9 Ethical Questions You Should Ask When Implementing Artificial Intelligence in Healthcare
Artificial intelligence (AI) has the potential to transform healthcare. AI is making it easier for doctors and researchers to analyze huge volumes of data, spot patterns, and detect risks sooner. This means that artificial intelligence can help doctors diagnose patients faster, prevent diseases earlier on, monitor patient progress better — and even tailor treatments to their individual needs. However, with so many benefits of artificial intelligence in healthcare also come some risks. New technologies can lead to an erosion of privacy if users are not careful about what information they make available online or whether it’s stored securely on servers that aren’t accessible by hackers. Similarly, the use of artificial intelligence in healthcare can result in biased algorithms if the data sets used to train them are not representative of all users.
What is Artificial Intelligence?
Artificial intelligence refers to machines that can “think” and “learn” in ways that are comparable to human cognition. While AI machines don’t replicate human thought perfectly, they are designed to complete tasks that would be difficult or impossible for people to do. Computers have been assisting with medical diagnoses for decades, but AI promises to take the process much further. Rather than relying on programmed algorithms to sort through data, AI systems are designed to learn from new information and adjust their processes accordingly. AI systems can accomplish this in a number of ways. Some build upon “machine learning,” a method of programming computers to solve complex problems by identifying patterns in large sets of data. Training a machine-learning algorithm requires feeding it huge amounts of information, often a mix of real and fictitious data, so it can identify patterns and make accurate predictions. Other systems use “artificial neural networks,” a computer architecture inspired by biology.
Know Your Data
One of the most important things to do when implementing AI is to evaluate the data you’re using. If you’re building an app that uses AI to diagnose health conditions, for example, you’ll need a large data set of accurate patient information. That way, the app can learn to predict future health issues based on the information it already has. If the data you’re planning to use isn’t representative of your entire user base, then your AI could end up misdiagnosing many customers. Similarly, if you’re building an app that uses AI to recommend treatments, you’ll need a large data set of accurate treatment information. That way, the AI can learn to recommend treatments based on the information it already has. If the data you’re planning to use isn’t representative of your entire user base, then your AI could end up recommending incorrect treatments.
Be transparent and educate your users
When implementing AI, it’s important to be transparent with the public. Make sure that you’re educating your users about the way your AI works so that they know how it’s making decisions and can challenge any incorrect conclusions it draws. If your AI is recommending treatments, for example, make sure that it’s clear how the AI is determining which treatments are best for each user. If your AI is recommending which patients should receive a specific treatment, make sure that you’re transparent about the data the AI is using to make that decision. If patients can see the data, they can challenge any incorrect conclusions the AI may draw.
Be cautious with AI-based remote diagnostics
Many healthcare providers are now using AI to remotely diagnose patients. While this can be helpful, it can also result in incorrect diagnoses if the AI isn’t properly trained. If your doctor is diagnosing patients remotely, make sure that the data he or she is using is representative of your entire user base. If it’s not, then there’s a higher chance that the AI will draw incorrect conclusions. Similarly, if your doctor is diagnosing patients remotely, make sure that the data you provide is accurate. If you’re misdiagnosed, the AI won’t be able to help you.
Be cautious with AI-based remote monitoring
Some healthcare organizations are using AI to remotely monitor patients’ conditions. This can be helpful, but it can also result in incorrect conclusions if the AI isn’t properly trained. If your doctor is monitoring your condition remotely, make sure that the data he or she is using is representative of your entire user base. If it’s not, then there’s a chance that the AI will draw incorrect conclusions about your condition. Similarly, if your doctor is monitoring your condition remotely, make sure that you’re providing accurate data. If you misreport your condition, the AI won’t be able to help you.
Be cautious with AI-based remote treatment
Some healthcare providers are now using AI to remotely provide personalized treatments. This can be helpful, but it can also result in incorrect treatments if the AI isn’t properly trained. If your doctor is providing you with a treatment remotely, make sure that the data he or she is using is representative of your entire user base. If it’s not, there’s a chance that the AI will draw incorrect conclusions about which treatment is best for you. Similarly, if your doctor is providing you with a treatment remotely, make sure that you’re providing accurate data. If you misreport your condition, the AI won’t be able to help you.
Conclusion
Artificial intelligence is a powerful tool that has the potential to transform healthcare. It can help doctors diagnose patients faster, prevent diseases earlier on, monitor patient progress better — and even tailor treatments to their individual needs. However, with all of these benefits come some risks. New technologies can lead to an erosion of privacy if users are not careful about what information they make available online or whether it’s stored securely on servers that aren’t accessible by hackers. Similarly, the use of artificial intelligence in healthcare can result in biased algorithms if the data sets used to train them are not representative of all users. When implementing AI, it’s important to be transparent with the public. Make sure that you’re educating your users about the way your AI works and that they know how it’s making decisions. It’s also important to make sure that the data you’re using is accurate and representative of your entire user base.
