Artificial intelligence in healthcare: huge potential, and ethical questions

Check out the on-demand sessions from the Low-Code/No-Code Summit to learn how to successfully innovate and achieve efficiencies by upskilling and scaling citizen developers. watch now.

Artificial intelligence (AI) continues to grow in development, largely due to advances in machine learning (ML). However, there are still important questions that need 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 sector well, especially when you consider that 30% of all data generated worldwide comes from healthcare alone.

However, AI in healthcare is still relatively in its infancy in many areas, and it’s often pushed back from managing medical records or automating mundane, repetitive tasks. Of course, neither of these things is lacking in value, but the move toward greater industry-wide adoption has the potential to solve the healthcare “triple”: accessibility, affordability, and accuracy. Explainable AI has even greater potential: It can help organizations better find correlations through data and improve diagnostics.

Consider mental disorders. During the past twenty to thirty years, there has been surprisingly little progress in the field of mental disorders. 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 interpretable AI provides an opportunity to find correlations between data points, allowing clinicians to provide more personalized diagnostic results.


Smart Security Summit

Learn about the critical role of AI and machine learning in cybersecurity and industry-specific case studies on December 8th. Register for your free traffic pass today.

Register now

Explainable AI can move the healthcare industry beyond the “black box” of ML, helping users discover and understand the associations presented to them. It provides personalization in everything, from treatments to delivery of care, and this is the direction healthcare has taken for some time now. This is what patients want – and deserve. It also makes healthcare workers more efficient.

Embrace the opportunity of artificial intelligence in healthcare

With the increasing adoption of AI across the healthcare industry, it’s clear that repetitive work is going to be less and less of an issue. Medical coding alone could become more efficient with the addition of AI capabilities. It takes a lot of time to catalog the unique reasons for a patient’s visit. However, advances in AI are not only helping coding systems identify and verify codes, but also helping programmers themselves better understand unstructured data.

Medical imaging can also see significant improvements with artificial intelligence and machine learning. As it is, doctors review and label many images each day to arrive at diagnoses. Technology can now analyze medical images to help detect and diagnose specific conditions. As a result, clinicians can focus on early intervention and treatment rather than revision. They also get to see more patients, which improves access to care.

On the pharmaceutical side, you’ll find AlphaFold, an artificial intelligence system developed by Google’s DeepMind. Using this AI tool helps scientists better predict the folding structure of a protein, which means they can move to the drug development phase faster. This has the potential to bring life-saving medicines to market at speeds thought impossible.

Understand ethical considerations about patient data

Turning to the ethical considerations of AI in the context of patient data, many healthcare organizations are wondering where to draw the line – and what the implications are for using patient data to improve care. These organizations are responsible for managing, storing, and often securing highly sensitive information.

HIPAA has established basic requirements, but the key is understanding the value of the data and the technology used to track, monitor, capture, analyze, and protect patient information. Any policy associated with patient information should include access controls and risk assessments (ie identification of potential system vulnerabilities).

When it comes to data privacy, attention should turn to the protective barriers around data. When using patient data, you need to enable some kind of alert. After all, this information can tell the whole story of the patient’s life. It is important to establish controls to allow data isolation. These measures can ensure that the organization is using technology and patient data for a good reason.

Another major ethical concern is the bias that can arise with data collection and use. If you have biased data, the algorithm will also become biased. The information available to the organization may not be representative of the community as a whole. It is crucial to have a diverse coverage. It is critical to have technology that can classify and use such diverse information.

On the other hand, new technology is enabling the healthcare industry to use artificial intelligence and data to treat many diseases — an important advance, no matter how you look at it. At the same time, this same data can improve patients’ well-being.

With the help of technology, healthcare professionals can chop and slice information to better monitor and prevent serious health conditions. If the healthcare industry can overcome the hurdles and enable AI to do more preventable early intervention work, it is entirely possible to deliver higher quality care and lives to people.

Lu Chang He is the founder and managing partner of merger fund. As a Silicon Valley celebrity investor and serial healthcare entrepreneur, Zhang was recently named one of Business Insider’s Top 25 Early Stage Investors.


Welcome to the VentureBeat community!

DataDecisionMakers is where experts, including the technical people who do data work, can share data-related insights and innovations.

If you want to read about cutting-edge ideas, updated information, best practices, and the future of data and data technology, join us at DataDecisionMakers.

You could even consider contributing an article of your own!

Read more from DataDecisionMakers