Watch the Low-Code/No-Code Summit on-demand sessions to learn how to successfully innovate and achieve efficiencies by upskilling and scaling citizen developers. Watch now.
Artificial intelligence (AI) is becoming increasingly sophisticated, thanks in large part to advances in machine learning (ML). However, there are still critical questions to be answered.
Machine learning has close ties to predictive analytics. Both can be powerful tools for uncovering insights and identifying patterns in large amounts of data. These capabilities could serve the healthcare industry well, especially when you consider that 30% of all data that is generated worldwide only comes from healthcare.
However, AI in healthcare is still in its infancy in many areas, often relegated to managing medical records or automating repetitive, mundane tasks. Of course, neither is lacking in value, but the move to greater industry-wide adoption has the potential to solve the “triple axis” of healthcare: accessibility, affordability, and accuracy. Explainable AI has even more potential: it can help institutions better find correlations through data and improve diagnostics.
Think of mental disorders. Surprisingly little progress has been made in the field of mental disorders over the past 20 to 30 years. Health care providers often don’t always know what causes certain mental disorders in different people. Mental disorders are, by their very nature, very personal. Fortunately, the use of explainable AI offers the ability to find a correlation between data points, allowing physicians to provide more personalized diagnostic results.
Explainable AI can take healthcare beyond the “black box” in ML, helping users discover and understand the correlations presented to them. It offers customization in everything from treatments to care delivery, and it’s been the direction healthcare has been heading for some time. It’s what patients want – and deserve. It also makes healthcare workers much more efficient.
Embracing the possibilities of AI in healthcare
Obviously, as the adoption of AI in healthcare increases, repetitive work will become less and less of an issue. Only medical coding could become much more efficient with the addition of AI capabilities. Identifying the unique reasons for a patient’s visit takes a lot of time. However, advances in AI not only help coder systems identify and validate codes, but also coders themselves better understand unstructured data.
Medical imaging, too, could see huge improvements with AI and ML. As it stands, doctors review and label many images every day to arrive at diagnoses. Technology can now analyze medical images to help detect and diagnose certain conditions. As a result, doctors can focus on early intervention and treatment rather than assessment. They can also see more patients, which improves access to care.
On the pharmaceutical side, you’ll find AlphaFold, an AI system developed by Google’s DeepMind. By using this AI tool, scientists can better predict the structure of protein folding, which means they can move much faster to the drug development phase. This has the potential to bring life-saving drugs to market at speeds once thought impossible.
Understanding the ethical considerations surrounding 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 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 understanding the value of the data and technology used to track, monitor, record, analyze and protect patient information. Any patient information policy should include accessibility checks and risk assessments (that is, identifying potential weaknesses in the system).
When it comes to data privacy, the focus should be on the guardrails around data. When using patient data, you need to enable some kind of alarm. After all, that information can tell a patient’s entire life story. It is important to put controls in place to enable the isolation of data. Such measures can ensure that an organization uses the technology and patient data for a good cause.
Another important ethical concern is the bias that can arise in the collection and use of data. If you have biased data, the algorithm will also be biased. The information held by the organization is unlikely to represent the community as a whole. It is critical to have varied coverage. It is equally crucial to have technology that can categorize and use such diverse information.
On the one hand, new technology is enabling the healthcare industry to use AI and data to cure many diseases – a major advancement, whichever way you look at it. At the same time, that same data could potentially improve patient well-being.
Using technology, healthcare professionals can slice and dice the information to better monitor and prevent serious health problems. If healthcare can bypass the hurdles and enable AI to better prevent and intervene early, it is quite possible to provide people with a higher quality of care and life.
Lu Zhang is founder and managing partner of Fusion Fund. Zhang, a renowned Silicon Valley investor and serial healthcare entrepreneur, was recently selected by Business Insider as one of the top 25 female early-stage investors.
Data decision makers
Welcome to the VentureBeat community!
DataDecisionMakers is where experts, including the technical people who do data work, can share data-related insights and innovation.
To read about advanced ideas and up-to-date information, best practices and the future of data and data technology, join DataDecisionMakers.
You might even consider contributing an article yourself!
Read more from DataDecisionMakers