcare systems.
In the realm of medical research, AI plays a pivotal role by synthesizing vast amounts of historical data alongside pertinent sources, thus assisting researchers, pharmacists, and pharmaceutical companies in their quest for new treatments and medications. This optimization process focuses on enhancing the quality and efficiency of healthcare services, driving innovative solutions while managing the plethora of information that modern healthcare generates. As reported by Khanijahani and colleagues, the adoption of AI in healthcare has become indispensable, with both public and private sectors investing a staggering $6.6 billion in 2021 to create precise models that improve patient care and streamline operations (Khanijahani et al. 2022). Such substantial investments are projected to dramatically boost the efficiency of healthcare organizations, potentially saving them up to $150 billion annually by 2026. This financial relief could alleviate many staffing and organizational challenges that have historically plagued the industry, including the pressing issue of staff shortages and the increasing demand for healthcare services.
However, these transformative changes—particularly within clinical IT systems—are not without their drawbacks. Khanijahani highlights the pervasive skepticism among medical professionals about integrating AI into clinical settings, which could inadvertently lead to significant financial repercussions due to the considerable investments required for these advanced systems. This lack of trust in AI technologies risks leaving specific healthcare organizations trailing behind their competitors, hampering their ability to provide comprehensive patient care. Furthermore, the authors caution that AI is not infallible; it holds the potential to misguide physicians in making critical clinical decisions, undermining the very care that patients depend on.
In a comprehensive review conducted by Khanijahani and colleagues, it was found that while AI performs effectively in limited stages of treatment, such as triage, the results depend significantly on the severity of the injuries and the level of care required. This heightened efficiency, combined with the ability to provide swift treatment, allows healthcare professionals to act decisively, which can be crucial for patients whose lives may hinge on timely interventions. The capacity for rapid assessment and treatment is invaluable in acute care scenarios, illustrating the potential for AI to enhance patient outcomes when integrated thoughtfully into clinical workflows.
The growing reliance on AI underscores the urgent need for well-defined governance frameworks tailored explicitly for AI use in healthcare. As emphasized by Ryan and colleagues, many algorithms that support clinical decision-making or prioritize access to specialized services might operate beyond the regulatory reach of the FDA (Ryan et. al 2025). This regulatory gap arises partly because AI can be viewed as a facet of medical practice, traditionally not overseen by the FDA. To address this challenge, there is a pressing need for government legislation that effectively regulates AI’s applications in healthcare or for an expansion of the FDA’s regulatory authority to encompass AI technologies. This step is crucial to promoting fair competition among all healthcare providers and ensuring that innovation does not come at the expense of patient safety and care quality.
Moreover, it is essential to establish clear boundaries regarding the extent to which AI influences doctors’ decision-making processes to ensure that clinical expertise remains paramount. While AI has the potential to enhance decision-making and reduce errors, it should ultimately serve as a supportive tool that complements the judgment of healthcare professionals rather than replacing it. Ensuring collaboration between AI technologies and human expertise will be pivotal in creating a healthcare system that prioritizes patient safety while leveraging the advantages that modern technology offers.
References:
Khanijahani, A., Iezadi, S., Dudley, S., Goettler, M., Kroetsch, P., & Wise, J. (2022). Organizational, professional, and patient characteristics associated with artificial intelligence adoption in healthcare: A systematic review. Health Policy and Technology, 11(1), 100602.
Kim, J. Y., Boag, W., Gulamali, F., Hasan, A., Hogg, H. D. J., Lifson, M., … & Sendak, M. (2023, June). Organizational governance of emerging technologies: AI adoption in healthcare. In proceedings of the 2023 ACM conference on fairness, accountability, and transparency (pp. 1396-1417).
Ryan, K., Yang, H.–J., Kim, B., & Paik Kim, J. (2025). Assessing the impact of AI on physicians decision-making for mental health treatment in primary care. npj Mental Health Research, 4, 16. https://doi.org/10.1038/s44184-025-00124-y
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