Predicting cartilage failure or predicting biological failure?Machine learning still does not examine the patient To the Editor, We read with great interest the article by Gilat et al, "Evidence-based machine learning algorithm to predict failure following cartilage procedures in the knee," recently published in the Journal of Cartilage & Joint Preservation. 1The authors should be congratulated for their large cohort, robust methodology, and transparent reporting.Their work represents a meaningful step toward data-driven decision support in cartilage surgery.Nevertheless, an important conceptual distinction warrants further discussion: the difference between predicting cartilage procedure failure and predicting biological failure of the joint environment. 2he proposed machine learning models identify predictors largely related to patient demographics, symptom duration, lesion characteristics, and prior surgical history. 1 While these variables are clinically relevant, they primarily act as mechanical or historical surrogates rather than direct indicators of the biological capacity for cartilage repair.As such, the algorithm appears to predict patterns of previous failure more than the biological mechanisms that ultimately determine success or failure.Cartilage repair success is fundamentally driven by biological processes, including cell viability, inflammatory milieu, subchondral bone health, metabolic status, host regenerative potential, and the increasingly recognized muscle-cartilage crosstalk, which plays a key role in joint homeostasis through neuromuscular control, mechanical loading, and local biochemical signaling. 2,3None of these biological dimensions can be adequately captured by demographic variables or lesion morphology alone, nor are they directly assessed within the current algorithmic framework.Furthermore, machine learning models do not "examine" the patient.They cannot account for pain behavior, functional muscle status, psychosocial factors, rehabilitation adherence, or subtle clinical findings that often emerge only during direct physician-patient interaction. 4These elements have been shown to substantially influence cartilage repair outcomes and joint preservation strategies but remain largely invisible to purely data-driven models.The authors appropriately acknowledge that machine learning cannot replace clinical acumen.Without the incorporation of biologically meaningful variables, such as advanced imaging of the subchondral bone, inflammatory or metabolic biomarkers, and objective assessment of muscle function, machine learning risks being perceived as predictive of biology, while in reality it reflects historical clinical associations.In conclusion, machine learning may assist in predicting cartilage procedure failure, but predicting biological failure remains a far more complex challenge, one that still requires careful clinical evaluation and a comprehensive understanding of the patient as a biological system rather than a collection of features.
Pegreffi et al. (Sun,) studied this question.