Customer Acceptance of Artificial Intelligence in Healthcare: A Systematic Literature Review and Proposition of Conceptual Framework for Future Research

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Customer Acceptance of Artificial Intelligence in Healthcare: A Systematic Literature Review and Proposition of Conceptual Framework for Future Research

https://doi.org/10.21203/rs.3.rs-4979499/v1

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Artificial Intelligence (AI) is transforming the healthcare sector worldwide. AI solutions are improving healthcare by complementing workforce requirements and enhancing the quality of care through early diagnosis, robot-assisted surgery, remote patient monitoring, electronic healthcare record keeping, etc. Customer adoption is crucial for the successful implementation of AI in healthcare. There has been an increase in academic research on customer adoption of AI in healthcare. Through a systematic literature review, this study tries to determine the state of the art of customer acceptance of AI in healthcare along with factors affecting customer adoption of AI in healthcare. The authors appliedthe Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for article search and selection for the review. A total of 3732 articles were identified for the screening after removing duplicates, and one hundred and twenty-six articles fulfilled the inclusion and exclusion criteria. The selected articles were analyzed using the Theory, Characteristics, Contexts, and Methods (TCCM) framework. Among the articles that fulfilled the inclusion criteria in the study, the most commonly used methodology and theoretical foundation were the survey method and the Unified Theory of Acceptance and Use of Technology (UTAUT), respectively. Performance expectancy, effort expectancy, privacy concerns, etc., are some of the key factors affecting customer adoption of AI in healthcare. This review leads to a conceptual research framework of Antecedents, Service encounters, and outcomes (A-S-O) for future research on customer acceptance of AI in healthcare.

Customer Acceptance

Consumer Behaviour

Electronic Health Records

Artificial Intelligence

Healthcare

Robot-assisted surgery

John McCarthy defined Artificial Intelligence (AI) as “the science and engineering of making intelligent machines” (McCarthy, 2007; p. 2). Tegmark (2017) described AI as non-biological intelligence at its basic level. AI may also be defined as the use of coded computer algorithms with specific instructions to perform a task for which a human brain is usually considered necessary. AI enables computer software to model intelligent behaviour with minimal human effort to perform a specific job (Shariati et al., 2019).

At the initial level of development of the term AI, there was much enthusiasm within the scientific community. However, AI has been utilized in a very limited number of industries. AI became a subject of art on which several fictional movies were made in Hollywood and other film industries. But in the real world, AI found minimal application.

But with the advancement of computational capabilities and technological revolution (Kaushal et al., 2023), AI started getting real-world applications in the late 1990s. Hence, it is no longer science fiction. AI solutions are now embedded in our daily activities. AI is transforming several sectors, like retail, customer engagement, astronomy, gaming, e-commerce, finance, data security, healthcare, and countless other industries. AI is enabling self-care through monitoring of health-related essential vitals.

There is a significant transition taking place in the demography of the world; the average age of the population is increasing, World Health Organization (WHO) and United Nations (UN) estimated that by 2025, 70% of all illnesses would be comorbid and chronic in nature, early diagnosis is the key for better management and cures for the disease, AI-based technologies are proving to be helpful for the transition of healthcare management from reactive to proactive; patient-centric care like personalized medicines are currently being applied in the area of healthcare. Global healthcare costs are expected to rise from 8.4 trillion US$ in 2015 to 18.3 trillion in 2030, and AI-based technologies will take a big chunk of worldwide healthcare expenditure (Bohr & Memarzadeh, 2020).

Healthcare professionals worldwide are facing immense and unprecedented pressure to utilize technology due to changes in demographics, changes in lifestyles, increased morbidity, more administrative requirements, and shortages of workforce. AI is showing promising potential to address the scarcity of the healthcare workforce, and it is being utilized in healthcare administration, clinical decision support, patient monitoring, and healthcare interventions like robotic support for elderly individuals (Reddy et al., 2019).

Healthcare is an area where technologies are collecting a lot of data about patients. Data security, privacy concerns, and information aspects are critical for ethical, legal, and customer acceptance perspectives. Information security involves various stakeholders protecting and optimally utilizing the data, innovators by creating sustainable solutions, providers by following regulations, and patients adhering to the instructions provided by the clinicians (Hlavka, 2020).

