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Expansion in Artificial intelligence will lead to better healthcare in India

  • Category
    Science & Technology
  • Published
    22nd Oct, 2018

Context:

Launching Centre for the Fourth Industrial Revolution, PM Modi has marked that the expansion of Artificial Intelligence will lead to better healthcare, and reduce expenditure on health

Issue

Context:
Launching Centre for the Fourth Industrial Revolution, PM Modi has marked that the expansion of Artificial Intelligence will lead to better healthcare, and reduce expenditure on health

Background:

Artificial intelligence and healthcare:

  • Artificial Intelligence (AI), where computers perform tasks that are usually assumed to require human intelligence, is currently being discussed in nearly every domain of science and engineering.
  • The term broadly refers to computing technologies that resemble processes associated with human intelligence, such as reasoning, learning and adaptation, sensory understanding, and interaction.
  • According to a report, about 86% of healthcare provider organizations, life science companies, and technology vendors to healthcare are using artificial intelligence technology. By 2020, these organizations will spend an average of $54 million on artificial intelligence projects.

Analysis

Role/ Applications of AI in healthcare:

AI has the potential to be used in planning and resource allocation in health and social care services. The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. AI programs have been developed and applied to practices such as diagnosis processes, treatment protocol development, drug development, personalized medicine, and patient monitoring and care. 

  • Medical Research 
    • AI can be used to analyse and identify patterns in large and complex datasets faster and more precisely than has previously been possible.
    • It can also be used to search the scientific literature for relevant studies, and to combine different kinds of data for example, to aid drug discovery.
    • Researchers have developed an AI ‘robot scientist’ called Eve which is designed to make the process of drug discovery faster and more economical.
    • Developing pharmaceuticals through clinical trials can take more than a decade and cost billions of dollars. Making this process faster and cheaper could change the world. Amidst the recent Ebola virus scare, a program powered by AI was used to scan existing medicines that could be redesigned to fight the disease.
  • Clinical Care: AI has the potential to aid the diagnosis of disease. Using AI to analyse clinical data, research publications, and professional guidelines could also help to inform decisions about treatment.
    • Medical imaging
    • Screening for neurological conditions
    • Surgery: Robotic tools controlled by AI have been used in research to carry out specific tasks in keyhole surgery, such as tying knots to close wounds. AI assisted robotics can guide the surgeon’s instrument during a procedure, cutting down the time required to do the surgery and reducing complications
  • Patient And Consumer-Facing Applications 
    • Several apps that use AI to offer personalized health assessments and home care advice are currently on the market.
    • Wearable health trackers – like those from FitBit, Apple, Garmin and others – monitors heart rate and activity levels. They can send alerts to the user to get more exercise and can share this information to doctors (and AI systems) for additional data points on the needs and habits of patients.
  • Precision Medicine: Genetics and genomics look for mutations and links to disease from the information in DNA. With the help of AI, body scans can spot cancer and vascular diseases early and predict the health issues people might face based on their genetics.
  • Diagnosis: It is estimated that more than 80% of the health data is unstructured, making it invisible to current systems, according to a PWC report. Unlike humans, AI decisions are all evidence-based and free of cognitive biases or overconfidence, enabling rapid analysis and vastly reducing even eliminating misdiagnosis.
  • Monitoring of Chronic Conditions: Conditions like diabetes, cholesterol, fertility issues and cardiac heath are managed by regular monitoring and lifestyle changes. Connected POC devices help generate a lot of data about the user’s body parameters. This can be combined with lifestyle information like food habits, exercise, etc., by an AI algorithm to help manage the conditions and adjust dosage of medication.

Indian scenario:

