Artificial intelligence is already being used in healthcare, for example, to make sense of large amounts of complex data and automate formerly manual processes. AI algorithms are and will be used to assist humans with diagnostics, the analysis of medical images as well as outcomes and for understanding the surgical workflow.
Data in healthcare, as with most sectors, has expanded exponentially since the beginning of the Digital Revolution. In 1950, for example, it took about 50 years for the amount of data in the medical field to double. By 2010, that timeframe had shrunken to 3.5 years. In 2020, the amount of time it took for healthcare data to double was just 73 days1. The sheer volume and speed of available data today holds almost infinite potential.
Compiling, comparing and studying this data could unveil previously unknown information about diseases and their best courses of treatment. It is not feasible, however, for humans to manually process the massive amount of healthcare data available today. That is why we turn to software and algorithms that can process data at speeds and volumes that would be dizzying to the human mind. For these tasks, we need artificial intelligence.
What is artificial intelligence?
Artificial intelligence is intelligent behavior demonstrated by machines. This type of behavior can include perception, natural language processing, knowledge representation, planning, reasoning and learning.
What is machine learning?
Machine learning, currently the most popular way to achieve artificial intelligence, can be defined as computers having the ability to learn with data but without being explicitly programmed. Machine learning can offer state-of-the-art results for many tasks, it can make use and sense of large and complex data, it is often faster than classic algorithms and may continue to learn over time as more data becomes available.
How is artificial intelligence used in healthcare?
Overall, the use of AI in healthcare is focused on streamlining workflows by automating processes that were traditionally done manually and by analyzing large amounts of data to draw conclusions that could impact our understanding of disease and treatments.
AI for diagnostics
Artificial intelligence has been investigated for its potential in diagnostics since the early 1970s with the development of MYCIN at Stanford University, an AI program which attempted to diagnose patients by analyzing test results and reported symptoms2. In the last 50 years, AI for diagnostics has continued to improve including early disease detection. AI for diagnostics can primarily benefit radiologists by automating more time consuming tasks and assisting with case prioritization.
AI for medical images
An example of a direct application of artificial intelligence in healthcare would be the analysis of patient scans. With modern deep learning methods and large amounts of data, algorithms can be trained to extract information from medical images. AI could automatically detect the type of image, identify the depicted body parts, and determine the location of anatomical objects and landmarks. In the brain, which is full of complex structures, it could, for example, be used to differentiate small, difficult to see structures in the deep brain or detect cranial pathologies like tumors.
AI for outcome analysis
Another current example of AI used in healthcare is the mining of patient data for outcome analysis. Data of patient treatments and their outcomes is first gathered in a registry and then AI tools are used to compare and analyze the outcomes of each case to establish trends and ultimately recommend the optimal treatment for a given patient and disease.
AI for understanding the surgical workflow
Machine learning algorithms can also be trained to understand the state and progress of a surgical procedure by looking at video and other sensor data recorded in the operating room. This information can be used to dynamically adapt to the current state of the patient in real time. It can also allow for retrospective analysis and statistical evaluation of surgeries, for example, for the detection of instrument usage or people and their activities in the O.R.
What is the future of artificial intelligence in healthcare?
As with any technology that evolves quickly, it can sometimes be hard to predict what the future holds for AI. What is for certain is that medical data is becoming more digitalized and standardized, which means large, multi-site or even international data pools with consistent information will be available. When trained with this wealth of data, machine learning will become even more capable of automating an expanding number of tasks, increasing both the precision and versatility of such systems.
Since AI algorithms can continue to learn over time, users in the future will automatically profit from treatments they performed in the past, provided their patients give consent to share this information. Systems will automatically adapt to their surroundings and become better and better with every new patient that they “see”.
Regardless of AI’s development in the future, we expect that algorithms will always exist to support—not replace—human beings in the medical field. Working hand-in-hand with advanced software, healthcare professionals will be free to tap into a plethora of possibilities for diagnosis, treatment and follow-up.
How can clinicians benefit from AI now?
AI is already making an impact on healthcare decision-making and treatment planning. To take advantage of the abundance of information AI can provide today, clinicians can choose software applications for their procedures that were developed with machine learning algorithms. When choosing software for your institution, it’s worthwhile to inquire with the vendor about whether AI and machine learning were used to develop their applications and how AI fits into their plans for future software.
Technology of any kind, whether hardware or software, should ideally be future-proof and ready to adapt to the latest innovations. The ability of AI algorithms to grow and adapt is indeed indicative of the direction in which the entire healthcare technology industry is headed.
1 How can a digital operating room bring value into the complex hospital setting? Download the whitepaper now.
2 MYCIN. Encyclopædia Britannica. https://www.britannica.com/technology/MYCIN. Accessed May 4, 2021.