Presentation
Artificial Intelligence (AI) as a Middleman in Healthcare: Preserving the Human Connection at the Heart of Healthcare
SessionPoster Session 1
DescriptionAs technologies continue to evolve and become increasingly accessible, the use of technologies for healthcare purposes continues to be a topic that needs to be monitored and investigated. More specifically, artificial intelligence (AI) is becoming increasingly prevalent with its easy accessibility and so-called “validity” among users. This begins the debate of positive versus negative applications of AI software in the healthcare field. AI technologies are rapidly transforming clinical practices. From diagnostic imaging and predictive analytics to virtual health assistants and automated documentation, the use of AI in healthcare will only continue to become more widespread. While these innovations promise greater efficiency and accessibility, they also introduce challenges that directly affect the human connection central to healthcare delivery. Recognizing that healthcare is not solely a sum of diagnoses and clinical accuracies, this research is grounded in the fact that healthcare relies heavily on human interaction.
This research examines how AI impacts the patient–provider relationship, particularly focusing on four central dimensions: generalization vs. personalization of care, equity of access, trust and transparency, and patient autonomy vs. AI-driven decision-making. While AI has the power to boost efficiency, accuracy, and accessibility, it also risks compromising empathy, individualized care, and trust, which are all fundamental components of effective healthcare.
This work emphasizes that the future of AI in healthcare depends on balancing efficiency with the preservation of the human connection between patient and provider. A central advancement is the recognition of tension between generalization and personalization. AI excels at identifying population-level trends and reducing variability through standardized guidelines, which improves efficiency and clinical accuracy (Mutharasan & Walradt, 2024). However, true personalization arises when AI complements rather than replaces clinical judgment–ensuring care remains sensitive to individual cultural, emotional, and contextual needs (Bærøe et al., 2023). This perspective reframes personalization as not only a technical challenge but also an ethical and relational one.
AI-driven platforms, including chatbots and remote monitoring tools, can expand access to underserved populations by mitigating barriers to entry (Gurevich et al., 2022; Sun & Zhou, 2023). However, disparities in digital literacy, broadband access, and biased training datasets risk exacerbating inequities. With programmed biases and a lack of an inclusive design, AI may disproportionately disadvantage marginalized groups who already face systemic barriers to healthcare (Cross et al., 2024). The equity, diversity, and inclusion in artificial intelligence (EDAI) framework demonstrates how equity, diversity, and inclusion (EDI) must be embedded throughout the AI lifecycle, from dataset design to deployment. Without such intentional design, however, inequities in digital literacy, broadband access, and algorithmic bias could exacerbate disparities, particularly for marginalized groups who already face systemic barriers (Abbasgholizadeh Rahimi et al., 2024). This insight advances knowledge by showing that equity is not a byproduct of AI—it must be deliberately built in.
Trust and transparency are equally indispensable. Research on explainable AI (XAI) shows that algorithmic confidence and explainability directly affect user trust and diagnostic performance (Ullah et al., 2024). While explainability fosters confidence, both underreliance and overreliance can emerge, highlighting the delicate balance needed when presenting AI recommendations to providers and patients (Klingbeil et al., 2024; Ni & Jia, 2025). Transparency is vital in healthcare and can be seen through open communication and shared decision-making, which ultimately foster a patient’s trust in their provider (Montgomery et al., 2020; Nasarian et al., 2024).
Finally, AI’s integration complicates the traditional patient–provider dyad by introducing a third role: the algorithm. Clinical decision support systems (CDSS) risk shifting authority away from patients and even clinicians, especially if algorithmic recommendations dominate decision-making. With that, ethical concerns arise around resource allocation and professional responsibility when AI-driven systems influence care pathways, which emphasizes the need to safeguard autonomy in AI-mediated contexts (Elgin & Elgin, 2024; Khan et al., 2023). This demonstrates that autonomy must be actively protected to ensure AI augments rather than constrains choice.