  • AI is an effective measure to tackle challenges like the uneven ratio, making doctors more skilled at their jobs, catering to rural areas for a high-quality healthcare, training doctors and nurses to tackle complex procedures.
  • AI in the healthcare sector in India is potentially developing. According to a report by the CIS India published earlier this year, AI could help add USD 957 billion to the Indian economy by 2035.
  • The adoption of AI in India is being propelled by the likes of Microsoft and a slew of health-tech startups. For instance, Manipal Hospitals, headquartered in Bengaluru, is using IBM Watson for Oncology, a cognitive-computing platform; to assist physicians discover personalized cancer care options, according to an Accenture report. 
  • Chronic diseases:
    • India faces a chronic disease risk burden. It is on its way to becoming the diabetic capital of the world with about 6% of the population diagnosed with the condition.
    • A quarter of the population has high blood pressure or hypertension.
    • many people especially those in the age group of 25 to 40 are also being diagnosed with cardiovascular diseases
    • Thus, prevention and management of chronic diseases is an area where AI-led user engagement solutions can play a vital role,
  • India is extremely short in doctors at all levels, General Physicians to diagnose and help manage chronic conditions to specialists in Pathology and radiology. AI can help the doctors in faster diagnosis allowing them to focus on reviewing the data given by AI algorithms and work on complicated cases that AI cannot handle.
  • AI is capable of solving various healthcare challenges in India. The technological innovation is proving to be beneficial in diagnosis procedure, monitoring of chronic conditions, assisting in robotic surgery, drug discovery etc.
  • The power of AI can also be leveraged to help newer physicians, who don't have much of experience to actually be able to come to the right conclusions.
  • Tackle economic disparity:
    • The focus of most AI-based healthcare initiatives in India has been to extend medical services to traditionally underserved populations in India such as rural areas that do not have the required infrastructure or enough primary physicians, and economically weaker sections of society who may not be able to afford certain medical facilities. Therefore, AI as it is used in healthcare in India appears to be addressing issues of economic disparity rather than widening existing gaps as feared.

Government initiatives:

  • National eHealth Authority (NeHA): It was proposed by the Ministry of Health and Family Welfare in 2015 as an authority to be responsible for the development of an integrated health information system in India. It will be the nodal authority that will develop an integrated health information system along with the application of telemedicine and mobile health by collaborating with various stakeholders.
  • Artificial Intelligence Task Force: The ‘Task Force on AI for India’s Economic Transformation’ was set up by the Ministry of Commerce and Industry in 2017 to explore possibilities to leverage AI for development across various fields.
  • Policy Group on Artificial Intelligence: The Ministry of Electronics and Information Technology has recently formed a policy group to study aspects of AI technology and formulate a policy framework and road map for its adoption.
  • National IPR Policy: It recognizes the potential for innovation that exists in new and emerging technologies and talks about developing novel technology platforms in order to ensure enhanced access to affordable medicines and other healthcare solutions.
  • United States–India Science & Technology Endowment Fund (USISTEF)
  • Biotechnology Ignition Grant Scheme (BIG), Biotechnology Industry Research Assistance Council 
  • Centre of Excellence for Data Science and Artificial Intelligence
  • State governments are also providing support to AI startups with reports quoting the Karnataka government mobilizing 2,000 crore by 2020 towards supporting the same. The Karnataka government also has a Startup Policy and Karnataka Information Technology Venture Capital Fund that can support AI startups.

Concerns with Artificial Intelligence in healthcare

  • Cultural Acceptance: Patients often seek assurance from doctors physically present. This creates aversion to technology diagnosing. Elderlies are found to be more averse to adopting new technology.
  • Data Safety/ Privacy: AI systems can challenge privacy through real time collection and use of a multitude of data points that may or may not be disclosed to an individual in the form of a notice with consent taken. Hackers can exploit AI solutions to collect private and sensitive information such as Electronic Health Records.
  • Liability: In case of error in diagnosis malfunction of a technology, or the use of inaccurate or inappropriate data the question arises of who the liability would fall upon the doctor or the software developer.
  • Ethical issues: Clinical practice often involves complex judgments and abilities that AI currently is unable to replicate, such as contextual knowledge and the ability to read social cues.
    • Reliability and safety: Reliability and safety are key issues where AI is used to control equipment, deliver treatment, or make decisions in healthcare. AI could make errors and, if an error is difficult to detect or has knock-on effects, this could have serious implications.
    • Transparency and accountability: It can be difficult or impossible to determine the underlying logic that generates the outputs produced by AI. Machine learning technologies can be particularly opaque because of the way they continuously tweak their own parameters and rules as they learn. This creates problems for validating the outputs of AI systems, and identifying errors or biases in the data.
    • Data bias, fairness, and equity: Concerns have been raised about the potential of AI to lead to discrimination in ways that may be hidden or which may not align with legally protected characteristics, such as gender, ethnicity, disability, and age.
    • Effects on patients: Concerns have been raised about a loss of human contact and increased social isolation if AI technologies are used to replace staff or family time with patients. 
    • Effects on healthcare professionals: Healthcare professionals may feel that their autonomy and authority is threatened if their expertise is challenged by AI. The ethical obligations of healthcare professionals towards individual patients might be affected by the use of AI decision support systems
    • Malicious use of AI: While AI has the potential to be used for good, it could also be used for malicious purposes. For example, there are fears that AI could be used for covert surveillance or screening. The question of who is responsible when AI is used to support decision-making; difficulties in validating the outputs of AI systems, securing public trust in the development and use of AI technologies etc. are other ethical issues.
  • Issues Specific to India:
  • Data: The obstacle to AI implementation in healthcare is not technological but access to data. Research is hampered by difficulties in accessing large medical datasets, for legal or other reasons. It’s particularly tough for startups in the field as larger players already have access to such data.
    • Development: The lack of robust medical open data sets in India can also hinder the development of AI, as developers must rely on data sets from other countries for their prototypes.
    • Implementation and Adoption:
      • Regulatory Authority: At present, India lacks a Regulating Authority for AI in healthcare. There is also a regulatory gap around medical devices, which has sought to be addressed by the recent Indian Medical Devices Rules, 2017. 
      • Appropriate certification mechanism: One of the biggest issues with the adoption of AI in healthcare in India is acceptability of results, which include direct results arrived at using AI technologies as well as opinions provided by medical practitioners that are influenced/aided by AI technologies.
      • Infrastructure: Though India is working to develop and improve national infrastructure necessary for AI to take off in the country remains ignored by policy makers. Cloud- computing infrastructure, for example, is mostly concentrated in servers outside India. Delays in investing in native infrastructure have resulted in many Indian start-ups incorporating themselves outside India due to easier access to infrastructure and technology.
      • Investment: Investment, though growing, in health related AI in India appears to be currently limited and research is under-funded and explored, especially by the government.
      • Information Asymmetries and Perceptions: AI-based healthcare solutions often face the issue of information asymmetry between the doctors who use the system and the coders who built it.
    • Digitization issues: In many Indian health centers, medical records are still paper, and radiology still uses films
    • The lack of government spending on healthcare means that public health programmes are still largely funded from outside the country. This sometimes results in importing technology rather than fostering the development of indigenously developed locally appropriate inventions.
    • Medical education in India does not place enough emphasis on research and on keeping up with new developments. Combined with an overburdened system, this results in generations of practicing clinicians with little motivation to innovate or to understand and adopt technology.
    • Scaling up and distributing technology in India is challenging

