Objective physical function assessment is crucial for determining patient eligibility for treatment and adjusting the treatment intensity. Existing assessments, such as performance status, are not well standardized, despite their frequent use in daily clinical practice. This paper explored how artificial intelligence (AI) could predict physical function scores from various patient data sources and reviewed methods to measure objective physical function using this technology. This review included relevant articles published in English that were retrieved from PubMed. These studies utilized AI technology to predict physical function indices from patient data extracted from videos, sensors, or electronic health records, thereby eliminating manual measurements. Studies that used AI technology solely to automate traditional evaluations were excluded. These technologies are recommended for future clinical systems that perform repeated objective physical function assessments in all patients without requiring extra time, personnel, or resources. This enables the detection of minimal changes in a patient’s condition, enabling early intervention and enhanced outcomes.

Assessing objective physical function in patients with cancer is crucial for evaluating their ability to tolerate invasive treatments. Current assessment methods, such as the timed up and go (TUG) test and the short physical performance battery, tend to require additional resources and time, limiting their practicality in routine clinical practice. To address these challenges, we developed a system to assess physical function based on movements observed during clinical consultations and aimed to explore relevant features from inertial measurement unit data collected during those movements. As for the flow of the research, we first collected inertial measurement unit data from 61 patients with cancer while they replicated a series of movements in a consultation room. We then conducted correlation analyses to identify keypoints of focus and developed machine learning models to predict the TUG test outcomes using the extracted features. Regarding results, pelvic velocity variability (PVV) was identified using Lasso regression. A linear regression model using PVV as the input variable achieved a mean absolute error of 1.322 s and a correlation of 0.713 with the measured TUG results during five-fold cross-validation. Higher PVV correlated with shorter TUG test results. These findings provide a foundation for the development of an artificial intelligence-based physical function assessment system that operates without the need for additional resources.