Health Care AI Concerns![](https://libapps.s3.amazonaws.com/accounts/215932/images/icon_trust_me_robot.png)
Errors: AI systems may make mistakes, leading to incorrect diagnoses or treatment recommendations, which could potentially harm patients. Liability resulting from inaccuracies or misuse is an evolving concern.
User Privacy: Information you enter into many chatbots, including your personal information may be used unless you opt out. See these example privacy policy and terms of use from OpenAI.
**Security: Similar to privacy issues, a Gen AI system must continuously defend itself from unauthorized access. More...
Missing the human touch: Some are concerned that increased use of AI could reduce the number of opportunities to have caring and compassionate interactions with patients.
Biased data impacting equity and fairness: AI systems can reflect and perpetuate human biases in healthcare as they are trained on human-generated data. This may lead to unequal treatment or outcomes for certain patient groups. This training method could also impact equity, as the dominance of languages like English in AI models can marginalize Indigenous languages and cultures. More...
Overdependence on technology: Critical thinking is a skill that must be practiced. “Automation bias” occurs when people trust the information that comes out of a machine, bypassing critical thinking. In health care this is an important patient quality and safety issue. Overreliance on AI systems may lead to unthinking acceptance of AI-created information. (See Artificial Intelligence Can Improve Health Care - but Not Without Human Oversight; Pew)
Hallucinations: These occur when a generative AI algorithm creates text or audio that is either nonsensical or appears credible but is factually inaccurate.
Regulatory challenges: When technology leaps forward, as AI is doing now, guidelines and regulations trail far behind. There are legitimate concerns about safety, efficacy, and oversight.
No More Radiologists? Generative AI can locate and measure abnormalities without getting tired. It can analyze images more quickly than a human. Predictions are that Radiologists will have more time to devote to complex cases. Others see this as the thin edge of a wedge leading to job losses in this specialty.