Ms.R.Vani|Asst.Prof|CS|Case Study|Ethical AI in Healthcare and it's principles

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The integration of Artificial Intelligence (AI) in healthcare has the potential to revolutionize patient care, diagnostics, and treatment. However, the ethical challenges associated with its deployment are significant. This case study explores the ethical considerations of AI in healthcare by examining the development and deployment of an AI diagnostic tool.
AI Diagnostic Tool for Radiology
Background: A tech company developed an AI-powered tool designed to assist radiologists in detecting anomalies in medical images such as X-rays and MRIs. The AI was trained on a large dataset of images and was intended to reduce diagnostic errors, speed up the review process, and ultimately improve patient outcomes.
Deployment: The AI tool was deployed in several hospitals as a decision-support system, where it provided radiologists with recommendations based on image analysis. Radiologists would review the AI's suggestions and make final decisions on diagnoses.
Ethical Concerns:
Bias in Data and Algorithms:
The AI system was trained predominantly on data from urban hospitals with a specific demographic profile. As a result, when deployed in rural hospitals with different patient demographics, the AI’s accuracy decreased, leading to concerns about bias.
Ethical Principle: Fairness and Equity-AI systems must be trained on diverse datasets to avoid biases that could lead to unequal treatment of patients from different backgrounds.
Transparency and Explainability:
The AI tool provided diagnostic recommendations but lacked transparency in how it arrived at its conclusions. Radiologists found it challenging to trust the AI's decisions without understanding the underlying logic.
Ethical Principle: Explainability-AI systems in healthcare must be designed to provide clear, understandable explanations for their recommendations to allow healthcare professionals to make informed decisions.