article · Journal of Medicine Surgery and Public Health
Artificial intelligence is rapidly altering patient care by delivering improvements in diagnostics, operational efficiency, and clinical decision-making. Despite these benefits, relying heavily on data-driven systems introduces risks of depersonalising medicine and weakening the doctor-patient relationship. The lack of transparency in black-box algorithms can reduce patient trust, whilst systems trained on biased data threaten to exacerbate disparities for underrepresented groups. Although these technologies can relieve clinicians of routine burdens, automated tools risk sidelining the empathy and interpersonal connection fundamental to medical treatment. Future development must therefore prioritise solutions designed to support and augment compassionate clinical care rather than replace human practitioners, ensuring technical advances remain aligned with core medical values.
As healthcare systems adopt automated technologies to improve efficiency, patients risk losing the human empathy and trust vital to effective healing. Identifying the risks of algorithmic bias, opacity, and depersonalisation helps clinicians, developers, and policymakers implement digital tools that preserve the core interpersonal values of medical practice whilst protecting vulnerable and underrepresented groups.
The work offers conceptual design guidance for software developers and health technology vendors building clinical decision-support and diagnostic tools. It highlights market demand for explainable, transparent, and bias-audited algorithms that assist rather than displace clinicians. Because the abstract provides high-level ethical and design recommendations rather than a tested product, these considerations remain at an early, pre-development stage for system architects.
AI-generated from the published abstract. Always read the original work before citing.
The integration of artificial intelligence (AI) into healthcare is rapidly transforming patient care, offering numerous advantages in diagnostics, efficiency, and clinical decision-making. However, this technological shift raises significant concerns about the potential erosion of the doctor-patient relationship, a cornerstone of effective medical practice. AI’s increasing role risks depersonalizing healthcare, as the emphasis on data-driven decisions may overshadow the empathy, trust, and personalized care traditionally provided by human clinicians. The "black-box" nature of AI algorithms further exacerbates this issue, as the lack of transparency in AI decision-making processes can undermine patient trust. Additionally, AI systems trained on biased datasets may inadvertently widen health disparities, particularly for underrepresented populations. While AI has the potential to streamline routine tasks and reduce the burden on healthcare providers, it is essential to ensure that these advancements do not come at the cost of the human connection vital to patient care. To address these challenges, future research and development should focus on creating AI systems that enhance, rather than replace, the compassionate aspects of healthcare. This balanced approach is crucial to preserving the integrity of the doctor-patient relationship while harnessing the benefits of AI, ultimately ensuring that technological progress aligns with the core values of medical practice.
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DOI: 10.1016/j.glmedi.2024.100138
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