With artificial intelligence incessantly reshaping diagnostic paradigms, the utility of deep learning in identifying osteoporosis merits rigorous evaluation. Firouz Amani et al.’s work attempts to do just that: quantifying the diagnostic validity of deep learning models for osteoporosis prediction. Pursuant to PRISMA guidelines, the authors conducted a systematic review and meta-analysis by querying PubMed, Elsevier, and Google Scholar up until December 1st, 2023. Such reviews were guided by prespecified eligibility criteria and standardized data extraction protocols that zeroed in on diagnostic metrics. The researchers used CNN-based models that were evaluated across ten selected studies, with a principal emphasis on lumbar spine and hip imaging modalities. Taking into account the pooled dataset of sensitivity and specificity, these DL models achieved rates of 0.86 and 0.89, respectively, in addition to an AUC of 0.94, ultimately exemplifying robust diagnostic capability. The Diagnostic Odds Ratio (DOR) illustrated was 49.09, with no overbearing publication bias detected (Deeks’ test, P=0.4). The study finalizes their assessment by stating that DL algorithms—with CNNs in particular—demonstrate great potential for osteoporosis prediction. Be that as it may, however, further clinical validation is needed to assess their generalizability in routine practice.