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Addressing Bias in AI Healthcare: Protecting Vulnerable Patient Populations
by Javaid Sofi Abstract AI’s integration into healthcare, while promising enhanced diagnostics and efficiency, carries a significant risk of amplifying health disparities affecting vulnerable populations. Bias arises from unrepresentative datasets (e.g., dermatology algorithms trained on lighter skin tones), cognitive biases in clinical labelling, and flawed proxies like zip codes conflating geography with health risk. Studies…
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Patient Centric Cybersecurity – Excerpt
Over the last several decades there has been a shift in standard models of healthcare both in the United States and globally. Patient centric approaches in health care reorient the power relationships of physicians and patients. This shift elevates the needs and challenges of the patients and builds a more robust and communicative relationship…