Artificial Intelligence Applications in Medicine: Interview with Expert Dr Isaac Kohane
Less complicated tasks of radiologists could be completed by AI.
Several studies and academic articles recently published offer updates regarding the use of artificial intelligence (AI) in medicine. Topics span a range of applications and specialties, including endocrinology, neurology, radiology, cardiology, pain care, and more.
In the field of radiology, a paper in JAMA described a computer that developed algorithms which enabled it to accurately detect fractures on radiographs.1 The authors proposed that the less complex tasks of radiologists, such as the detection of lung nodules on computer tomography, could ultimately be delegated to AI.
Concerning neurology, an article in Movement Disorders explored the potential of machine-learning algorithms to transform data from wearable, sensor-based systems that quantify signs of Parkinson's disease into meaningful information that could significantly improve research and clinical practice.2
Within cardiology, researchers at the University of Ioannina in Greece and the University of Lyon in France tested algorithms designed to classify patients with heart failure as medication adherent and global adherent (pertaining to medication, nutrition, and physical activity). Their results showed detection accuracy rates of 82% and 91% for each respective measure.3 Additionally, new research reported in JAMA found high specificity and sensitivity of algorithms designed to detect referable diabetic retinopathy.4
In response to that paper, Isaac Kohane, MD, PhD, chair of the Department of Biomedical Informatics and professor of Biomedical Informatics and Pediatrics at Harvard Medical School in Boston, and Andrew L. Beam, PhD, a postdoctoral fellow who works with Dr Kohane, published an editorial on the topic of transforming AI in to clinical care.5
Cardiology Advisor recently interviewed Dr Kohane to better understand the role of artificial intelligence in medicine (AIM).
Cardiology Advisor: Since readers may be unfamiliar with the use of AIM, could you describe what it means and a few of the top ways it is currently being used?
Dr Kohane: In many ways, the definition AIM has been slippery because some of the tasks that we used to think of as requiring uniquely human expertise have been effectively implemented as computer programs that are used routinely and therefore no longer seen as requiring “intelligence.” For example, in the past, if you would have told the average physician that computers could do a good job of characterizing arrhythmias in an ECG or interpreting gene expression patterns to predict breast cancer recurrence risk, they might have exclaimed, “It's an expert!” But now we just think of those tasks as mechanical.
So, to paraphrase William Schwartz from a 1970 article, I would say that AIM is the use of computing as an intellectual tool to assist in clinical assessment and decision making.6 [Editor's note: William B. Schwartz, MD, was one of the first pioneers in the field of AIM.] I already gave a few examples, but in our recent editorial we highlighted the success of machine learning methods and deep neural networks in particular at performing at the expert level in image classification—the classification of retinopathy in that specific article, but clearly similar work is being followed for a number of clinical imaging modalities.5
Cardiology Advisor: What are your thoughts on why, as mentioned in your editorial, the promise of AI to transform medicine has not yet fully materialized?
Dr Kohane: There are many causes, but at the root it is a failure on the part of the medical system and the medical educational system to recognize that at its core, medicine is an information and knowledge processing discipline. You take in data from the patient, assess it based on what you know from other patients and compiled knowledge sources, and then transmute that assessment into a set of therapeutic actions that will be continually reviewed and revised based on the patient's response.