AI Ethics in Healthcare
Challenges
- Biased Data: The data used to train AI may be biased, generating misleading or inaccurate information that could pose risks to health, equity and inclusiveness;
- Incorrect Data: Large Language Model (LLMs) tools generate responses that can appear authoritative and plausible to an end user; however, these responses may be completely incorrect or contain serious errors, especially for health-related responses.
- Integration Issues: Ethical issues w.r.t., machines self-operating humans, plus, reluctance among medical practitioners to adopt AI and fear of AI replacing humans are some of the common concerns.
- Data Privacy and Security: Mobile health applications and devices are now using AI and ....
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