AI is nothing new in healthcare and has actually aided in it significantly – making it more personalized, while also improving quality of care and patient outcomes.
However, like any new development, it is coupled with its own challenges. We previously touched on the ethical and legal challenges of AI in general. There are guidelines and regulations in the deployment of AI systems, especially because of the sensitive data involved. It’s likewise important to consider this in the field of healthcare especially. We now have algorithms that improve diagnoses, reduce margins of error, chatbots that record symptoms and recommend cures, tools that identify the likelihood of diseases, surgical robots, and wearable health trackers. There are set to be even more advancements in the coming years, which may urge us to take a step back and first assess the trust placed in these innovations.
Large volumes of data are analyzed by machines to find connections and patterns – but how can we account for the data used? At times, algorithms can be based on hypothetical data. Biases can also complicate the issue, and the health sector is known for its implicit biases. Training algorithms based on these biases can further exacerbate them, making them even more profound as medical practitioners are becoming more reliant on these models. To make algorithms in healthcare more comprehensive, algorithms need to be trained on data from diverse demographics. Failure to get more representative samples can cause these algorithms to do more harm than good, as this will lead to inaccurate predictions and recommendations.
Implementing AI in healthcare could have serious legal ramifications if the proper precautions are not taken. When diagnoses or treatments are deployed based on AI models and these result in malpractice or misdiagnoses, you may wonder which entity is held liable – the hospital, health professional, or the manufacturer of the algorithm. It’s without question that these need to be ironed out, as technicalities like “black box medicine” can enter the conversation. As intelligent as some machines and algorithms are, there is still a possibility for mistakes to occur. Humans are prone to error, as are the machines that they train. When methods for computation are opaque – which they are, in many cases – we may be unable to trace how decisions were made. For this very reason, greater transparency among stakeholders needs to be enforced. It is only until then that we may be able to be more trusting of these technologies.
AI in healthcare is not without its challenges. However, with careful consideration, more stringent assessments, and better regulations, we may grow more accustomed to accepting the good that AI has to offer in improving the landscape of healthcare.
Content exclusively written for abbyy.com – By Alexis Mitchell