Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?
Approach · directive: “introduce / how / do you perceive”
What it asks · Define AI briefly, show how it improves clinical diagnosis, and weigh the privacy risks of using health data, with a clear personal view.
It asks 3 things — answer each
- Introduce the concept of Artificial Intelligence
- Explain how AI helps clinical diagnosis
- Give a clear view on whether AI in healthcare threatens individual privacy, with the safeguards needed
Open with · Artificial Intelligence is the ability of machines to learn from data and perform tasks that need human intelligence: recognising patterns, predicting and deciding; unlike rule-based software, machine-learning models improve with more data, which is both their promise and their privacy risk in medicine.
Cover
- Diagnosis: models read X-rays, CT, retinal and pathology images to flag TB, cancers and diabetic retinopathy; risk prediction from records; decision support where specialists are scarce.
- Privacy threat, yes: the November 2022 attack on AIIMS Delhi's servers; re-identification; commercial profiling; uninformed consent.
- Allied harms: bias from unrepresentative data, unclear liability; ICMR (2023) calls patient privacy and confidentiality 'of paramount concerns'.
- Safeguards: DPDP Act 2023 consent 'free, specific, informed'; ICMR's ten patient-centric principles; ABDM consent-based sharing; audits; doctor's final say.
- Capacity: IndiaAI Mission (2024, over ₹10,300 crore, 10,000+ GPUs) must fund safe and trusted AI.
Close with · AI should be the clinician's second reader, never the sole decider; adopted under consent, security and accountability norms, it can bring specialist-grade diagnosis to every district hospital.
Add value (verified)
- ICMR Ethical Guidelines for AI in Biomedical Research and Healthcare (2023): patient privacy, confidentiality and ethics are of paramount concern.ICMR: Ethical Guidelines for Application of Artificial Intelligence in Biomedical Research and Healthcare, 2023, Preface ↗“Patient privacy, confidentiality and ethics are of paramount concerns for this emerging field of biomedical research and its application.”
- ICMR (2023): ten patient-centric ethical principles for AI in health, including autonomy, data privacy and accountability.ICMR: Ethical Guidelines for Application of Artificial Intelligence in Biomedical Research and Healthcare, 2023, section 1 ↗“The ten ethical principles in Fig 1 addresses issues specific to AI for health. These principles are patient-centric and are expected to guide all the stakeholders in the development and deployment of responsible and reliable AI for health.”
- DPDP Act 2023, section 6(1): consent must be free, specific, informed, unconditional and unambiguous, limited to the specified purpose.Digital Personal Data Protection Act, 2023, section 6(1) (MeitY text) ↗“The consent given by the Data Principal shall be free, specific, informed, unconditional and unambiguous with a clear affirmative action, and shall signify an agreement to the processing of her personal data for the specified purpose and be limited to such personal data as is necessary for such specified purpose.”
- PIB (March 2024): Cabinet approved over ₹10,300 crore for the IndiaAI Mission.PIB release, Ministry of Electronics and IT (March 2024), as published by the Office of the Principal Scientific Adviser ↗“Cabinet Approves Over Rs 10,300 Crore for IndiaAI Mission, will Empower AI Startups and Expand Compute Infrastructure Access”
- PIB (March 2024): IndiaAI Compute Capacity to deploy over 10,000 GPUs through public-private collaboration.PIB release, Ministry of Electronics and IT (March 2024), as published by the Office of the Principal Scientific Adviser ↗“A cornerstone of this effort is the IndiaAI Compute Capacity, envisioned to erect a cutting-edge, scalable AI computing infrastructure by deploying over 10,000 Graphics Processing Units (GPUs) through strategic public-private collaborations.”
Question: UPSC's CS (Main) 2023, GS Paper III — paper ↗. Approach: Minimalist IAS, checked 3 Oct 2026 (how we verify) — UPSC publishes no model answers. ·
Model answer· 222words · Minimalist IAS
Artificial Intelligence is the ability of machines to learn from data and do tasks needing human intelligence: recognising patterns, predicting, deciding. More data makes models better, their promise and their privacy risk in medicine.
How AI helps clinical diagnosis
- Imaging: models read X-rays, CT, retinal and pathology images to flag tuberculosis, cancers and diabetic retinopathy early.
- Risk prediction: algorithms mine patient records to predict complications and prompt timely care.
- Reach: decision support where specialists are scarce; faster, cheaper triage.
- Health data
- model
- diagnostic flag
- doctor's decision
- audit
Threat to privacy: yes
- Breach risk: the November 2022 attack on AIIMS Delhi's servers showed how exposed health data is.
- Re-identification and misuse: anonymised data can be re-identified; insurers or advertisers may profile patients; consent is often uninformed.
- Allied harms: bias from unrepresentative data, unclear liability; ICMR's 2023 guidelines call patient privacy and confidentiality 'of paramount concerns'.
Safeguards
- Law: the DPDP Act 2023 requires consent 'free, specific, informed, unconditional and unambiguous', limited to the stated purpose.
- Norms: ICMR's ten patient-centric principles, consent-based sharing under the Ayushman Bharat Digital Mission, a doctor's final say.
- Capacity: the IndiaAI Mission (2024, over ₹10,300 crore, 10,000-plus GPUs) must fund safe and trusted AI.
AI should be the clinician's second reader, never the sole decider; under consent, security and accountability norms it can bring specialist-grade diagnosis to every district hospital.
Prepared by Minimalist IAS to the answer standard of Shivam Singh (mentor, 7 years) · facts checked against official sources (how we verify) · updated 3 Oct 2026. UPSC publishes no model answers: compare your structure and coverage with this, then write your own.
