Why in NEWS
Indian researchers have developed Garbhini-GA2, an advanced AI model that predicts fetal age from ultrasound images with an error margin of just half a day — a major improvement over existing models with a 7-day margin. This showcases the expanding role of AI in reshaping India’s healthcare system.
Key Concepts and Terms
| Term | Explanation |
|---|---|
| Garbhini-GA2 | An AI tool developed by Indian researchers to predict fetal gestational age with high accuracy. |
| AI in Healthcare | The use of artificial intelligence to assist in diagnostics, treatment planning, drug discovery, and health monitoring. |
| Ayushman Bharat Digital Mission (ABDM) | Government initiative providing digital health IDs and infrastructure for a national health database. |
| ICMR Ethical Guidelines (2023) | A framework ensuring responsible, equitable, and transparent use of AI in healthcare. |
What’s Happening?
AI is increasingly transforming India’s healthcare landscape — from diagnostics to hospital management. Garbhini-GA2 is a landmark advancement, using AI to read ultrasound images and accurately estimate gestational age, improving prenatal care outcomes. It joins a wave of Indian AI innovations like Qure.ai (TB, pneumonia detection), NIRAMAI (non-invasive breast cancer screening), and BeatO (chronic disease wearables).
Applications of AI in Indian Healthcare
| Domain | Examples/Developments |
|---|---|
| Early Diagnosis | iOncology.ai for cancer detection; Qure.ai for TB and pneumonia; NIRAMAI for radiation-free breast scans. |
| Telemedicine | Practo chatbot, Ask Apollo; AI-based triaging & symptom checkers. |
| Drug Discovery | InnAccel’s SAANS for neonatal care; personalized drug models in clinical labs. |
| Wearables | BeatO glucometers; fitness trackers that offer real-time health advice. |
| Hospital Efficiency | AI in Microsoft’s diabetic retinopathy network; automated records and appointment systems. |
| Medical Education | FundamentalVR offering AI-powered surgical training through virtual simulations. |
Government Support & Digital Infrastructure
| Initiative | Purpose |
|---|---|
| ABDM | Unique health ID for all citizens and integration of health services. |
| PHR & HealthLocker | Cloud-based personal health records. |
| National Health Stack | Digital tools like analytics platforms to enhance public health delivery. |
WHO has also launched S.A.R.A.H., an AI assistant to spread health awareness, marking global recognition of AI’s power in public health.
Major Challenges in AI-Driven Healthcare
| Challenge | Description |
|---|---|
| Data Quality | Lack of digitized and diverse Indian medical records; poor interoperability between systems. |
| Rural Infrastructure | Limited internet connectivity, few digital tools in PHCs; weak adoption of telemedicine platforms. |
| Legal & Ethical Concerns | No specific regulatory framework for AI in healthcare; risks of bias and misuse. |
| Language Barriers | Most AI tools not designed for India’s multilingual environment. |
| Skepticism Among Doctors | Fear of automation replacing clinical judgement; low AI literacy among practitioners. |
ICMR’s Ethical Guidelines for AI in Healthcare (2023)
| Principle | Summary |
|---|---|
| Accountability | Public audits of AI performance. |
| Autonomy | Mandatory human oversight and informed consent. |
| Privacy | Full protection of personal health data. |
| Inclusiveness | Ensure accessibility and fairness across all demographics. |
| Transparency | Explainability of algorithms for clinical trust. |
What Should India Do Next?
| Recommendation | Strategy |
|---|---|
| Standardized Data Collection | Scale ABDM; encourage data-sharing from top hospitals while ensuring privacy. |
| Rural AI Enablement | Equip ASHA workers with portable AI tools; improve 5G/telecom support in PHCs. |
| Strong Regulatory Framework | Empower CDSCO to certify AI tools; audit for bias; implement DISHA and enforce Digital Data Protection Act. |
| Clinician Training | Integrate AI into medical curricula; conduct national workshops on digital health tools. |
| Mass Awareness | Use media to build trust in AI, similar to Pulse Polio campaigns. |
In a nutshell (Mnemonic):
“H-E-A-L-T-H”
H – High-accuracy models like Garbhini-GA2
E – Early detection through AI scans
A – Accessible care in rural areas via AI
L – Localized datasets for better relevance
T – Trained doctors in AI ethics & tools
H – Healthcare equality through digital inclusion
Prelims Questions
- Which of the following is correctly matched?
A. Garbhini-GA2 – Cancer Screening
B. NIRAMAI – Thermal Imaging for Breast Cancer
C. iOncology.ai – Tuberculosis Detection
D. SAANS – Device for Cardiovascular Treatment - Consider the following statements about ICMR’s Ethical AI Guidelines:
- They mandate human oversight in AI diagnosis.
- They encourage international collaboration.
- They prohibit AI usage in public hospitals.
Which of the above are correct?
A. 1 and 2 only
B. 2 and 3 only
C. 1 and 3 only
D. 1, 2, and 3
- Which of the following is not an application of AI in Indian healthcare?
A. AI-enabled drug formulation
B. Virtual surgical simulations
C. Manual data entry in hospital registers
D. Symptom-checking chatbots
Mains Questions
- “AI-based technologies are redefining the future of healthcare delivery in India.” Discuss with examples. (GS-3: Science & Technology)
- What are the major ethical concerns associated with the use of AI in healthcare? Evaluate India’s preparedness to deal with them. (GS-2: Governance)
Prelims Answers and Explanations
| Qn | Answer | Explanation |
|---|---|---|
| 1 | B | NIRAMAI uses thermal imaging to detect breast cancer non-invasively. |
| 2 | A | ICMR emphasizes human oversight and collaboration; AI is allowed in public hospitals. |
| 3 | C | Manual data entry is not an AI application; it reflects pre-digital systems. |