Way Forward 

The government has formulated a seven-point strategy as a framework for the adoption of AI in India. This includes developing methods for human machine interactions, ensuring safety and security of AI systems, creating a competent workforce in line with AI and R&D needs, understanding and addressing the ethical, legal and societal implications of AI, and measuring and evaluating AI technologies through standards and benchmarks, among others.

  • Lessons from international experiences: India can learn from the approaches of the UK and the USA in encouraging the use of AI by also focusing on: 
    • Formulating a Multi-Stakeholder Plan for AI in India: Like China, which has laid down targets for the development of AI in phases, India must also prioritize similar clear milestones and bring in stakeholders from all relevant sectors including health care professionals and developers working on AI and health solutions. 
    • Enabling Access to Data: By encouraging an Open Data system and ensuring that this data meets the standards set in terms of interoperability, privacy and safety. 
    • Encouraging AI research and development: Enabling medical colleges to develop centers devoted to research on Artificial Intelligence, and facilitates the exchange of knowledge between academic centers across countries. This can be achieved by collaborations between the government and large companies to promote accessibility and encourage innovation through greater R&D spending.
  • Pushing for adoption of AI by businesses and the public sector: Encouraging companies to invest in AI by providing support and incentives, and encouraging the public sector to adopt AI to improve its services.
  • Equipping existing and future labour forces with the skill sets to successfully adopt 
    AI:
    Medical colleges and other educational institutions should provide opportunities for students to skill themselves to adapt to adoption of AI in healthcare, and also push for academic programmes around AI.
  • Setting up a dedicated regulatory framework to oversee AI in India: This calls for a national-level regulatory agency that oversees developments in AI in addition to formulating a framework that ensures transparency and accountability of AI systems while promoting and enabling innovation. 
  • Design standards and appropriate certification system for health systems driven by 
    AI:
    An appropriate certification system is needed to qualify the security and quality of health systems driven by AI. Such a system can incentivize developers to meet needed standards and can work to build trust amongst health practitioners and patients. 
  • Regulatory and data sandbox for the health sector: It is been promoted as tools for enabling innovation while protecting privacy, security etc. 
  • Close monitoring towards harmonized implementation of EHR policy: It will help in enabling AI startups developing health solutions access to accurate and usable data sets. 
  • Research into impact of AI on the human: The impact of the use of AI on humans in highly sensitive and personal situations has not been thoroughly studied.

Learning Aid

Practice question:

Data security is the biggest challenge in modern age. The rise of AI and IoT further makes it harder. Analyze the challenge in context of healthcare industry which deals with lot of sensitive data of patients?

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