This article highlights the potential of AI in solving India’s healthcare challenges while emphasizing the need to balance technological advancements with ethical considerations, human empathy and fundamental reforms in the healthcare system.
India’s healthcare system suffers from some major challenges including inadequate infrastructure, shortage of health professionals and unequal access of people to quality healthcare. In recent years there has been growing interest in leveraging artificial intelligence (AI) to address these gaps in healthcare. AI technologies are pivotal in increasing efficiency, improving access to medical facilities, and potentially revolutionizing healthcare delivery in a country where resources are often scarce.
However the integration of AI in healthcare (especially in a country like India) raises important questions about feasibility, sustainability and ethical implications. AI is excellent at processing data and automating repetitive tasks but it lacks important human qualities needed in healthcare, such as empathy, cultural understanding and the ability to understand patient situations. India’s healthcare must explore the potential of AI, carefully understand its potential benefits, and develop comprehensive regulations to ensure that AI tools “do no harm” while adhering to basic medical ethics.
What Is The Importance Of AI In Healthcare?
1. Revolutionizing Diagnosis: Artificial intelligence is transforming medical diagnosis with unprecedented accuracy and speed.
- AI algorithms in radiology can detect subtle abnormalities that cannot be seen by the human eye.
- For example, a study published in Nature in 2020 showed that AI systems resulted in a 1.2% and 2.7% reduction in the rates of false-positive and false-negative identification errors related to biopsy-confirmed breast cancer.
- As AI continues to develop, it is likely to improve diagnostic accuracy across a variety of medical fields, from ophthalmology to pathology.
2. Personalized treatment plans: AI is ushering in the era of precision medicine by analyzing large amounts of patient data to create personalized treatment plans.
- By considering a person’s genetic makeup, lifestyle factors, and medical history, AI could lead to targeted treatments with higher efficacy and fewer side effects.
- For example, IBM Watson Oncology is used in more than 230 hospitals worldwide to help oncologists develop personalized cancer treatment plans.
- This tailored approach not only improves patient outcomes but also optimizes resource allocation across health care systems.
3. Drug discovery and development: AI is fueling the drug discovery and development process, bringing potentially life-saving drugs to market faster and at lower costs.
- Machine learning algorithms can analyze biological data, predict target-drug interactions, and optimize molecular structures, significantly reducing the time and resources required for early-stage drug discovery.
- In 2020, Insilico Medicine used AI to design, synthesize, and validate a new drug for fibrosis in just 46 days. Traditionally such a process takes years.
4. Speeding up the clinical system: AI is streamlining the clinical system, reducing administrative burden and allowing healthcare professionals to focus more on patient care.
- Natural language processing (NALP) algorithms can automatically transcribe and summarize doctor-patient conversations, update electronic health records, and prepare clinical notes.
- Additionally, AI-powered scheduling systems can improve patient accessibility, reduce wait times, and improve resource allocation in hospitals.
5. Remote monitoring and telemedicine: AI is playing a significant role in expanding healthcare access through remote monitoring and telemedicine solutions.
- AI-powered wearables and IoT devices can monitor patient vital signs, detect anomalies, and alert healthcare providers to potential problems.
- The use of AI in telemedicine increased during the COVID-19 pandemic, with platforms like Babylon Health using AI chatbots to prioritize patients and provide initial consultations.
- This technology is especially important in rural and backward areas, where access to experts is limited.
- WHO’s SARA is a digital health promoter prototype available 24/7 in eight languages via video or text.
- Along with relieving stress and promoting proper eating habits, she can also provide information on many other health topics, including giving suggestions for quitting tobacco and e-cigarettes.
- However, it is not suitable for giving medical advice.
6. Boosting medical education and training: AI is revolutionizing medical education and training by providing personalized learning experiences and simulating complex clinical scenarios.
- Virtual reality (VR) and augmented reality (AR) platforms powered by AI can create immersive training environments for medical students and professionals.
- For example, companies like FundamentalVR offer AI-powered haptic VR systems that allow surgeons to practice better procedures with realistic feedback.
- AI-powered adaptive learning systems can tailor medical curricula to the needs of individual students, accelerating the learning process and creating more competent health care professionals.
What Are The Major Challenges Related To AI In India’s Healthcare Sector?
