Editorial Commentary: Machine Learning and Artificial Intelligence Are Tools Requiring Physician and Patient Input When Screening Patients at Risk for Extended, Postoperative Opioid Use

As the implementation of artificial intelligence in orthopedic surgery research flourishes, so grows the need for responsible use. Related research requires clear reporting of algorithmic error rates. Recent studies show that preoperative opioid use, male sex, and greater body mass index are risk factors for extended, postoperative opioid use, but may result in high false positive rates. Thus, to be applied clinically when screening patients, these tools require physician and patient input, and nuanced interpretation, as the utility of these screening tools diminish without providers interpreting and acting on the information. Machine learning and artificial intelligence should be viewed as tools that can facilitate these human conversations among patients, orthopedic surgeons, and health care providers.

Automated summary

As artificial intelligence is increasingly used in orthopedic surgery research, responsible use becomes more necessary. Research in this field should report algorithmic error rates clearly. Recent studies indicate that factors such as preoperative opioid use, male sex, and higher body mass index are associated with extended postoperative opioid use, but may also have high false positive rates. Therefore, to be used clinically, these screening tools require input from physicians and patients, as well as nuanced interpretation. Machine learning and artificial intelligence should be seen as tools that facilitate human conversations among patients, orthopedic surgeons, and healthcare providers.