A recent review concluded that artificial intelligence (AI) has significant value in supporting clinical decision-making across the arthroplasty care pathway. The primary benefit is the optimization of patient outcomes through data-driven approaches, including enhanced preoperative planning, patient selection, risk stratification, and outcome prediction. Additionally, it assists in predicting complications, patient-reported outcomes, and prolonged opioid use, thereby improving earlier personalized interventions. Integrating AI with clinical data helps create tailored care pathways, reducing the time and costs for older, comorbid patients.
The review was published in December 2025 in the Journal Cureus.
Growing Role of AI in Clinical Decision-Making in Arthroplasty Procedure Continuum
Machine learning (ML), a type of AI, is used in many medical fields, including orthopaedic surgery.
- AI improves orthopaedic practice by improving diagnosis, decision-making, surgery, implant type identification, and administration.
- AI/ML models predict the costs of hip and knee replacements by assessing the surgical needs of older patients with other health conditions.
- These tools help create treatment plans by recognizing past implants and sorting candidates for knee replacement based on their risk factors.
- AI/ML models also predict complications and outcomes after surgery, such as how well patients will function and their reported experiences post-surgery.
- They accurately predict postsurgical pain levels and identify patients who may require long-term opioid prescriptions, thereby improving pain management and opioid prescribing.
- These tools also help tailor treatment plans, including consideration of non-surgical options when there is a high risk of requiring revision surgery.
AI Assistance in Preoperative Planning
The use of AI in preoperative planning makes joint replacement surgery smoother and more efficient. The authors suggest using AI for preoperative revision surgery planning, checking for bone loss or fractures, and managing data records. For a successful revision surgery, it is important to know the exact implant used previously. However, many patients lack surgical records, making preoperative planning difficult. Surgeons often have to use X-rays and incomplete records to determine the same. AI systems can assist by analyzing X-rays to quickly identify implant designs based on their shape and size. These systems learn from labelled images and can identify implants in seconds, thus saving time and ensuring that the right tools are ready for surgery, thereby reducing complications. AI can also help estimate the hip joint center more accurately, thereby improving surgical planning.
AI Assistance for Predicting Postoperative Outcomes
Current evidence suggests that AI can support more personalized care pathways in arthroplasty by improving patient selection, setting realistic expectations, guiding resource use, and enabling earlier targeted interventions. These tools can help estimate which patients are likely to benefit most from surgery, achieve meaningful improvements in patient-reported outcomes, and experience dissatisfaction after the procedure. By identifying higher-risk patients early, clinicians can improve shared decision-making, optimize patients before surgery, and plan closer postoperative monitoring. They can also predict important postoperative issues, such as prolonged hospital stay, complications, treatment success for infections, and long-term opioid use.
Clinical Implications
AI in arthroplasty care enhances preoperative planning, intraoperative preparation, and postoperative management of hip and knee replacements.
- Preoperatively, AI recognizes implant designs on radiographs, supports revision planning, and estimates the hip joint center for complex cases. It also assists with patient selection and risk stratification, predicts readmission, and guides resource use.
- Postoperatively, learning algorithms can predict complications, pain levels, dissatisfaction, patient-reported outcome measures (PROMs), and assess the requirement for long-term opioid use, allowing for targeted care.
- Other benefits include faster implant identification for revision surgery and personalized care plans that save time and costs for older patients.
The literature review supports the view that integrating AI with clinical and imaging data may improve decision-making from preoperative assessment to follow-up. However, real-world evidence and data standards are required to make these tools reliable in clinical practice.
Reference: Sayed A, Elkohail A, Soffar A, et al. Current Concepts in Artificial Intelligence-Assisted Arthroplasty: A Review of the Perioperative Pathway. Cureus. 2025;17(12):e99946. Published 2025 Dec 23. doi:10.7759/cureus.99946
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