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