Jojy Cheriyan MD; PhD; MPH; MPhil

The adoption of artificial intelligence (AI) in healthcare faces numerous challenges beyond financial burdens and questions about return on investment. One of the biggest challenges of utilizing AI in medicine is that physicians are reluctant to trust and adopt something they do not fully understand and are regarded as a “black box”. Patients, too, share same concerns, including those related to accuracy, transparency, cost, bias, and privacy.
The healthcare industry has historically lagged behind other sectors in adopting new technologies, including AI, due to a variety of complex factors: high costs of technology and implementation, regulatory and compliance barriers, the diverse nature of healthcare data, migration from legacy systems, data privacy and security concerns, cultural resistance, lack of standardization, and ethical and legal uncertainties. Most critically, both patient and physician trust and perception are significant barriers to widespread adoption.

A Patient-Centered Approach
The integration of AI into healthcare is rapidly transforming patient care and physician workflows. Yet, healthcare is inherently personal, and no technological advancement should replace the compassionate, human touch that defines quality patient care.
A patient-centered approach to AI can enhance the human element by offering personalized treatment plans, improving access to care, and bolstering preventive healthcare. These improvements can foster trust and help bridge the gap between patients and physicians, as AI complements—rather than replaces—the essential human interaction.

Reducing Physician Burnout
Physician burnout is an increasing concern, with many doctors facing overwhelming workloads, administrative burdens, and emotional exhaustion. AI has the potential to alleviate some of these pressures by automating administrative tasks, optimizing clinical decision support, and improving the balance between work and life. By mitigating the risk of human error, AI can boost physicians’ confidence and reduce liability concerns.
By freeing up doctors’ time, AI enables them to focus more on patient care. It reduces their cognitive load, enabling faster and more informed decisions without fear of missing critical information. With less time spent on paperwork, physicians can derive greater satisfaction from their work, reducing burnout and building trust among patients who benefit from more thoughtful interactions and better explanations of AI’s role in their care.

Addressing Bias in AI Algorithms
AI algorithms are only as good as the data they are trained on. If these datasets are not diverse or representative of various populations, AI systems can unintentionally perpetuate or amplify biases in healthcare. For instance, AI systems trained primarily on data from certain racial or ethnic groups may fail to accurately predict outcomes for other populations, leading to unequal treatment or misdiagnoses.
Bias in AI can exacerbate existing healthcare disparities, resulting in poorer outcomes for marginalized groups and further eroding trust in healthcare systems. Therefore, ensuring that AI models are trained on diverse, representative datasets is critical to reducing bias and promoting equitable care.

Ethical Concerns
The use of AI in healthcare raises several ethical questions, particularly around decision-making, accountability, and the potential for dehumanizing care. Who is responsible when an AI system makes an incorrect recommendation that leads to harm? How should healthcare providers balance AI input with their clinical judgment, especially in life-or-death situations?
Over-reliance on AI could diminish human oversight, with serious consequences if clinicians become too dependent on its recommendations without thoroughly questioning them. Ethical concerns such as these must be carefully addressed to ensure that AI enhances care rather than compromising it.

Conclusion
While the conversation around AI often focuses on its technological potential, it is essential to remember the core of healthcare: the patient and the doctor. By leveraging AI to improve patient outcomes and alleviate the pressures on physicians, we can build a more sustainable, patient-centered model of care.
However, AI must be implemented thoughtfully to avoid introducing new challenges. Poorly designed systems, complex workflows, or inadequate training can increase cognitive load and frustration among healthcare providers. In some cases, rather than reducing burnout, AI could worsen it if doctors are forced to spend more time troubleshooting or adapting to new systems.
Thus, the focus should be on integrating AI in a way that supports and enhances the healthcare process, ultimately building trust among both patients and physicians.

References:

1. Poon, AIF., & Sung, JJY. (2021). Opening the black box of AI-Medicine. Journal of Gastroenterology and Hepatology, 36(3), 581-584.
2. Chaudhry, B., Wang, J., Wu, S., et al. (2006). Systematic review: Impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine, 144(10), 742-752.
3. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
4. Jiang, F., Jiang, Y., Zhi, H., et al. (2017). Artificial intelligence in healthcare: Past, present, and future. Stroke and Vascular Neurology, 2(4), 230-243.
5. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.
6. Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 1-9.

The content provided on this blog, “Medical and Healthcare Insights,” is for informational purposes only and is not intended as a substitute for professional medical advice, diagnosis, or treatment. The views and opinions expressed in the blog posts are those of the author and do not necessarily reflect the official policy or position of any healthcare institution, organization, or employer. Readers are encouraged to consult with qualified healthcare professionals for any health-related questions or concerns. The author and the blog are not responsible for any errors or omissions, or for any outcomes related to the use of this information. Use of this blog and its content is at your own risk.