Review articles provide an understanding of the state of the knowledge available in the domain and the direction for future research on a particular topic of relevance (Paul et al., 2021; Paul & Criado, 2020; Tsiotsou et al., 2022). Literature review contributes to advancing the subject areas by synthesizing the current work and by identifying the research gap (Kumar et al., 2020; Paul et al., 2021). The literature review provides future direction in a subject area by examining the existing literature in a transparent, rigorous, and easy-to-replicate manner (Paul et al., 2021; Paul & Criado, 2020; Snyder, 2019). Various types of review articles, like systematic literature reviews (SLR), bibliometric analysis, and thematic reviews, provide novel insights for future research by giving an understanding of the current status of the work published in academic journals (Dwivedi et al., 2019; Jeyaraj & Dwivedi, 2020; Jones & Gatrell, 2014; Palmatier et al., 2018; Paul et al., 2021). Several literature reviews are done in the context of AI in healthcare and customer adoption of AI. Some of the prominent studies are presented in Table 1.

Table 1 List of review papers (in chronological order) (Source: Compiled by the authors)

Ali et al. (2023) reviewed articles based on AI in healthcare. They identified several benefits, like increased efficiency and accuracy, and challenges, like privacy issues. They observed that academic research in AI in healthcare is still emerging. Secinaro et al. (2021) found out some key application areas like patient data and diagnostics, health services management, predictive medicine, and clinical decision-making through their systematic literature review of AI in healthcare articles. They have also identified that most studies are done in the United States of America (USA), the United Kingdom (UK), and China.

AI researchers have traditionally been focused on technological aspects of human behaviour. AI developers take the human perspective to develop mathematical models based on AI mimicking human logic. Multidisciplinary research has increased significantly in the last two decades, from a strict focus on technology objectives to an interest in human users’ perspective (Glikson & Woolley, 2020).

The literature review articles presented in Table 1 have primarily focused on AI applications in healthcare, marketing, and consumer research (Ali et al., 2023; Ling et al., 2021; Mariani et al., 2022; Secinaro et al. 2021; Verma et al. 2021; Zahlan et al. 2023). Some of the literature articles also focus on customer adoption of AI in several industries except healthcare (Chi et al., 2020; Glikson & Woolley, 2020; He & Zhang, 2023; Lim et al., 2022). Customer adoption of AI is essential for successfully implementing healthcare AI solutions in clinical practice. Many studies are being conducted in the area of patient and public acceptance of AI in healthcare. In recent years, AI-based solutions have been employed all over the world in different domains of healthcare. Consequently, academic research on patient and customer adoption of AI in healthcare is also increasing.

This review article summarizes the existing literature on customer adoption by answering the following research questions.

Q1. What are the factors affecting customer adoption of AI in healthcare?

Q2. What are the various costs and benefits associated with adopting AI in healthcare from the customers' point of view?

Q3. What is the relationship among the various factors affecting customer adoption of AI in healthcare?

Q4. What are the major theories researchers use to understand customer acceptance of AI in healthcare?

These research questions lead us to the following objectives for this systematic literature review:

1. To identify the factors affecting customer adoption of AI in healthcare.

2. To investigate the psychological factors and perceived costs and benefits in customer adoption of AI

3. To examine the relationships among the antecedents, mediators, moderators, and outcome variables affecting customer adoption of AI in healthcare.

4. To determine the theoretical foundations of customer adoption of AI in healthcare

To the best of our knowledge, no systematic literature review is available regarding customer patient or public acceptance of AI in healthcare. So, to provide an overview of the current status and future research direction, we have conducted a framework-based systematic literature review on customer acceptance of AI in healthcare. In the area of healthcare AI, customers can be the healthcare workers, patients, or the public at large, but we have restricted our study to the patients and public only, not the healthcare workers, so if there is any mention of customers, there in the article, that means the patient, their relatives and the public at large.

This study reviews the research conducted on customer acceptance of AI in healthcare. After following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for article selection, 126 articles were considered for the review. Based on an elaborate Theory, Characteristics, Context, and Methods (TCCM) framework-based review and with the support of available theoretical literature, a future research agenda with a conceptual framework has been presented. The conceptual framework will be useful for future researchers to conduct studies on customer adoption of AI in healthcare.

The article is arranged in the order of methodology, review process, discussion, future research agenda and conceptual framework, and conclusion.

Methods:

Methodologies used in the 95 papers selected for review are categorized into Interview, Experimental design, Case Study, Survey (Descriptive), Survey (Regression), Survey (Structural Equation Modeling) (SEM), Mixed method, Content Analysis/Text analysis, and Conceptual Paper. The distribution of articles in different methodologies categories is presented in Table 4. The highest number of articles comes under the category of survey methods. Survey method papers are further classified into Survey (Descriptive), Survey (Regression), and Survey (SEM). Experimental designs are utilized by the second highest number of articles, followed by the Interview method. Some papers applied two or more methods for their reports; these articles are kept in the row of mixed methods in Table 3. Researchers who employed an interview method for their study analyzed the interview data through thematic analysis. Only some of the articles applied case study methods. The least number of pieces used text or sentiment analysis for their study.

