A recent review by scholars including Sage Kelly, Sherrie-Anne Kaye, and Oscar Oviedo-Trespalacios looked at 60 studies about AI acceptance from many industries. These 60 were chosen out of almost 8,000 articles. The review showed that almost half of these studies (31 out of 60) did not clearly say what AI means in their papers. Also, 38 studies did not explain AI to the participants who took part.
This unclear definition causes big problems in understanding the results. If people do not agree on what AI means, their answers about using AI might be mixed up or wrong. For example, some might think AI is just automated software, while others believe it includes smart things like machine learning or natural language processing. This mix-up can change how much people trust AI and how useful they think it is. These feelings are important for whether they accept AI or not.
Not defining AI well makes it hard to trust these studies. This weakens their value, especially for decisions about using AI in healthcare and other fields.
In healthcare, privacy, accuracy, and reliability are very important. So, it is important to know how staff and patients see AI tools. Medical leaders and IT managers in clinics and hospitals often use studies about AI acceptance to decide on AI tools like virtual front-office helpers, phone answering automation, or AI chatbots for managing appointments.
If a study says people are ready to use AI answering services but does not explain what AI is or how it works, healthcare leaders may make wrong decisions based on wrong ideas. Clear information about AI’s abilities and limits helps leaders choose the right tools and prepare the staff better.
Also, using AI in healthcare has special challenges like following privacy laws (such as HIPAA), keeping data safe, and keeping personal care where human feelings matter. Without a clear AI definition, staff may fear or not trust new technology. Giving everyone the same clear idea about AI in studies helps find real reasons why people might not want AI. This way, training and communication can be improved to help acceptance.
The most used model to study AI acceptance is called the extended Technology Acceptance Model (TAM). This model looks at different factors that explain if a person wants to use AI or likes it. These factors include:
These factors influence whether people plan to accept or refuse AI in healthcare. For example, if a receptionist finds an AI answering system easy to use and it helps with work, they may trust it more and accept it.
However, the review found that in some cultures where personal contact is very important, AI cannot replace talking to a real person, no matter how easy or useful it is. This is true in healthcare in the United States, where patient care needs empathy, careful communication, and trust—things hard for AI to do.
Healthcare depends much on human relationships. Many patients want to talk to a real person, especially when sharing private medical information or needing emotional support. This affects how much they accept AI tools in roles like scheduling appointments, answering phones, or giving first advice.
While AI can manage routine questions and admin work well, healthcare staff must balance automation with the need for human contact. The review showed that many people prefer human interaction. So, healthcare leaders should use AI to help staff, not to replace them fully.
Training programs that explain how AI supports humans, not replaces them, may help people accept it more. Also, telling patients and staff clearly when AI is used and when a human will take over helps keep trust.
One big benefit of AI for healthcare leaders is automating workflows. AI-powered front-office phone systems, like Simbo AI, can handle many tasks that take time, such as answering common patient questions, scheduling, sending reminders, and managing busy call times.
By automating routine work, staff can spend more time on complex tasks that need human judgment. AI can also reduce mistakes by recording patient information correctly, lowering missed calls, avoiding scheduling mix-ups, and making it easier for patients to get help.
Simbo AI uses advanced natural language processing. This lets patients talk naturally with the system for a better experience. Older automated systems had fixed menus and answers, but new AI can understand what people want and handle many types of questions like a conversation. This helps busy clinics or hospitals in the U.S. by managing calls after hours, reducing patient wait times, and making patients happier.
Healthcare IT managers need to know if staff accept these AI tools. If staff think AI is useful and easy—and worries about jobs or trust are dealt with—then AI is likely to be successful.
Also, AI helps with following rules. It keeps records of calls and data, making sure patient information is handled safely. This helps healthcare organizations meet legal requirements while working better.
The review about AI acceptance also showed a method problem: Most studies use only self-reported data and not real observation of AI use. This limits how much we can trust their results about real AI use.
By clearly explaining AI in study materials and teaching participants, researchers can get better data on how people actually use different AI tools. This helps tell apart simple acceptance based on wrong ideas from true acceptance based on real experience.
Future studies should also consider culture and social factors. They should look at how workplace habits, past knowledge, and worries like fear of losing jobs change how people feel about AI. This is especially important for hospital leaders working with many kinds of staff.
Though this article focuses on healthcare, the findings apply to other industries in the U.S. where AI is growing. Banks, stores, factories, and customer service all use AI to get work done better and improve customer experience.
One clear lesson is that studies and surveys about AI acceptance must give a clear and consistent definition of AI. They must explain what AI can do, what it can’t do, and how it is expected to be used.
This helps measure how people really feel about AI and stop wrong ideas, either too much excitement or too much fear. It also helps companies plan good training and clear communication to fix worries like trust and ease-of-use that affect acceptance.
By using this thoughtful plan, healthcare groups can increase acceptance of AI, lower staff worries, and use AI tools to create safer and more efficient places for patients and workers.
The review focused on user acceptance of artificial intelligence (AI) technology across multiple industries, investigating behavioral intention or willingness to use, buy, or try AI-based goods or services.
A total of 60 articles were included in the review after screening 7912 articles from multiple databases.
The extended Technology Acceptance Model (TAM) was the most frequently employed theory for evaluating user acceptance of AI technologies.
Perceived usefulness, performance expectancy, attitudes, trust, and effort expectancy were significant positive predictors of behavioral intention, willingness, and use of AI.
Yes, in some cultural situations, the intrinsic need for human contact could not be replaced or replicated by AI, regardless of its perceived usefulness or ease of use.
There is a lack of systematic synthesis and definition of AI in studies, and most rely on self-reported data, limiting understanding of actual AI technology adoption.
Future studies should use naturalistic methods to validate theoretical models predicting AI adoption and examine biases such as job security concerns and pre-existing knowledge influencing user intentions.
Acceptance is defined as the behavioral intention or willingness to use, buy, or try an AI good or service.
Only 22 out of the 60 studies defined AI for their participants; 38 studies did not provide a definition.
The acceptance factors applied across multiple industries, though the article does not specify particular sectors but implies broad applicability in personal, industrial, and social contexts.
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