Introduction to AI in Healthcare
As artificial intelligence begins to transform healthcare, a big question often is overlooked: Are hospitals, health systems and IT vendors training AI on the right data? Many AI models rely heavily on data from U.S. and European sources. As such, this can create biases that limit treatment options. Valuable insights from other parts of the world are left out. In fact, research has shown biased datasets can contribute to healthcare disparities and overlook effective treatments available outside the U.S.
The Importance of Diverse Data
John Orosco, CEO of Red Rover Health, has plenty of experience in AI and datasets. The company specializes in simplifying EHR integration through a platform that uses secure RESTful APIs to connect third-party software with EHR systems. All of this is designed to enable healthcare organizations to enhance existing EHRs with best-of-breed systems, improving access to real-time patient data and streamlining clinical workflows.
Challenges with AI and Data
The main problem with AI in healthcare today isn’t the technology itself – it’s that we’re still in the early stages of its evolution. Large language models continue to mature at a rapid pace, and while they’re already showing incredible promise, it’s clear we’ve only scratched the surface. Early indicators suggest AI is here to stay and that it will fundamentally reshape how we approach automation, decision making and productivity across industries, especially in healthcare. But as powerful as these models are becoming, their effectiveness hinges on access to data. AI can only be as good as the information it has to work with.
The Need for Global Data
Training AI solely on regional or national data bakes in the cultural, systemic and clinical biases of that region. It gives us a narrow lens through which AI understands medicine and health, and that fundamentally restricts its usefulness. If AI is only trained on U.S.-based data, it will naturally reflect and reinforce those treatment patterns, even when other approaches might be equally or more effective in different contexts. If we truly want AI to support better health outcomes globally, we need to think beyond borders. That means training models on a wide range of data from different countries, cultures and care models.
The Connection to Genomics and Precision Medicine
There’s a powerful connection between AI, data and the future of personalized care through genomics and precision medicine. Think of the human body as an operating system. Each of us runs on our own unique source code, which is our DNA. Mapping the genome is essentially decoding that system. It tells us how we’re wired to respond to certain medications, how we metabolize drugs, and even what conditions we might be predisposed to. By including genomics in the data mix, AI can help identify the most effective treatments for each individual before trial and error even begins.
Considering Non-Mainstream Therapies
AI models should expand their view beyond just local, mainstream treatment protocols, especially when those protocols are defined by regional governing bodies. Too often, AI systems are trained on datasets that reflect only what’s been approved or reimbursed in one country, usually based on regulatory or insurance parameters. While that might make sense from a compliance standpoint, it limits the potential of AI to offer patients a truly comprehensive view of available treatment options. Non-mainstream or alternative therapies used globally shouldn’t be ignored by AI simply because they fall outside the local medical playbook.
Conclusion
In conclusion, AI has the potential to revolutionize healthcare, but it’s crucial that we train it on the right data. This means using diverse, global data sets that reflect different cultures, care models, and treatment approaches. By doing so, we can create AI systems that are more effective, equitable, and personalized. Additionally, considering non-mainstream therapies and including genomics in the data mix can help AI provide better treatment options for patients.
FAQs
Q: What is the main problem with AI in healthcare today?
A: The main problem with AI in healthcare today is that we’re still in the early stages of its evolution, and its effectiveness hinges on access to data.
Q: Why is it important to train AI on diverse, global data sets?
A: Training AI on diverse, global data sets helps to avoid biases and ensures that AI systems are more effective, equitable, and personalized.
Q: What is the connection between AI, data, and genomics?
A: The connection between AI, data, and genomics is that AI can help identify the most effective treatments for each individual by analyzing genomic data in combination with other clinical data.
Q: Should AI models consider non-mainstream therapies?
A: Yes, AI models should consider non-mainstream therapies to provide patients with a truly comprehensive view of available treatment options.