Functionalities

AI is applied in different domains of healthcare. Articles selected for review are classified into AI in healthcare in general, Diagnosis, Robotic/Robot Assisted Surgery, Wearable devices, Chatbot, Carebot, Telemedicine, Electronic health records, and Clinical decision-making.

AI in healthcare in general: This entails studies that talk about AI applications in the healthcare field but do not specify the role of the AI devices. It also entails the research that has studied more than one role of AI in healthcare.

Diagnosis: Diagnosis can be referred to as the first stage of any treatment. It relates to the detection of symptoms and identifying the underlying health issue. Studies that have specifically studied the customer acceptance of AI in performing preliminary diagnosis have been included.

Robotic/Robot-Assisted Surgery: Surgery is a crucial part of healthcare service delivery, wherein precision is prioritized. For this reason, AI robots are to assist surgeons with procedures requiring precision. Here, the AI does not act autonomously but is constantly supervised by a human surgeon. Studies that capture consumer attitudes towards these AI robots have been included therein.

Wearable devices: To increase the transparency and accuracy of real-time monitoring of patient’s bodily changes, AI devices that can be worn on the body have been developed (Korving et al. 2022). Studies have shown that wearable technologies can improve physical health(Bao and Lee 2023; Barricelli et al. 2020).

Chatbots: Chatbots are computer programs that simulate human conversation in writing or speech, simulating human-to-human communication.

Carebot: Carebot refers to an AI companion that remains close to the care receiver and administers necessary and primary care when human care is not available, e.g., helping care to patients in a hospital (Tuisku et al., 2023), supporting the elderly in day-to-day activities (Liu, Tetteroo, Timmermans, et al. 2022), etc.

Telemedicine: Telemedicine as an AI technology enables video or phone appointments between a patient and their health care practitioner. The use of telemedicine has increased manifold post-COVID-19 (Elkefi and Layeb 2022)

Electronic Health Record (EHR): EHR is a digital version of a patient's paper chart. EHRs are real-time, patient-centered records that make information available instantly and securely to authorized users.

Clinical Decision-Making: Clinical decision-making is the art of balancing best-known practices, being aware of the current situation and environment, and knowing the patient. AI helps seamlessly integrate the above and makes clinical decision-making precise and less time-consuming.

The distribution of studies based on functionality or domain of healthcare is stated in Table 4.

Constructs:

Constructs of the papers selected for this review process are divided into Benefits, Costs, Individual Characteristics, Psychological factors, and Psychosocial factors. The categorization of benefits and costs is done by taking reference from social exchange theory and privacy calculus theory, and both theories emphasize that individuals participate in social transactions if the perceived benefits outweigh the perceived costs of any activity. The classification of psychological factors and individual characteristics is done since our study focuses on individuals’ behaviour. Psychosocial factors have been part of various technology acceptances like TPB and UTAUT, and dependent variables like customer adoption, behavioural intention, and customer acceptance are in the outcome category. The constructs are presented and defined in Table 5.

Context:

Articles selected for the current reviews are categorized into the geographical contexts of the studies' respondents. The categorization table is made up of all the continents of the world. Europe and North America host the most significant number of articles selected for the current review process. The distribution of articles continent- and country-wise is presented in Table 6. Conceptual papers and studies conducted in more than three countries are kept outside of the table for the convenience of presentation purposes.

Theory

The studies related to customer adoption of AI used many theoretical underpinnings. Since the review’s topic is interdisciplinary, the theories applied are from several disciplines: technology acceptance, Psychology, Consumer Behaviour, health behaviour, decision-making, and sociology.

