Applied Evidence

An FP’s guide to AI-enabled clinical decision support

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To better understand the capabilities and challenges of artificial intelligence and machine learning, we look at the role they can play in screening for retinopathy and colon cancer.


› Encourage patients with diabetes who are unwilling to have a regular eye exam to have an artificial intelligence-based retinal scan that can detect retinopathy. B

› Consider using a machine learning-based algorithm to help evaluate the risk of colorectal cancer in patients who are resistant to screening colonoscopy. B

› Question the effectiveness of any artificial intelligence-based software algorithm that has not been validated by at least 2 independent data sets derived from clinical parameters. B

Strength of recommendation (SOR)

A Good-quality patient-oriented evidence
B Inconsistent or limited-quality patient-oriented evidence
C Consensus, usual practice, opinion, disease-oriented evidence, case series



Computer technology and artificial intelligence (AI) have come a long way in several decades:

  • Between 1971 and 1996, access to the Medline database was primarily limited to university libraries and other institutions; in 1997, the database became universally available online as PubMed.1
  • In 2004, the President of the United States issued an executive order that launched a 10-year plan to put electronic health records (EHRs) in place nationwide; EHRs are now employed in nearly 9 of 10 (85.9%) medical offices.2

Over time, numerous online resources sprouted as well, including DxPlain, UpToDate, and Clinical Key, to name a few. These digital tools were impressive for their time, but many of them are now considered “old-school” AI-enabled clinical decision support.

In the past 2 to 3 years, innovative clinicians and technologists have pushed medicine into a new era that takes advantage of machine learning (ML)-enhanced diagnostic aids, software systems that predict disease progression, and advanced clinical pathways to help individualize treatment. Enthusiastic early adopters believe these resources are transforming patient care—although skeptics remain unconvinced, cautioning that they have yet to prove their worth in everyday clinical practice.

In this review, we first analyze the strengths and weaknesses of evidence supporting these tools, then propose a potential role for them in family medicine.

Machine learning takes on retinopathy

The term “artificial intelligence” has been with us for longer than a half century.3 In the broadest sense, AI refers to any computer system capable of automating a process usually performed manually by humans. But the latest innovations in AI take advantage of a subset of AI called “machine learning”: the ability of software systems to learn new functionality or insights on their own, without additional programming from human data engineers. Case in point: A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

A software platform has been developed that is capable of diagnosing or screening for diabetic retinopathy without the involvement of an experienced ophthalmologist.

The landmark study that started clinicians and health care executives thinking seriously about the potential role of ML in medical practice was spearheaded by ­Varun Gulshan, PhD, at Google, and associates from several medical schools.4 Gulshan used an artificial neural network designed to mimic the functions of the human nervous system to analyze more than 128,000 retinal images, looking for evidence of diabetic retinopathy. (See “Deciphering artificial neural networks,” for an explanation of how such networks function.5) The algorithm they employed was compared with the diagnostic skills of several board-certified ophthalmologists.

Continue to: Deciperhing artificial neural networks


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