Artificial intelligence has several applications in health care, including early cancer detection through analysis of medical scans, health care chatbots, and robot assisted surgery. While around $632 million has been invested in AI startups in India since 2012, predominantly by companies using AI, there are also challenges to wider adoption of AI in health care. Key challenges include lack of data from Indian health systems and limited computing power at most Indian health companies. There are also risks that AI could worsen healthcare access and affordability in rural areas of India.

1. ARTIFICIAL
INTELLIGENCE
IN
HEALTH CARE

2. APPLICATION OF ARTIFICIAL INTELLIGENCE

3. ARTIFICIAL INTELLIGENCE IN HEALTH

4. TIMELINE OF AI IN HEALTH

5. ACCEPTANCE OF AI IN THE WORLD

6. ACCEPTANCE OF AI IN THE WORLD

7. RESEARCHES IN AI
• YAHA PE COUTRIES NE KYA KYA RESEARCH KARI HE WO LIKHNA HE

8. APPLICATOINS OF AI IN HEALTH CARE

9. Early cancer detection
A deep learning algorithm trained to analyze
images from MRI scans predicts the presence of
an IDH1 gene mutation in brain tumors.

10. HEALTH CARE CHATBOTS

11. ROBOT ASSISTED SURGERY
During robot-assisted heart surgery,
a surgeon works at a remote
console controlling the robotic
instruments, which use small,
precise movements to perform the
surgery

12. DEVELOPMENT OF NEW MEDICIDNES

13. USE OF AI IN INDIA
Since 2012, about 1200 crores has been invested in AI start-ups
in India, raised predominantly by companies that use AI. Of this,
around 632 croreswas raised in 2017 alone.

14. CHALLENGES THAT WE ARE FACING
• The main sources of data for developing these systems are primarily
historical data held within research institutions, non-profit
organizations and medical service providers. In some cases, data are
collected by developers through other platforms or healthcare
services they already provide.
• Most Indian healthcare companies do not have the computing
power to build AI systems, and rely on cloud services provided
by the big tech players – Google and Microsoft in particular.

15. RISKS INVOLVED
• In contrast to the promise of improving access to quality healthcare
for underserved populations, there is a risk that affordability and
the lack of a sustainable business case for AI in rural healthcare
could be severe constraints for adoption.
• Further concerns arise on account of the linking of health data with
the controversial biometric identity project Aadhaar, which has
already been documented as suffering multiple privacy and security
breaches.

16. • CASE STUDY