Overview
Analyze one application of AI in healthcare and evaluate its impact on clinical decision making or client care outcomes in practice.
One application of AI in healthcare is AI-assisted diabetic retinopathy screening. AI can analyze retinal images and identify signs of diabetic eye disease or patients who may need further evaluation. This application can improve clinical decision making by giving providers rapid information on patients needing a referral to an eye specialist. Early detection can support faster treatment.
Explain one risk or challenge of using AI in healthcare, such as bias, privacy, or overreliance on technology.
One challenge of using AI in healthcare is bias and inaccurate results. AI systems learn patterns from data used to develop them. If AI is not trained using diverse patient populations, it may not work equally well for all patients. There is also a risk that staff may rely too heavily on AI instead of using their own clinical knowledge leading to incorrect diagnosis or treatment.
Evaluate how the integration of AI into healthcare systems influences health equity, access to care, or client outcomes across diverse populations, and propose one strategy to promote equitable and ethical use of AI in practice.
With the use of this AI application, patients have improved access to eye screenings, especially patients who have limited access to specialists. To promote equitable care, healthcare settings need to regularly evaluate AI accuracy across diverse populations and require healthcare professional to review AI results before making clinical care decisions.