A population-based United Kingdom (UK) cohort study assessed the underlying cancer risk of patients aged 30 years and older who presented to primary care with new-onset fatigue and co-occurring vague symptoms. After excluding persons with anemia or alarm symptoms (e.g., dysphagia), researchers followed patients for up to 9 months for a diagnosis of cancer. Cancer risk increased with age, reaching 3% or more in patients in their mid-60s. For all age groups combined, cancer risk was highest for women with fatigue and abdominal bloating and men with fatigue and weight loss, constipation, dyspnea, or abdominal pain.
A Danish research group developed an artificial intelligence (AI) based model that used results from common blood tests (complete blood count, electrolytes, and/or liver function tests) to generate a risk score that predicted cancer within 90 days. This laboratory data is often readily available; another UK study of patients who were diagnosed with cancer found that 41% received common blood tests in primary care as part of their diagnostic process. However, relying solely on blood tests neglects the value of primary care physicians’ non-analytical “gut feelings” that the patient has a benign or serious condition.
In an observational study of 155 general practitioners in Spain, a “sense of alarm,” present in 22% of consultations for new symptoms, had a sensitivity of 59% for cancer and other serious diseases and a negative predictive value of 98%. Thus, AI may also assist in cancer diagnosis by imitating the intuitive behavior of groups of family physicians. In an editorial in Annals of Internal Medicine, Dr. Gary Weissman and colleagues proposed that AI clinical decision support systems (CDSSs) utilize a “wisdom of crowds” approach that, like the best chess-playing AI systems, “reli[es] on imitation learning and collective intelligence” rather than set rules:
Averaging the judgments of many clinicians may outperform even the best clinician in the group. Training models to learn these consensus behaviors could lead to clinically significant improvements in accuracy. Furthermore, most diagnostic errors are the result of overlooking common diagnoses rather than very rare ones. … An AI CDSS that offers human-like suggestions may improve the reliability of clinical care by helping to avoid these clinical blunders. … Having an AI system that acts more like a thoughtful human guide rather than a black-box arbiter of truth may be the best next move.
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This post first appeared on the AFP Community Blog.