Samuelsson, Kristian

AbstractArtificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high‐stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real‐world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality.Level of EvidenceLevel V.

AbstractDeep learning is a subset of artificial intelligence (AI) with enormous potential to transform orthopaedic surgery. As has already become evident with the deployment of Large Language Models (LLMs) like ChatGPT (OpenAI Inc.), deep learning can rapidly enter clinical and surgical practices. As such, it is imperative that orthopaedic surgeons acquire a deeper understanding of the technical terminology, capabilities and limitations associated with deep learning models. The focus of this series thus far has been providing surgeons with an overview of the steps needed to implement a deep learning‐based pipeline, emphasizing some of the important technical details for surgeons to understand as they encounter, evaluate or lead deep learning projects. However, this series would be remiss without providing practical examples of how deep learning models have begun to be deployed and highlighting the areas where the authors feel deep learning may have the most profound potential. While computer vision applications of deep learning were the focus of Parts I and II, due to the enormous impact that natural language processing (NLP) has had in recent months, NLP‐based deep learning models are also discussed in this final part of the series. In this review, three applications that the authors believe can be impacted the most by deep learning but with which many surgeons may not be familiar are discussed: (1) registry construction, (2) diagnostic AI and (3) data privacy. Deep learning‐based registry construction will be essential for the development of more impactful clinical applications, with diagnostic AI being one of those applications likely to augment clinical decision‐making in the near future. As the applications of deep learning continue to grow, the protection of patient information will become increasingly essential; as such, applications of deep learning to enhance data privacy are likely to become more important than ever before.Level of Evidence: Level IV.

Background: Factors relating to the patient and anterior cruciate ligament (ACL) reconstruction may help to identify prognostic factors of long-term outcome after reconstruction. Purpose: To determine 10-year risk factors for inferior knee function after ACL reconstruction. Study Design: Cohort study; Level of evidence, 2. Methods: Prospectively collected data from the Swedish National Knee Ligament Register were extracted for patients who underwent ACL reconstruction between January 2005 and December 2006. Patients who had no data at the 10-year follow-up for the Knee injury and Osteoarthritis Outcome Score (KOOS) were excluded. Multivariable proportional odds regression modeling was used to assess 10-year patient- and surgery-related risk factors across all the KOOS subscales and the KOOS 4 (mean score of 4 subscales: pain, knee-related symptoms, function in sport and recreation, and knee-related quality of life). Results: A total of 874 (41%) patients were included (male, 51.5%; median age at the time of ACL reconstruction, 27.5 years [range, 11.2-61.5 years]). An increase in the severity of concomitant articular cartilage injuries resulted in a reduced KOOS on 4 subscales (odds ratio, 0.64-0.80; P < .05). A higher preoperative KOOS pain score increased the odds of a higher score on the pain, symptoms, and sport subscales and the KOOS 4 . In addition, a higher preoperative body mass index was a significant risk factor for lower scores on 3 KOOS subscales and the KOOS 4 . No patient- or surgery-related predictor was significant across all KOOS subscales. Conclusion: This 10-year risk factor analysis identified several factors that can affect long-term knee function after ACL reconstruction. Most risk factors were related to preoperative patient-reported outcome and potentially modifiable. On the other hand, most of the surgery-related risk factors were nonmodifiable. Nevertheless, this information may be helpful to physicians and physical therapists counseling patients on their expectations of outcome after ACL reconstruction.