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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Ethics, Integrity & Aptitude
- 1 Philosophy and Contribution of Major Indian Thinkers and Leaders (The philosophies of these thinkers are used for quote-based answers and value-based analysis in the Ethics paper.)
- 2 Essence, Determinants, and Consequences of Ethics
- 3 Ethics in Human Actions & its Dimensions
- 4 Ethics in Private and Public Relationships
- 5 Human Values – Lessons from the Lives of Great Leaders, Reformers, and Administrators
- 6 Human Values
- 7 Notable Quotes from Prominent Indian Thinkers and Leaders
- 8 Philosophy and Contribution of Global Thinkers and Leaders
- 9 Moral Intuition vs. Moral Reasoning
- 10 Empathy, Tolerance, and Compassion towards Weaker Sections
