AI Safety in Healthcare: Challenges of Responsible AI Adoption
Advertisement
Adoption of AI in healthcare is happening at a pace unmatched in any other industry and rapidly spreading from diagnostic imaging and clinical documentation into areas such as hospital management and direct patient encounters in just a few years. As more is adopted in healthcare, the fundamental question that arises is not if AI can be used in a clinical context, which is obviously possible, but if it will be safe and reliable enough to be put into action on a large scale where any mistake will have a direct impact on the quality of healthcare provided. This is exactly the reason why the safety of AI in healthcare and responsible AI adoption are becoming intertwined within the discussion of how far and fast we should allow this technology to scale, and this article discusses its meaning, current status in India, and remaining unresolved questions.
What is AI Safety in Healthcare?
"AI safety" in health care refers to the techniques, processes, and controls applied to mitigate the possibility of a given AI system delivering the wrong, skewed or harmful output during clinical practice. It encompasses testing the accuracy of a model before implementation, performance monitoring after its implementation and ensuring that a qualified clinician takes responsibility for the decision-making process facilitated by the system.
The adoption of responsible AI is similar to the former but is wider in scope. It involves not only the question of whether the particular AI technology is safe but also whether the adopting entity has appropriate accountability, transparency of how the technology was trained and tested and well-defined boundaries of what it is allowed to do and what it is not.
Both concepts have greater importance in the health care sector than most others due to the different nature of the possible consequences. While an error made by a recommendation engine of a shopping application would lead to minimal losses, an erroneous drug interaction or symptom output generated by a clinical AI system can pose a direct threat to patients' safety.
Where India's AI Safety Regulations Currently Stand
There are no specific laws regulating the use of AI in healthcare in India. Currently, the regulation of the sector is divided into existing frameworks: AI diagnostic systems may be subject to regulation by the medical device regulator if the software fulfils a clinical function, whereas data protection aspects are regulated by India’s data protection law and general information technology rules.
The development of policy seems to be more dynamic than regulatory measures in the country. So, in February 2026, the authorities of the health sector introduced a national strategy document on AI and a benchmarking tool for testing and validation of AI tools before the deployment of such tools in healthcare. Additionally, a series of AI governance guidelines issued in December 2025 suggested institutional solutions, including the creation of a body for the safety of AI, but these are not legally binding measures.
Recently, it was noted that the risk-based regulatory approach to AI may be considered, according to which the healthcare sector will be seen as a high-risk area requiring more stringent obligations in comparison with the low-risk consumer areas, such as chatbots.
High-Risk Applications Where AI Safety Matters Most
AI diagnostic aids represent one of the most safety-critical applications of AI in medicine. Such software takes images or results of tests as input and provides a diagnosis, so a wrong output from the system can directly affect patient treatment plans in the absence of a clinician review.
The next important application for AI is related to the process of writing or summarising clinical notes. This class of systems creates notes for each patient visit, so if the system behind it makes up some information that has not been discussed during the consultation, it can become a problem.
Another class of applications includes AI chatbots and triaging tools designed to help patients with their questions or guide them towards the right kind of medical attention. As these tools interact directly with patients without involving clinicians as intermediaries, any error made by these systems can affect patient decision-making.
Why Getting Safety Right Makes AI More Useful, Not Less
If used in an appropriate manner, AI technologies in the healthcare sector may discover certain patterns that would remain unnoticed by a human reviewer, especially when the field involves many images, like radiology. This happens thanks to the safety tests that allow using these technologies safely and without adding new risks to the patients.
Practices of responsible AI adoption include the requirement for human evaluation of results obtained through AI. It has been proved in surveys that if healthcare workers encounter inaccurate results obtained with the help of a certain tool several times, it becomes less and less likely that they will trust the tool.
As for the system as a whole, standardised safety testing before deployment may also decrease inconsistency between different providers. Benchmarking or validation of an AI tool is conducted in the same way in all hospitals; thus, when a tool is deployed, it has been tested according to the same standard in all hospitals.
Where AI Safety Still Falls Short
The issue of accuracy is still a major one. For example, independent studies that analysed large language models' performance based on clinical scenarios that included one planted error (fabricated lab results and diagnosis) showed that the models repeated and embellished on the planted error in most of the test scenarios, and the issue could be only partially fixed through prompt correction.
Regulatory fragmentation is another issue that is unique to India. The responsibility for the clinical decision in case an AI system was utilised in the process still lies with the individual physician and the organisation where he or she works, which is a contradiction to the source of risks generated by AI. There is also no single regulatory body overseeing the application of AI technologies throughout the health care sector.
The third problem is that of an uneven infrastructure. While large-scale hospital networks can afford to have their own team for security and compliance issues, which can conduct tests on the software, small diagnostic centres and clinics lack the necessary resources for that.
Where AI Safety in Healthcare Goes from Here
While the trend on an international level has been one of moving towards more structured supervision, the fact is that all current systems still rely largely on voluntary rather than legally binding approaches. Resolutions and principles supporting safe and reliable use of AI have been passed in international forums, but the enforcement measures and mechanisms vary widely region to region, as well as there being no global standards for this issue as yet.
In India, it seems that the process of moving from voluntary guidelines to structured, risk-based regulations has started already, with healthcare being mentioned multiple times as the field that is likely to have more rigid requirements compared to other less risky applications. However, whether this will result in any legislation and when exactly remains an open question.
The thing that can be noted in all the sources reviewed is that the effectiveness and risks associated with this technology are developed simultaneously, not one after the other. It is likely that the way this issue will be handled, through testing procedures and regulation, will shape the balance between safety of AI in healthcare and responsible use of the technology in the future.
The effectiveness of clinical AI and the risks associated with it are emerging simultaneously, as opposed to one preceding the other. The open question for India as it transitions from a voluntary approach to a risk-based approach is whether regulatory certainty and oversight are able to keep up with how quickly these technologies are being implemented.
Stay tuned for more such updates on Digital Health News
