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We spoke with Laura Velasquez and Jose Zea to learn about the common mistakes when implementing AI in healthcare - Click to learn more.
Artificial intelligence (AI) is revolutionizing healthcare, but its implementation comes with challenges. At Arkangel AI, we have learned that the key to successful implementation lies in understanding the problem thoroughly to see if these tools can add value. Once this is established, it's crucial to start small and scale gradually. Here are some key mistakes and successes we've learned from implementing AI in healthcare.
One of the most widespread myths in healthcare is that massive amounts of perfect data are needed to implement AI. This misconception has held back many healthcare organizations that feel overwhelmed by the perception that their data is not "clean" or structured enough. However, through our experiences at Arkangel AI, our founders Laura Velasquez and José Zea found that this approach is counterproductive.
In reality, the best data is what is already available, even if it’s not perfect. AI is a progressive process that improves over time. Starting with a small, high-quality data set can be more effective than waiting to gather large volumes of data, which can be hard to manage and may not be as relevant.
For example, in a project with UNICEF for early detection of leukemia, Arkangel AI achieved over 90% accuracy using a reduced but high-quality dataset. This case shows that millions of data points are not necessary to achieve significant results. The key is to start with what you have, build a first version of a model, and then create a roadmap for optimization with new data.
Another common mistake is believing that AI is a magical solution that will automatically transform the problem at hand. Thinking this way indicates a misunderstanding of what AI is and how it can genuinely add value from a strategic perspective. It is essential to understand the basics of AI to see if it can help solve your problem.
The reality is that AI is a powerful tool but not a magic wand. It’s crucial to understand the specific problem before applying technological solutions—often, the solution may not even require AI. In some cases, simple data analytics may be enough to solve an issue.
For example, in many healthcare systems, basic descriptive analytics can alert doctors about potentially dangerous drug interactions. Applying AI isn’t necessary in every situation; understanding when and where AI adds real value is more important.
To overcome fear of the unknown and avoid costly mistakes, Arkangel AI recommends starting with pilot projects. These allow organizations to experiment with AI in a controlled environment, evaluate its effectiveness, and make adjustments before rolling it out on a large scale.
A successful pilot project not only provides valuable data and experience but also builds confidence for tackling larger and more complex projects. These pilots can also serve as proof of concept, demonstrating AI’s value to other departments and facilitating organization-wide adoption.
A common mistake is treating AI as an isolated area within healthcare organizations. AI can function across any department and be implemented by anyone, much like the internet, which is used across all daily activities. To adopt this mindset, many institutions have found success in creating multidisciplinary teams that work across departments to determine how and when to implement these tools.
When multidisciplinary teams collaborate, AI becomes a powerful tool to improve efficiency, reduce costs, and, most importantly, enhance patient outcomes.
Digital transformation isn’t just about technology; it’s also about a cultural shift that requires everyone to understand the basics of AI. Once the concept is grasped, it becomes easier to recognize when and where AI is necessary to solve problems or optimize processes.
Investing in team education maximizes the benefits of emerging technologies while reducing resistance to change, which is a common barrier in many healthcare organizations.
Healthcare professionals are naturally creative when managing diseases and patient care. Adopting new tools like AI allows them to see things previously impossible and make better decisions for patients and systems alike. For example, in treating rare diseases where medical knowledge may be limited, AI can help identify hidden patterns in genetic or clinical data, offering new perspectives for treatment.
AI doesn’t replace human creativity; it enhances it by providing tools that enable healthcare professionals to explore new ideas and approaches. This collaborative approach between AI and humans can lead to significant innovations in treatment and disease management.
One of the biggest mistakes healthcare organizations can make is viewing digital transformation as a final goal instead of an ongoing process. Digital transformation is a journey that requires constant iteration and adaptation. It’s not about implementing a solution and considering the job done, but about continually improving and adjusting systems as needs evolve and new technologies emerge.
This iterative approach ensures that organizations stay at the forefront of innovation and that the solutions they implement remain relevant and effective in the long term. Successful digital transformation in healthcare requires a commitment to continuous improvement and a mindset of innovation that permeates every level of the organization.
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In conclusion, while AI holds immense potential to revolutionize healthcare, avoiding common pitfalls and understanding its strategic value is essential. Starting small, focusing on real problems, and fostering collaboration across teams are key steps to ensuring a successful and impactful AI implementation.
