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Health·The Trace·Dual reading·Published 2026-08-27

adoption and use of medical AI among Chinese oncology healthcare professionals

Source article: Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey

Abstract: Objectives To conduct a nationwide survey among professionals working in oncology departments in China to investigate their attitudes, perceptions, and experiences regarding medical artificial intelligence (AI), and to explore and compare the factors influencing AI behavioral intention (BI; willingness to adopt AI) between physicians and nurses using the Unified Theory of Acceptance and Use of Technology (UTAUT). Materials and methods A nationwide cross-sectional survey was conducted among professionals in oncol…

TRV-2026-0906Peer-reviewedPermanent record — cite & verify
Trace impact reading

Negative state: both sides are scored from claims and sources, not community votes.

P 70The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 65The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Attitudes, perceptions, and UTAUT-based factors influencing the acceptance of medical artificial intelligence among Chinese oncology healthcare professionals: a national cross-sectional survey

Construction site tour of the Skilled Nursing Residence, in Edgartown, on the island of Martha's Vineyard, in Dukes County, Maryland on August 5, 2024 - 33 by USDAgov. Public domain

The quick read

A nationwide cross-sectional survey of 610 oncology professionals in China (188 physicians, 422 nurses) examined attitudes toward medical AI using the UTAUT model. Only 17.7% had both heard of and used medical AI while 67.7% had heard but never used it, indicating an awareness-usage gap.

The analysis matters because trust in system reliability and effort expectancy emerged as key correlates of intention to use AI, with different patterns for physicians versus nurses. Uncertainty remains about whether intention translates into sustained clinical integration and how training or validation protocols would change actual use.

Main points
  • Nationwide survey included 610 valid responses: 188 physicians (30.8%) and 422 nurses (69.2%) from oncology departments across China.
  • UTAUT framework with structural equation modeling tested 6 constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, and trust.
  • Subgroup SEM found for physicians both EE (b2 = .257) and TR (b2 = .486) were significant predictors of behavioral intention.
Gain

Among Chinese oncology professionals, higher trust in system reliability and higher effort expectancy were associated with stronger behavioral intention to adopt medical AI.

Problem

Despite high awareness, most Chinese oncology professionals have not practically integrated medical AI, with only 17.7% reporting both hearing of and using it and 67.7% hearing but never using it.

The rundown

The survey collected sociodemographic characteristics, awareness, perceptions, and user experiences, then applied UTAUT to evaluate perceptions and model associations between behavioral intention and six latent constructs.

Authors conclude hospital management should implement differentiated training and local validation protocols that address unique technical and ethical concerns of physicians and nurses respectively to bridge the gap.

What this doesn’t fix

Findings are based on a cross-sectional survey of 610 oncology professionals in China, limiting causal inference and generalizability beyond this population and setting.

Sources

Reader signal

How should this claim be treated?

The debate