The role of Chief Information Officers (CIOs) has changed over the last decade. The change is driven by the digitization of healthcare and the ability of modern technology to solve…
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Healthcare professionals have used artificial intelligence for decades, starting in the 1970s with rule-based systems that aided in disease diagnosis and drug prescription. These early systems were useful but limited, requiring the manual programming of rules and decision logic, a time-consuming and expensive process. By today’s standards, they barely count as AI.
In the last few years, three developments have contributed to a quantum leap in the use of artificial intelligence in healthcare. The availability of inexpensive high-powered number-crunching hardware makes AI a practical option for healthcare providers. The adoption of electronic healthcare records creates the large healthcare datasets machine-learning algorithms rely on.
The Impact of Machine Learning on Healthcare Leadership
But it is the evolution of machine learning algorithms such as recurrent and convolutional neural networks that has the most significant impact, allowing computer scientists and healthcare professionals to collaborate on a wide array of projects that improve healthcare outcomes.
The application of machine learning to healthcare generates substantial demand for professionals with machine learning expertise. Data scientists are needed, but so are professionals who can integrate systems based on machine learning into the day-to-day workflow of healthcare organizations.
A.I. and Data Collection
Artificial intelligence is used in healthcare for applications as varied as automated data collection and experimentation, biomarker discovery, drug discovery, disease diagnosis, patient monitoring, and treatment selection. Some of the most impactful research relies on computer vision and the ability of deep learning algorithms to analyze and classify images — an advanced application of the technology Google uses to find cat pictures.
Uses in Radiology and Tumor Detection
InnerEye is a research project from Microsoft that uses deep learning algorithms to define the borders of brain tumors. This is an essential step in radiotherapy treatments, and it is usually done manually. A surgeon laboriously marks the edges of a tumor on CT images. To develop a three-dimensional view of the tumor, a surgeon has to repeat the process on dozens of images, each of which displays a slice of the brain. InnerEye can analyze the images and create a three-dimensional representation in seconds.
Early Diagnoses for Hard-to-Detect Conditions
Diabetic retinopathy is a side effect of diabetes that causes blindness in millions of people across the world. Almost 30% of individuals with diabetes in the US are affected by diabetic retinopathy, which can be treated if diagnosed early. Researchers from Google Research have developed a deep learning system that can identify signs of retinopathy from fundus (back of the eye) images. The hope is that automatic diabetic retinopathy diagnosis will improve “patient outcomes by providing early detection and treatment.”
Empower Your Healthcare Leadership
Tumor demarcation and diabetic retinopathy diagnosis are just two of the hundreds of areas in which artificial intelligence is transforming healthcare. Professionals in radiology, dermatology, ophthalmology, pathology and more are working to reap the benefits of machine learning and integrate the insights it provides into the daily workflow of thousands of healthcare workers.
Businesses with leadership expertise at the intersection of healthcare and artificial intelligence will develop products and services that reshape the healthcare industry. To find out how your business can hire executives with a proven track record and AI expertise, get in touch today.