Consumer decision-making regarding the acceptance of new technology or the continuation usage of the technology is explained mainly by various established theories like TRA, TPB, TAM, UTAUT, S-O-R theory, etc. (Dwivedi et al., 2019; Huarng et al., 2022; Mishra et al., 2022; Ye et al., 2019). Technology acceptance theories have evolved over a period of time to provide a better explanation of the acceptance phenomenon. The latest development in the theoretical area of customer acceptance of AI-specific solutions is AIDUA (Gursoy et al., 2019). The literature review identified that even after the development of the AI-specific technology acceptance framework AIDUA, researchers are still relying largely on previous theories. We are not suggesting that previous theories have become obsolete because even AIDUA is based on the previous theories with some significant modifications. Since AIDUA was specifically developed for the customer adoption of AI, researchers may apply the AIDUA framework in future research. Even when the studies are focused on customer acceptance of AI in healthcare, theories related to human health behaviour have been used in a very limited number of studies. The health belief model has been used in only one study among the selected 126 articles for this review. Researchers should utilize health behaviour models and other health-related theories for their research in this area. Attribution theory also has scope to be included in future studies on customer acceptance of AI in healthcare. Attribution Theory refers to the causal explanation of a situation through which responsibility for failure or success is ascribed to an agent. Attributing success to themselves and attributing failure to others is a standard attribution error or bias (Buss, 1978). Another influential theory that can be utilized to establish the relationship between independent variables and behavioural intention is Agency Theory.

Methods

Researchers applied various methodologies to study consumer acceptance of AI in healthcare. Qualitative studies like one-to-one interviews, focus group interviews, and case studies were utilized as study designs in various papers. In a few studies, basic descriptive statistics were performed through percentage analysis and frequency analysis. Several studies have applied thematic analysis to determine factors affecting customer adoption of AI in healthcare. Various researchers have applied experimental design to identify the perception of patients and the public regarding AI applications in different healthcare domains. Some studies like (Muaddi et al. (2022), Nurek & Kostopoulou 2023; Ou et al. (2022), and Tran et al. (2019) applied vignette-based experiments.

One significant observation regarding the experimental design is related to the geographical contexts where the experimental study has been performed. Most of the experimental studies have been done in North America and Europe. Some of the experiments have been done in Asian countries like China and Japan. However, in other developing regions like South East Asian countries and the Indian subcontinent, there are few experimental studies in the field concerned.

Some of the researchers have applied survey methods to study the relationships. Regression Analysis and SEM are the most prevalent methods for study design and data analysis. Only some studies have been done using big data analytics tools like content analysis and text analysis. Researchers are recommended to do more text and sentiment analysis-based studies since the vast amount of unstructured user data is generated on social media, company websites, and other internet platforms.

Some studies have used a mixed-method approach to determine customers’ behavioural intentions in AI in healthcare. The mixed method approach provides more credibility to the predictors when explaining a particular phenomenon. Future researchers are requested to apply a mixed-method approach in determining the consumer adoption of AI in healthcare.

Context

Regarding geographical contexts, the most selected studies have been done in countries like the USA, the UK, and Australia. Some studies were conducted in China, Japan, India, and other Asian countries. However, more studies are needed on continents like Africa and South America. Even in Asia, a greater number of studies are conducted in China, Japan, and South Korea. Studies from Indian subcontinents, South East Asia, and Gulf Countries are fewer.

Table 7 shows the population distribution of all the continents in 2020. Around 18% of the global population resides in Europe and North America, but the most significant number of studies have been done there only. AI has a promising potential for improving healthcare systems in low-income developing countries from Asia, Africa, and South America. For the success of AI implementation in healthcare, patients and the public, adoption of AI is essential. Academic journals prefer to publish articles that are global in context so that readers from across the world will be interested in those journals. However, in the context of public adoption of AI, it is crucial to get the views of all kinds of people across the world.

In Asia, around 60% of the world's population resides. However, a significant number of studies are available in studies selected for the review. However, there is a caveat in the data. Most studies are done in China, Japan, and South Korea. Around 40% of the world’s population lives in Asian countries other than China, Japan, and South Korea. India is the most populous country in the world at this point in time. Indonesia, Pakistan, and Bangladesh are 3^rd, 4^th, and 5^th, respectively. Researchers are requested to include African, South American, and Asian populations in their study. Within the Asian countries, researchers are asked to include respondents from India, Pakistan, Indonesia, and Bangladesh in their research

Future research related to constructs is being presented in Fig. 3 (Antecedents, Service Encounters, and Outcomes (A-S-O) framework). The Proposed A-S-O framework is based on the current extensive systematic literature review of 126 articles, previous conceptual frameworks of customer adoption of AI and several theories related to technology adoption, consumer behaviour, health behaviour, consumer decision making, etc.

The decision-making of humans related to the adoption and usage continuation of new technology has been explained through various theories starting from TRA, S-O-R, TPB, TAM, and UTAUT. A few frameworks are available for determining consumers' behavioural intentions in the context of customer acceptance of AI. Gursoy et al. (2019) have conceptualized and empirically tested the AIDUA framework in the context of service robots. AIDUA is already explained in the previous section, so to avoid redundancy, another conceptual framework of AI acceptance in service encounters by Ostrom et al. (2019) will be briefly described below.