1. Infrastructure limitations: India’s healthcare infrastructure faces major constraints that are challenging the widespread adoption of AI technologies.
- Many health care facilities (especially in rural and peri-urban areas) lack the basic technical infrastructure needed to support AI systems.
- A recent study states that out of 7,821 health and wellness centers in rural India, only 3,496 (45%) have electricity back-up facilities.
- This infrastructural gap makes it difficult to implement and maintain sophisticated AI systems
2. Data Challenges: India faces constraints in the availability and quality of health care data needed for training effective AI models.
- A fragmented health care system with both public and private providers results in inconsistent data collection practices.
- Electronic health records (EHR) are maintained in many health centers in India but there are no provisions for integrating this data for analysis, nor are there any clear guidelines on how long health records should be kept.
- This problem is further exacerbated by issues related to data quality, standardization and interoperability.
3. Digital Divide: The digital divide in India is a major barrier to equitable implementation of AI in healthcare.
- Urban centers can benefit from AI-powered health care but rural areas often lack the necessary digital infrastructure.
- According to a joint study by Internet and Mobile Association of India (IAMAI) and Kantar, 45% of the Indian population will not have access to the internet by the year 2023.
- This disparity in digital access means that AI in healthcare may benefit predominantly urban populations, potentially widening the existing healthcare gap.
4. Regulatory barriers: The lack of comprehensive regulations specifically addressing AI in healthcare is a major challenge in India.
- Although the Digital Information Security in Healthcare Act (DISHA) was proposed in 2017 by the Ministry of Health and Family Welfare to regulate digital health data, it has not been enacted yet.
- This regulatory void creates uncertainty for AI developers and healthcare providers, potentially slowing the process of innovation.
- The lack of clear guidelines on issues such as AI algorithm validation, liability in case of AI errors, and patient data protection poses risks to patients and healthcare providers.
5. Ethical and cultural considerations: Implementing AI in healthcare in India poses complex ethical and cultural challenges due to the country’s wide diversity.
- Issues such as algorithmic bias, informed consent and privacy, and multicultural and multilingual societies with varying levels of health literacy add additional dimensions.
- The AI algorithms used in the Indian health care system (which are trained primarily on datasets from Western countries) raise concerns about usability for Indians.
- Health issues and cultural sensitivities related to data sharing also present challenges.
6. Cost and resource allocation: The high costs associated with the development, implementation, and maintenance of AI systems in healthcare are a major challenge for India’s resource-poor health sector.
- Although AI has the potential for long-term cost savings, the initial investment can be quite high.
- The average cost of adopting AI in healthcare is between US$20,000 to 1,000,000, which is a very high amount for many healthcare providers.
- This cost is particularly challenging as India’s healthcare expenditure was only 1.8% of its GDP in 2020-21.
7. Language and localization issues: India’s linguistic diversity poses a major challenge for AI implementation in healthcare.
- With 22 official languages and hundreds of dialects, building an AI system that can effectively communicate with and understand patients across the country is a complex task.
- This language barrier can reduce the effectiveness of AI tools with misdiagnosis, miscommunication.
ICMR Guidelines For Use Of AI In Health Sector
In March 2023, the Indian Council of Medical Research (ICMR) released “Ethical Guidelines for the Application of AI in Biomedical Research and Healthcare”, outlining 10 key patient-centric ethical principles for the use of AI in healthcare.
Guiding Principles:
- Accountability and accountability: Regular audits ensure best AI practices are available to the public.
- Autonomy: This requires manual monitoring, requires patient consent and is informed about the risks.
- Data Privacy: AI must protect privacy and personal data at every level.
- Collaboration: Fostering interdisciplinary, international partnerships.
- Security and risk mitigation: Preventing misuse, ensuring data security and emphasizing the need for ethics committee assessment.
- Access, Equity and Inclusivity: It aims to bridge the digital divide by ensuring access to AI infrastructure.
- Data optimization: Resolving biases and errors caused by poor data quality or representation.
- Non-discrimination and fairness: ensuring universal, bias-free AI technology.
- Trustworthiness: AI must be legitimate, trustworthy, and ethical to gain user trust.
- Transparency: The need for systematic ways for practitioners to test the validity and reliability of AI.