While AI’s ability to increase efficiency, expand access, and improve diagnostic accuracy is undeniable, these benefits must not come at the expense of empathy, personalization, and patient trust. The four dimensions explored–generalization versus personalization, equity of access, trust and transparency, and patient autonomy versus AI-driven decision-making–illustrate that the real measure of success for AI in healthcare is not technological sophistication alone, but its capacity to strengthen the human connection at the heart of medicine. By embedding equity into design, ensuring transparency, and preserving the role of human judgment, AI can serve as a powerful partner in care rather than a disruptive replacement. The key takeaway is clear: healthcare’s future depends on advancing technology responsibly, with deliberate attention to inclusivity, ethics, and the preservation of patient-provider relationships. Only then can AI fulfill its promise of enhancing the humanity of healthcare.
This research examines how AI impacts the patient–provider relationship, particularly focusing on four central dimensions: generalization vs. personalization of care, equity of access, trust and transparency, and patient autonomy vs. AI-driven decision-making. While AI has the power to boost efficiency, accuracy, and accessibility, it also risks compromising empathy, individualized care, and trust, which are all fundamental components of effective healthcare.
This work emphasizes that the future of AI in healthcare depends on balancing efficiency with the preservation of the human connection between patient and provider. A central advancement is the recognition of tension between generalization and personalization. AI excels at identifying population-level trends and reducing variability through standardized guidelines, which improves efficiency and clinical accuracy (Mutharasan & Walradt, 2024). However, true personalization arises when AI complements rather than replaces clinical judgment–ensuring care remains sensitive to individual cultural, emotional, and contextual needs (Bærøe et al., 2023). This perspective reframes personalization as not only a technical challenge but also an ethical and relational one.
AI-driven platforms, including chatbots and remote monitoring tools, can expand access to underserved populations by mitigating barriers to entry (Gurevich et al., 2022; Sun & Zhou, 2023). However, disparities in digital literacy, broadband access, and biased training datasets risk exacerbating inequities. With programmed biases and a lack of an inclusive design, AI may disproportionately disadvantage marginalized groups who already face systemic barriers to healthcare (Cross et al., 2024). The equity, diversity, and inclusion in artificial intelligence (EDAI) framework demonstrates how equity, diversity, and inclusion (EDI) must be embedded throughout the AI lifecycle, from dataset design to deployment. Without such intentional design, however, inequities in digital literacy, broadband access, and algorithmic bias could exacerbate disparities, particularly for marginalized groups who already face systemic barriers (Abbasgholizadeh Rahimi et al., 2024). This insight advances knowledge by showing that equity is not a byproduct of AI—it must be deliberately built in.
Trust and transparency are equally indispensable. Research on explainable AI (XAI) shows that algorithmic confidence and explainability directly affect user trust and diagnostic performance (Ullah et al., 2024). While explainability fosters confidence, both underreliance and overreliance can emerge, highlighting the delicate balance needed when presenting AI recommendations to providers and patients (Klingbeil et al., 2024; Ni & Jia, 2025). Transparency is vital in healthcare and can be seen through open communication and shared decision-making, which ultimately foster a patient’s trust in their provider (Montgomery et al., 2020; Nasarian et al., 2024).
Finally, AI’s integration complicates the traditional patient–provider dyad by introducing a third role: the algorithm. Clinical decision support systems (CDSS) risk shifting authority away from patients and even clinicians, especially if algorithmic recommendations dominate decision-making. With that, ethical concerns arise around resource allocation and professional responsibility when AI-driven systems influence care pathways, which emphasizes the need to safeguard autonomy in AI-mediated contexts (Elgin & Elgin, 2024; Khan et al., 2023). This demonstrates that autonomy must be actively protected to ensure AI augments rather than constrains choice.
While AI’s ability to increase efficiency, expand access, and improve diagnostic accuracy is undeniable, these benefits must not come at the expense of empathy, personalization, and patient trust. The four dimensions explored–generalization versus personalization, equity of access, trust and transparency, and patient autonomy versus AI-driven decision-making–illustrate that the real measure of success for AI in healthcare is not technological sophistication alone, but its capacity to strengthen the human connection at the heart of medicine. By embedding equity into design, ensuring transparency, and preserving the role of human judgment, AI can serve as a powerful partner in care rather than a disruptive replacement. The key takeaway is clear: healthcare’s future depends on advancing technology responsibly, with deliberate attention to inclusivity, ethics, and the preservation of patient-provider relationships. Only then can AI fulfill its promise of enhancing the humanity of healthcare.
Event Type
Poster Presentation
TimeMonday, March 234:45pm - 6:15pm EDT
LocationRhinelander Gallery
Digital Health