Artificial Intelligence in Healthcare

Need For AI In Healthcare In India
• India’s ratio 0.8 doctors per one thousand head of population (UK: 2.8, Australia: 5, China: ~ 4)
• Average patient-to-doctor face-to-face contact of just two minutes in India. This illustrates the challenges of extremely heavy workloads on Indian doctors and opportunities for AI based solutions to make a difference.

Opportunities And Applications

  1. AI in Assistance to Physicians
    • AI can relieve highly-skilled medical professionals from routine activities, freeing up doctors to concentrate on the higher-value cognitive application of medical practice.
    • AI-based technologies can offer improvements with speedy diagnosis and therapy selection, reducing medical errors, improving productivity, assessing and modelling risk and stratifying disease.
  2. AI in Diagnostics
    • AI based diagnosis can be especially helpful for radiology, pathology, skin diseases, and ophthalmology.
    • For example, Aravind Eye Care Systems and Sankara Nethralaya have developed and validated an AI-based algorithm for diabetic retinopathy, which assists the ophthalmologists in screening for diabetic retinopathy on the basis of images of retina set to the doctor from peripheral centres.
    • The Tamil Nadu e-Governance Agency is helping the health department with the shortage of radiologists by developing an AI-based system to read CT brain scans and grade them for further interventions.
  3. AI for Optimising Treatment Plans
    • AI can also be used for assisting doctors and patients to choose an optimal treatment protocol.
    • Such technology is in use in India, China and Thailand to provide appropriate recommendation plans for cancer treatment using patient’s details linked to medical literature.
  4. AI for Monitoring/Ensuring Compliance
    • The potential for AI application in remote monitoring has enhanced manifolds via the use of wearables.
    • Devices can be used for helping people exercise and adopt healthy eating.
  5. AI in the COVID-19 Epidemic
    • The COVID-19 epidemic highlights the need for an AI based epidemic monitoring system that can model and predict outbreaks and help optimise scarce resources.
    • AI can help fight the virus via Machine Learning-based applications including population screening, notifications of when to seek medical help and tracking how infection spreads across swathes of the population.
    • A Chinese tech firm uses AI systems to flag anyone who has a temperature above 37.3 degrees within Beijing’s Qinghe Railway Station.

Challenges and Controversies

A. Healthcare Industry Issues
• Traditional healthcare personnel may resist new innovations, doctors may not trust AI systems, patients may question AI-based decision-making and medical staff could view the changes as disenfranchising them from their key roles and decision-making powers.
• The required transformation to an AI-centric healthcare system requires not just trust from medical professionals, but also from patients unaccustomed to new ways of diagnosis & decision-making.
• The key challenge for policy makers is the engendering of confidence in the outcomes and trust that a human medical practitioner has an active role within the AI system.
• The challenge for the training of doctors is to address the transformational nature of AI-based healthcare, whilst not elongating the period for learning and qualification to integrate these new systems alongside everyday working practices.
B. Technology-related Issues
• AI systems and the underlying algorithms are reliant on the quality of data to perform the necessary processing and decision-making.
• The challenge within India is the disparate nature of health care related data. Each state has its own system and working process.
• This is complicated by the massworker migration between states, and highlights the need for solutions at a national level.
C. Socio-cultural Issues in Technology Implementation
• Within India, access to internet is primarily undertaken via mobile phones. While the penetration of mobile phones would at face value seem to be a positive factor for the adoption of AI.
• However, it could inadvertently amplify the gender disadvantage as research shows that women are less likely to own a mobile phone than men. When overlaid with patriarchal and misogynistic social factors, the real access figure could be less.
• Solutions need to take account of the Indian context where pockets of the population are socially and educationally challenged, culturally marginalised and economically disadvantaged.
D. Regulatory and Ethical issues
• Data security and privacy is especially important with the increasing use of wearables which can potentially cause identity theft through hacking of devices and data.
• AI is set to alter the traditional relationship between the doctor and the patient as technology plays the role of a third substantial actor. Under these circumstances, the regulators need to provide clear and concise user agreement and privacy policies to enhance widespread and safe adoption of these devices.
Way Forward
• AI and its applications should be incorporated within curriculum for medical & paramedical training.
• The technology design and implementation should take into account cultural practices and address the gender divide in India.
• Ethical guidelines regarding security and privacy of data should be protected. The data should be strictly used for clinical purposes only.
• The AI system must be explainable and auditable. All decisions made in the context of diagnosis or recommendations can impact on human lives. As such the underlying algorithms must be transparent and explainable to ensure ease of audit rather than acting as a black-box based system.
• AI systems should not exhibit bias. It must not exhibit any racial, gender or Pincode-based decision making that disenfranchise or favor any population groups.
• AI healthcare systems must conform to human values and ethics.
• Adoption of AI based healthcare must be benefits-driven.

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