JMIR Publications

ABSTRACT

Background:

Despite the promising potential of artificial intelligence (AI) in the perioperative context, the rapid pace of development and diverse implementation warrants a systematic review to consolidate existing knowledge, identify gaps, and assess the utilization of trustworthiness principles of AI integration into the perioperative period for patients with serious illness.

Objective:

The purpose of this study was to address deficiencies in perioperative AI literature by elucidating the extent to which equity, ethics, and safety discussions are incorporated, thereby establishing a foundation for developing robust ethical guidelines for the safe and effective integration of AI in healthcare. This study also examined the utilization of AI enabled team augmentation in perioperative serious illness care.

Methods:

We searched PubMed, Embase, CENTRAL, and Scopus for studies published between 2010 to July 2024. We included studies that reported patient functional outcomes, occurred in the perioperative period (30 days before and up to 90 days after surgery), included AI integration, and included patients with serious illness (defined as: malignancy, advanced organ failure, frailty, dementia/neurodegenerative disease, or stroke). To ensure reliability and minimize bias, two independent reviewers screened all studies through the title/ abstract and full-text stage; conflicts were resolved through team consensus. The abstraction form was developed iteratively and was tested through pilot abstractions. Any discrepancies identified during data extraction were resolved through discussion and consensus among the reviewers. The ROBINS-I risk of bias tool in non-randomized studies was used to assess quality. Abstraction and risk assessment occurred through a blinded, independent dual review. A narrative review was compiled with the identified studies.

Results:

Of the 10,980 articles identified through the database searches, this review yielded 81 articles that met inclusion criteria. A majority of the studies were published in China (35), with the United States (9) and South Korea (7) having the subsequent most publications, and 80 out of 81 (98.8%) articles focused on patients with malignancy. Analysis of AI implementation strategies revealed foundational efforts toward equitable access, with six studies providing open-access tools and several more designing models with simple inputs suitable for low-resource settings (17). Seven studies mentioned their commitment to transparency (e.g., publishing code) to enhance safety and trust. However, significant ethical deficiencies persist, particularly around input data, as only two studies explicitly addressed racial or ethnic disparities, and concerns about lack of sample diversity (16) and the omission of socially relevant features (5) were frequently noted as limitations. Although no current studies considered AI enabled team augmentation, a majority of articles described how AI could be used to prompt a team member to make a tangible action.

Conclusions:

Machine learning for predictive analytics and other types of AI tools in surgical outcomes offers significant potential but requires adherence to trustworthiness and safety principles to be clinically viable. By leveraging longitudinal data and continuous performance tracking, these models have the potential positive impact on diverse patient needs and healthcare systems. Future research should prioritize adhering to guidelines for equity, ethics, and safety, conduct prospective studies, incorporate more external validation of AI models, and facilitate transparent monitoring and reporting of model performance to build clinician and patient trust and to encourage broader healthcare system adoption. Clinical Trial: PROSPERO CRD42024608387