ARTIFICIAL INTELLIGENCE IN HEALTH

The modern era of global connectivity and high levels of mobile usage in India present significant opportunities for access to AI technology focused healthcare within the following areas –

  • 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, truly connect with patients and positively impact cases of medical errors and misdiagnosis.
  • AI in Diagnostics – One of the key healthcare challenges in India is acute shortage of radiologists. AI based diagnosis can be especially helpful for radiology, pathology, skin diseases, and ophthalmology.
  • AI for Optimising Treatment Plans – AI can also be used for assisting doctors and patients to choose an optimal treatment protocol. Machine Learning can be used to mine not only doctor’s notes and patient’s lab reports, but also link to the extant medical literature to provide optimal treatment options.
  • AI for Monitoring/Ensuring Compliance – The potential for AI application in remote monitoring has enhanced manifolds via the use of wearables. These can be used for monitoring various aspects such as movements, physiological parameters, temperature and alerts that can be communicated to healthcare professionals.
  • 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.

Challenges and Controversies –

  • Healthcare industry issues – The challenges of migrating to an AI-technology based healthcare infrastructure are numerous as medical professionals attempt to transition to new ways of working and adopt new systems and processes. 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.
  • Technology-related issues – AI systems and the underlying algorithms are reliant on the quality of data to enable the Machine Learning elements to perform the necessary processing and decision making. Each state has its own system and working process. Initiatives are needed at the state and national government levels to ensure shared data standards, data security and exchange processes.
  • Socio-cultural issues in technology implementation – Studies indicate that decisions with respect to technological development and adoption are made to take account of cultural context and existing social conditions. Solutions need to take account of the Indian context where pockets of the population are socially and educationally challenged, culturally marginalised and economically disadvantaged. Decision makers need to ensure that public sector healthcare organisations benefit from AI technology rather than default to the private sector reaping the rewards for investment.
  • Regulatory and ethical issues – There are several ethical and regulatory challenges in implementation of AI in healthcare in India. Data security and privacy is especially important with increasing use of wearables which can potentially cause identity theft through hacking of devices and data. The regulators need to provide clear and concise agreement and privacy policies to enhance widespread and safe adoption of these devices.

What should be done?

  • To enhance the adoption of technology by healthcare providers, AI and its application should be incorporated within the curriculum for medical and paramedical training.
  • Technology should be recognised as socio-culturally embedded; hence, 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. The underlying algorithms must be transparent and explainable to ensure ease of audit.
  • AI systems should not exhibit bias. The algorithms developed for the AI system must not exhibit racial, gender or Pin code-based decision making.
  • AI healthcare systems must conform to human values and ethics.
  • Adoption of AI based healthcare must be benefits-driven. The migration towards greater levels of technology must ensure that changes are geared to the benefits of patients and the overall healthcare of Indian people.

Pilot initiatives should be developed within key states to trial the impact that AI systems could have on existing healthcare systems and infrastructure. Lessons should be learned from these initiatives before, wider rollout at a national level.

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