Check out the Low-Code/No-Code Summit on-demand sessions to learn how to successfully innovate and achieve efficiencies by enhancing and scaling citizen developers. Watch now.
Artificial intelligence (AI) continues to grow in sophistication, largely due to advances in machine learning (ML). However, there are still critical questions that need to be answered.
Machine learning has close ties to predictive analytics. Both can be powerful tools for discovering information and identifying patterns in large amounts of data. These capabilities could serve the healthcare sector quite well, especially considering that 30% of all data generated worldwide comes from healthcare alone.
However, AI in the healthcare industry is still in its relative infancy in many areas, often relegated to managing medical records or automating mundane and repetitive tasks. Of course, none of those things are without value, but moving toward greater industry-wide adoption has the potential to solve the “triple A’s” of healthcare: accessibility, affordability, and accuracy. Explainable AI has even more potential: it can help institutions better find correlations through data and improve diagnostics.
Consider mental disorders. During the last 20 to 30 years, there has been surprisingly little progress in the field of mental disorders. Health care providers often don’t always know what triggers certain mental disorders in different people. Mental disorders are, by their nature, highly personalized. Fortunately, the use of explainable AI presents an opportunity to find a correlation between data points, allowing clinicians to offer more personalized diagnostic results.
smart security summit
Learn about the critical role of AI and ML in cybersecurity and industry-specific case studies on December 8. Sign up for your free pass today.
Explainable AI can take the healthcare industry beyond the “black box” in ML, helping users discover and understand the correlations presented to them. It offers personalization in everything from treatments to care delivery, and it’s the direction healthcare has been in for some time. It is what patients want and deserve. It also makes healthcare workers much more efficient.
Seizing the AI opportunity in healthcare
As AI adoption in the healthcare industry increases, repetitive work will obviously become less of an issue. Medical coding alone could become much more efficient with the addition of AI capabilities. Cataloging the unique reasons for a patient’s visit is time consuming. However, advances in AI are helping not only coding systems to identify and validate codes, but also coders themselves to better understand unstructured data.
Medical imaging could also see big improvements with AI and ML. As it is, doctors review and label many images each day to reach a diagnosis. Technology can now analyze medical images to help detect and diagnose certain conditions. As a result, clinicians can focus on early intervention and treatment instead of review. They can also see more patients, which improves access to care.
On the pharmaceutical side, you’ll find AlphaFold, an AI system powered by Google’s DeepMind. Using this AI tool helps scientists better predict the structure of protein folding, which means they could move to drug development much faster. This has the potential to bring life-saving medicines to market at speeds previously thought impossible.
Understand the ethical considerations around patient data
When it comes to the ethical considerations of AI in the context of patient data, many healthcare organizations are wondering where to draw the line and what are the implications of using patient data to improve care. These organizations are responsible for managing, storing, and securing often highly sensitive information.
HIPAA has established basic requirements, but the key is to understand the value of data and the technology used to track, monitor, capture, analyze and protect patient information. Any policy related to patient information should include accessibility controls and risk assessments (ie, identify potential weaknesses in the system).
When it comes to data privacy, the focus should be on the security measures around the data. When using patient data, you must enable some type of alarm. After all, that information could tell the whole story of a patient’s life. It is important to implement controls to allow data isolation. These measures can ensure that an organization uses technology and patient data for a good cause.
Another key ethical concern is the bias that can arise with the collection and use of data. If you have skewed data, the algorithm will also be skewed. The information available to the organization may not represent the community as a whole. It is essential to have diverse coverage. It is equally crucial to have technology that can categorize and use such diverse information.
On the one hand, new technology is allowing the healthcare industry to use AI and data to cure many diseases, an important advance no matter how you slice it. At the same time, that same data can potentially improve the well-being of patients.
With the help of technology, health professionals can slice information to better control and prevent serious health problems. If the healthcare industry can get around the hurdles and allow AI to do more preventable and early intervention work, it is entirely possible to offer people a better quality of care and life.
lu zhang He is founder and managing partner of Fusion Fund. A renowned Silicon Valley investor and serial healthcare entrepreneur, Zhang was recently selected as one of Business Insider’s Top 25 Women Early-Stage Investors.
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including data technicians, can share data-related insights and innovation.
If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data technology, join us at DataDecisionMakers.
You might even consider contributing an article of your own!
Read more from DataDecisionMakers