Frameworks: India’s frameworks supporting AI in healthcare include Digital Health Authority under the National Health Policy (2017), DISHA, 2018 and Medical Devices Rules, 2017.
How Can India Effectively Implement AI In Healthcare?
1. Strengthening the National Health Resources Database: India can enhance its National Health Resources Repository (NHRR) by incorporating advanced AI technologies.
- By integrating the National Digital Health Mission with AI-ready data protocols through NHRR, India can build a robust AI healthcare model.
- The success of Estonia’s e-health system, which covers 95% of the population’s health data, demonstrates the feasibility of this approach.
2. Developing India-specific AI models: To address the challenge of AI models not being suitable for the Indian population, the government can collaborate with academic institutions and technology companies to develop India-specific AI models.
- These models should be trained on diverse Indian datasets considering factors such as genetic diversity, regional disease patterns and socio-economic determinants of health.
- For example, researchers at IIT-Delhi have developed AI-based detectors for malaria, TB, cervical cancer.
- The government may set up an “AI for Indian Healthcare” challenge, similar to the successful Google AI program for social welfare, inviting researchers and startups to develop solutions tailored to India’s unique healthcare challenges.
3. Creating a tiered AI implementation strategy: India can adopt a tiered AI implementation strategy to reduce the digital divide.
- Advanced AI systems can be implemented in tertiary care hospitals for diagnosis and treatment planning in urban areas with better infrastructure.
- Rural areas should initially focus on adopting simpler, more robust AI tools that can work with limited connectivity, such as AI-powered mobile apps for basic health checkups or telemedicine platforms with offline capabilities.
- For example, the ‘NITI Aayog AI for All’ initiative could be expanded to include health-specific programs for rural areas.
- The success of the Aarogya Setu app demonstrates the potential for widespread adoption of mobile health technologies in India.
4. Establishing a regulatory sandbox for healthcare AI: To overcome regulatory barriers, India can create a ‘regulatory sandbox’ for healthcare AI to allow controlled testing of AI solutions in real-world conditions under regulatory supervision.
- This approach will help develop appropriate regulation while promoting innovation.
- The sandbox can be modeled on the Fintech Sandbox of the Reserve Bank of India, which has successfully developed many innovative financial solutions.
- Sandboxes for healthcare AI may initially focus on non-critical areas such as administrative processes or low-risk clinical devices.
- The Indian Council of Medical Research (ICMR) can maintain this sandbox and collaborate with technology companies and hospitals to test AI solutions before their wider deployment.
5. Integrating AI education into medical curriculum: India should integrate AI and data science modules into medical and nursing education curriculum to address the skills gap.
- This may include mandatory courses on AI in healthcare, practical training on AI tools, and internships with health-tech companies.
- Additionally, the government can partner with online learning platforms to provide certified AI courses in healthcare for practicing professionals.
- The success of initiatives such as Stanford University’s AI in Healthcare online course demonstrates the potential of this approach.
6. Establishing ethical guidelines for AI in healthcare: To address ethical concerns, India should develop comprehensive ethical guidelines for AI in healthcare, considering its unique cultural and social context.
- These guidelines should include issues such as data privacy, algorithmic bias, and the role of AI in clinical decision making.
- The government may set up an AI ethics committee under the Ministry of Health and Family Welfare that will include medical professionals, ethicists, AI experts and patient advocates.
- This committee can take inspiration from the European Commission’s ethical guidelines for trustworthy AI and adapt them to the Indian context.
7. Building AI-ready healthcare infrastructure: India needs to focus on building AI-ready infrastructure in healthcare facilities.
- This includes ensuring stable power supply, strong internet connectivity and essential hardware in healthcare centres.
- The government can leverage existing schemes like the National Rural Health Mission to include upgrading of digital infrastructure.
- For example, the successful implementation of solar powered primary health centers in Chhattisgarh, which ensured 24/7 electricity. It can also be expanded by including digital infrastructure.
8. Launching a public awareness campaign: To address the challenge of patient trust and acceptance, India should launch a massive public awareness campaign about AI in healthcare.
- These campaigns should focus on explaining the benefits and limitations of AI in simple and accessible terms.
- Various media channels should be used, including social media, television and community outreach programs.
- For example, the success of the Pulse Polio campaign used celebrity endorsement and grassroots mobilization, could be a model for AI awareness.
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