Evolved Guidelines for Evolving AI
With the increasing availability of healthcare data and the rapid progress in analytic techniques – whether machine learning, logic-based or statistical – artificial intelligence (AI) tools could transform the health sector. The World Health Organisation (WHO) recognises the potential of AI in enhancing health outcomes by strengthening clinical trials; improving medical diagnosis, treatment, self-care and person-centred care; and supplementing healthcare professionals’ knowledge, skills and competencies.
However, AI technologies – including large language models – are being rapidly deployed, sometimes, without a full understanding of how they may perform, which could either benefit or harm end-users, including healthcare professionals and patients. When using health data, AI systems could have access to sensitive personal information, necessitating robust legal and regulatory frameworks for safeguarding privacy, security, and integrity.
AI holds great promise for health but also comes with serious challenges, including unethical data collection, cybersecurity threats and amplifying biases or misinformation. In response to the growing country’s need to responsibly manage the rapid rise of AI health technologies, the WHO released a new publication listing key regulatory considerations on AI for health on October 16.
The publication emphasises the importance of establishing AI systems’ safety and effectiveness, rapidly making appropriate systems available to those who need them, and fostering dialogue among stakeholders, including developers, regulators, manufacturers, health workers, and patients. It will support countries to regulate AI effectively, to harness its potential, whether in treating cancer or detecting tuberculosis, while minimising the risks.
The publication outlines six areas for regulation of AI for health including transparency and documentation, risk management, data quality, collaboration, privacy and data protection, validating data and intended use.
It may be noted that the first breakthrough of AI in healthcare goes back to 1950 with the development of turning tests. Later on, in 1975, the first research resource on computers in medicines was developed, followed by the National
Institutes of Health (NIH) ‘s first central Artificial Intelligence in Medicine (AIM) workshop marked the importance of AI in healthcare. With the development of deep learning in the 2000s and the introduction of DeepQA in 2007, the scope of AI in healthcare has increased.
Further, in 2010 CAD was applied to endoscopy for the first time, whereas, in 2015, the first Pharmbot was developed. In 2017, the first FDA-approved cloud-based DL application was introduced, which also marked the implementation of AI in healthcare. Then things started moving fast in AI as several AI trials were performed in gastroenterology from 2018 to 2020.
The US FDA Commissioner Robert M Califf, addressing industry stakeholders at the Global Summit in Regulatory Science 2023 on September 26 said, “While for many lay people, AI has just recently emerged as a hot issue, as regulators we’ve been keeping up with the science of data science for years, trying to anticipate and harness it’s potential. We know, for instance, that we’re likely to see substantial innovations in pharmaceutical manufacturing as a result of AI, including an impact on process measurement, modelling, and control, among other issues. And we also understand that some of these developments are likely to challenge approaches we’ve taken in the past.”
This points out that AI systems are complex and depend not only on the code they are built with but also on the data they are trained on, which come from clinical settings and user interactions. Better regulation can help manage the risks of AI amplifying biases in training data. The new WHO publication on AI aims to outline key principles that governments and regulatory authorities can follow to develop new guidance or adapt existing guidance on AI at national or regional levels.
As such, AI can aid, rather than replace the workforce, thereby increasing efficiency. As with many organisations, the most precious resource is the workforce. They must have the freedom and support to make good decisions while spending as little time as possible on repetitive documentation. This will help in the basic institutional mission of accomplishing better outcomes at a lower cost.