Globaldata, data and analytics company has highlighted some of the potential challenges that will likely follow the World Health Organization (WHO) considerations for the regulation of artificial intelligence (AI) in healthcare.
The WHO in a recent publication has outlined several considerations for the regulation of artificial intelligence (AI) in healthcare.
The move touches on the importance of establishing safety and effectiveness in AI tools, making systems available to those who need them, and fostering dialogue among those who develop and use AI tools.
But Globaldata’s Alexandra Murdoch, Senior Analyst at GlobalData, believes that AI has already improved several devices and systems, and there are so many benefits of AI. However, there are risks too with these tools and the rapid adoption of them.
“The use of false medical information is deeply concerning and could lead to several issues, including misdiagnoses or improper treatment for Black patients,” he said
AI technologies are and have been deployed quite quickly, and not always with a full understanding of how they will work in the long run, which could be harmful to healthcare professionals or patients. AI systems in medical or healthcare often have access to personal and medical information, so there should be regulatory frameworks in place to ensure privacy and security. There are several other potential challenges with AI in healthcare, such as unethical data collection, cybersecurity risks, and amplifying biases and misinformation.
A recent example of biases in AI tools comes from a study conducted by Stanford University. The study results revealed that some AI chatbots provided responses that perpetuated false medical information about Black people. The study ran 9 questions through four AI chatbots, including OpenAI’s ChatGPT and Google’s Bard. All four of the chatbots used debunked race-based information when asked about kidney and lung function.
The WHO has released six areas for regulation of AI for health, citing a need to manage the risks of AI amplifying biases in training data. The six areas for regulation are transparency and documentation; risk management; validating data and being clear about the intended use of AI; a commitment to data quality; privacy and data protection; and fostering collaboration.
