AI: The Importance of Governance A

By Joseph L. Marion and Henri Primo

rtifi cial intelligence (AI) is one of the more promising technologies in medi- cal imaging of the 21st century so far,

with widespread potential in healthcare. New applications are emerging every day in both diagnostic imaging, as well as in many workfl ows and analytical areas, driven by the changing norms of value-based care and population health management. Before AI grows exponentially, the

authors believe that AI governance is critical to an effi cacious and cost-effective implementation of the many AI-based applications. Healthcare providers can learn from other experiences in health- care, such as cardiovascular information systems. Cardiovascular services tend to be diverse in nature with invasive, non- invasive, and event-recording applications. As systems emerged, no single vendor seemed to have an all-inclusive solution, and consequently different department sections acquired what was best for them. The net effect was a proliferation of systems that, in many cases, did not interoperate well and made assimilation of the comprehensive patient information by the cardiovascular specialist diffi cult. Similar in nature has been the applica-

tion of advanced visualization. There can be multiple departments with advanced visualization needs, such as diagnostic radiology, orthopedics, surgery, etc. Each of these areas may have pursued a particular

vendor’s solution—again resulting in a proliferation of systems with overlapping functionality and poor interoperability. In order to avoid similar experiences

with AI, providing proper governance can potentially minimize organiza- tional

inconsistencies, inefficiencies,

and expenses. We will review the cur- rent state of AI through examination of some real-world experiences, and then explore what is needed in AI to avoid the mistakes of the past.

Current state of AI In order to understand the current state of AI and governance we must understand how AI has evolved. First, let us differen- tiate between imaging and non-imaging AI. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can.These algorithms use statistics to fi nd patterns in massive amounts of data. Imaging AI focuses on a branch of com- puter science dealing with the acquisition, reconstruction, analysis and/or interpre- tation of medical images by simulating human intelligent behavior in comput- ers. Machine learning algorithms are a subset of artifi cial intelligence methods, characterized by the fact that you do not have to tell the computer how to solve

12 | NOVEMBER/DECEMBER 2021 Joseph L. Marion Henri Primo

the problem in advance. Instead, the computer learns to solve tasks by recog- nizing patterns in the data. By analyzing thousands of similar images looking for specifi c patterns, the computer is able to predict if a certain pattern is representa- tive of a particular diagnosis. Non-imaging AI also employs algo- rithms, but instead of analyzing image content, they may look for patterns in data that are relevant. For example, if a patient has had multiple exams that include calculation of an ejection fraction, an algorithm that examines a vast amount of data looking for ejection fraction values and compares them would be helpful to the clinician. Similarly, an algorithm that uses machine learning to examine a number of patient parameters such as age, mobility, prior visits, etc. might be used to assess whether a patient is likely to be a no-show for an appointment, and recommend preemptive action.

Photo 156913026 / Artifi cial Intelligence © Pavlo I |

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