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Use of Artificial Intelligence in Medicine

GS Paper 3

 Syllabus: Science and Technology

 

Source: PIB, BBC 

Context: The Ministry of Ayush in India is leading an effort to integrate traditional medicine with artificial intelligence (AI) through the Ayush Grid, a comprehensive IT backbone for the traditional medicine sector.

 

What is Artificial Intelligence?

 Artificial intelligence (AI) refers to computer programs that can learn from and make decisions based on data. These programs are designed to perform tasks that typically require human intelligence, such as recognizing patterns, analysing images, and making predictions.

 

How can AI be used in Medicine?

Use Case Example
Diagnostics Using machine learning algorithms to analyse medical images and predict the likelihood of certain conditions, such as cancer or heart disease
Drug Discovery Using natural language processing algorithms to analyse scientific papers and identify potential drug targets e.g., a vaccine against COVID-19
Clinical Decision Support To analyse patient data and recommend the most effective treatments based on the patient’s medical history
Telemedicine Using chatbots or virtual assistants to provide patients with personalized medical advice and support
Predictive Analytics To analyse patient data and predict the likelihood of certain outcomes, such as hospital readmissions or disease progression
Robotic Surgery To control robotic surgical instruments and improve surgical precision
Personalised Medicine E.g., Israeli health-tech firm Genetika+ is using stem cell technology and artificial intelligence (AI) software to match antidepressants to patients and minimise side effects.
Traditional Medicine E.g., The Ayush Grid aims to transform the Ayush sector using AI to provide efficient, holistic, affordable, and quality services to all through a secure and interoperable digital ecosystem

 

Issues with the use of AI in Medicine:

Issue Example
Lack of Diversity in Data E.g., an algorithm trained only on data from white male patients may not perform well on women or people of colour.
Bias in Data E.g., an algorithm trained on data that includes racial biases may end up perpetuating those biases.
Safety and Reliability AI algorithms must be reliable and safe for use in healthcare settings.
Interpretability It is often difficult to interpret how an AI algorithm arrived at its decision. This is known as the “black box” problem. In medical settings, this can be a concern because doctors need to understand the reasoning behind a diagnosis or treatment recommendation.
Privacy and Security AI algorithms must be designed with privacy and security in mind to prevent unauthorized access or disclosure of patient information.

 

The government programme for the promotion of AI in Healthcare:

  • Ayushman Bharat Digital India Mission
  • IndiGen Programme (for genome sequencing of Indians)
  • Human Genome Project
  • Health Stack
  • ICMR guideline of use of AI in Healthcare
  • AIRAWAT (AI Research, Analytics and Knowledge Assimilation platform): India’s first AI-specific cloud computing infrastructure

 

Conclusion:

Despite the challenges, the future of AI in medicine looks promising, and with continued research and development, we can expect to see even more innovative and effective applications of AI in healthcare in the years to come.

 

About Ayush Grid:

AYUSH Grid (by Ministry of AYUSH) aims to bring on onboard all AYUSH (Ayurveda, Yoga and Naturopathy, Unani, Siddha and Homoeopathy) facilities including hospitals and laboratories and to promote traditional systems of healthcare.

 

Insta Links:

ICMR guideline of use of AI in Healthcare

 

Mains Links

Discuss the applications of Artificial Intelligence in the Healthcare sector in India. (250 Words)

 

Prelims Links

Q. With reference to agriculture in India, how can the technique of ‘genome sequencing’, often seen in the news, be used in the immediate future?
  1. Genome sequencing can be used to identify genetic markers for disease resistance and drought tolerance in various crop plants.
  2. This technique helps in reducing the time required to develop new varieties of crop plants.
  3. It can be used to decipher the host-pathogen relationships in crops

 

Select the correct answer using the code given below:

(a) 1 only

(b) 2 and 3 only

(c) 1 and 3 only

(d) 1, 2 and 3

 

Answer: D