ABSTRACT
Importance: An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer). Objectives: To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation. Evidence Review: A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed. Findings: A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I. Conclusions and Relevance: This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.
Subject(s)
Clinical Competence , Decision Support Systems, Clinical , Machine Learning , HumansABSTRACT
Ghrelin is a peptide hormone that is primarily released from the stomach. It is best known for its role in appetite initiation. However, evidence also suggests that ghrelin may play a much wider role in energy homeostasis and glucose metabolism. It is known that exogenous ghrelin exerts an orexigenic signal via growth hormone secretagogue receptors in the arcuate nucleus of the hypothalamus. However, blocking ghrelin signalling in the arcuate nucleus does not decrease feeding. Evidence now proposes that an alternative pathway for ghrelin's action is via the vagus nerve. Furthermore, it has been suggested that ghrelin signalling is an important physiological regulator of body adiposity and energy storage. Ghrelin also seems to be important in controlling glucose metabolism through action in the pancreatic islets of Langerhans, representing a promising novel therapeutic target in diabetes treatment. Despite these findings, further research in humans is required before ghrelin can be indicated as a therapeutic target in obesity or diabetes. This review summarises the current literature concerning ghrelin's physiological roles in energy and glucose homeostasis.