Using AI in health care: Balancing innovation and privacy
This article is authored by Vinod K Singh, co-founder and CTO, Concirrus.
As Artificial Intelligence (AI) is revolutionising various industries, it has brought a lot of hope and possibilities to the health care sector as well. AI-powered technologies are enabling more accurate diagnoses, personalised treatment plans, and streamlined workflows, thereby improving patient outcomes and increasing efficiency in health care. However, the rapid adoption of AI in health care has also given rise to some important ethical issues that need to be carefully understood.
India’s health care system today faces several challenges, including a shortage of medical professionals, inadequate infrastructure, and access to quality care that impacts the poor and the common man the most. AI can help fill these gaps, providing scalable and efficient solutions. For example, AI-powered diagnostic tools can help doctors in rural areas analyse medical images (such as X-rays) and identify conditions like tuberculosis or diabetic retinopathy, with high accuracy. Telemedicine platforms, which are powered by AI, can connect patients living in remote areas with specialists in urban centers, eliminating geographical barriers to care.
AI also has the ability to personalise treatment plans based on people's individual health and lifestyle data, leading to better outcomes. In India, where the burden of chronic diseases is rising, AI can help manage conditions such as diabetes and hypertension by predicting future diseases in patients and suggesting appropriate lifestyle changes for each individual.
Although AI offers significant benefits in the health care sector, it is important to understand its limitations. AI systems are not infallible, and they may make mistakes when faced with new or unexpected situations. Furthermore, AI systems may be prone to error or hallucinations, which can have serious consequences in health care. For example, an AI system may misdiagnose a disease or recommend inappropriate treatments. For example, if an AI system is trained on population data from primarily western countries, it may misinterpret the symptoms of Indian patients, leading to incorrect diagnosis or treatment recommendations. In a country like India, where cultural, genetic and environmental factors have a significant impact on health, the risk of AI errors is of particular concern.
A major concern with AI in health care is the security of patient data. In India, where data privacy laws are still evolving, the use of sensitive health information in AI systems raises important ethical questions. Many Indians do not know how their data is being used, and most of the time people give permission for unsolicited data access, complicating the legal process. In such a situation, the common man, who is more focused on access to immediate care than thinking about the negative consequences of long-term data sharing, may inadvertently compromise his privacy.
Another important aspect is the over-reliance on AI. While it is well known that AI can process vast amounts of data faster than any human, it lacks the insight, empathy, and understanding of context that any health care worker has. An AI system may flag a particular treatment as ideal based on the data and recommend the same to many patients but it cannot consider the patient’s individual circumstances, cultural beliefs, or mental state—all of which are important in health care.
In India, where the relationship between doctor and patient is based on trust and empathy, it is essential to ensure that AI does not weaken this bond. Patients must feel confident that their care is guided by both advanced technology and empathetic human decision-making. In addition, health care providers must be transparent with patients about how AI is being used in their care and what its limitations are. In addition, AI systems must be developed keeping in mind India's cultural and linguistic diversity. AI models must be trained on diverse data sets so that they can be effective for people from different backgrounds.
To address these challenges, health care organisations must adopt a strong ethical framework that encourages innovation while prioritising patient privacy. This includes taking into account several important aspects:
- Data minimisation: Health care providers should collect and use only the data necessary for AI applications. By minimising the amount of data collected, the potential impact of a data breach can be reduced and patient privacy can be better protected.
- Transparency: Organisations should be transparent about how patient data is used in AI systems. Patients should be told for what specific purpose their data will be used, and they should have the option not to have their data used in AI development if they feel uncomfortable.
- Security measures: Strong security measures should be implemented to protect patient data from unauthorised access. This includes encryption, secure data storage, and regular security audits. Additionally, AI systems should be designed in such a way that they do not create new vulnerabilities.
- Ethical AI development: AI systems should be developed with ethical considerations in mind. This includes ensuring that AI algorithms do not inadvertently promote prejudice or discrimination. AI developers should work closely with ethicists, health professionals, and patient rights groups to ensure that ethical considerations are integrated into the design and deployment of AI systems.
- Regulation and oversight: Governments and regulatory bodies must ensure that the role of AI in health care is used ethically. This includes developing rules to protect patient privacy, ensure the safety of AI systems, and promote transparency in AI-driven decision-making.
- Human-in-the-loop: Maintaining human oversight in AI-powered health care is essential. Doctors, nurses, and other health care professionals should work closely with AI systems, using them as tools to enhance their capabilities, not replace them. Human judgment is critical in interpreting AI recommendations, especially in cases where the patient’s condition cannot be quantitatively measured by data.
India's unique health care landscape presents both opportunities and challenges for AI adoption. On the one hand, the country's large population and diverse health care needs make it an ideal testing ground for AI-powered solutions. On the other hand, the country's infrastructure challenges and digital divide could hinder the widespread adoption of AI technologies. It remains to be seen how this coordination is determined in the times to come.
This article is authored by Vinod K Singh, co-founder and CTO, Concirrus.