Ostrom et al. (2019) have presented an Antecedent- Customer Response- Consequences framework for explaining customer acceptance of AI in service encounters. They have conceptualized several factors, like privacy concerns, role clarity, individual characteristics, etc., as antecedent factors. Approval, adoption, and usage are the customer response factors. Increased well-being and time savings are a few of the consequences. Along with the framework, they have classified the service encounters into three types. AI-supported encounters in which AI works behind the scenes, such as physicians using IBM Watson to help with patient diagnosis. AI Augmented encounters in which AI is visibly assisting the service providers, such as robot-assisted surgery. AI Performed encounters in which AI performed the task independently, such as chatbots.

The proposed ASO framework is developed to determine the customer or patient adoption of AI in healthcare. The theoretical underpinning of the proposed framework includes the S-O-R, UTAUT, TAM, TPB, and AIDUA. The framework is structured on the classical S-O-R theory. The antecedent part is basically the stimulus factors; emotion and trust are the organism factors, and the outcome variables are the response factors. Antecedents are categorized into four factors: Perceived benefits, Perceived costs, Psychological Factors, and Psychosocial Factors. Service Encounters are classified into three types, according to Ostrom et al. (2019), namely AI supported, AI augmented, and AI performed. Outcomes are provided with different terms of adoption, such as acceptance, usage, and continuance of use, so future researchers will have the opportunity to work on them.

Perceived Benefits and Perceived Costs

Constructs categorized as perceived benefits and perceived costs are based on social exchange theory and privacy calculus theory. Social exchange theory states that people examine their social exchanges based on perceived costs and perceived benefits (Cropanzano & Mitchell, 2005). As per privacy calculus theory, users assume privacy concerns in economic terms; people perform a subjective cost-benefit analysis before providing personal information as a cost and expect improved services as benefits (Gotsch & Schögel, 2023; Hoffmann et al. 2016;).

Constructs kept under the category of perceived benefits are Performance expectancy, Health Improvement Expectancy, Quality and Accuracy, and Transparency. The definition and explanation of each construct are already there in the previous section of the paper. But some descriptions, like the reason for selecting some of the constructs, are dropping. The others will be provided below.

Performance Expectancy is taken, and the perceived usefulness is dropped because the perceived usefulness is there among the root constructs of Performance expectancy. Gursoy et al. (2019) have stated through the AIDUA model that the construct performance expectancy is more suitable for determining customer acceptance of AI. Hayat et al. (2022) identified a healthcare behaviour-related construct, so the proposed model added a construct of Health Improvement expectancy to better explain customer acceptance of AI. Future researchers are encouraged to utilize Performance expectancy and Health improvement Expectancy to study customer adoption of AI in healthcare.

Effort Expectancy, Privacy Concern, and Perceived Risk are the constructs categorized under Perceived costs. Effort Expectancy is part of the previous technology acceptance models. Even in the articles selected for the current review, effort expectancy has been used as one of the constructs. Several studies have considered privacy concerns as one of the constructs in their studies of determining customer adoption of AI in healthcare. Gursoy et al. (2019) kept privacy concerns as one of the constructs in their conceptual framework. Hence, privacy concerns are also kept in the current model.

Psychological Factors

Emotional Intelligence, Intrinsic Motivation, Health Consciousness, Hedonic Motivation, and Role Clarity of customers are the constructs there under the category of psychological factors. Health consciousness is the only health behaviour-specific construct available in this category. As stated earlier, these constructs are defined and explained in the earlier section of the current paper. The construct Role and clarity of Customers should have been described earlier. Customer role clarity is the subjective and objective understanding of an individual has role while availing of AI-based service. The clarity of the task performed by AI and the service receiver is essential for a consumer while interacting with intelligent AI solutions. Despite being of critical importance, the role clarity of customers’ needs to be sufficiently explored in the literature, and upcoming researchers are requested to consider including the role clarity of customers in their model of studies.

Psychosocial Factors

Psychosocial factors include Social Influence, Facilitating Conditions, Anthropomorphism, Human Element of Care, and Cultural Differences. Social Influence and Facilitating Conditions are utilized in technology acceptance research in several areas. Anthropomorphism is an AI-specific construct, and the Human Element of Care is a healthcare-specific construct. The aspect of the human element of care is essential in healthcare AI adoption studies as the design and behaviour of the robots do affect the customer perception towards them (Liu, Tetteroo, and Markopoulos 2022). However, very few articles utilized this construct in this area, and future researchers should consider adding the construct of the human element of care in their scope of study. Future researchers may include four dimensions of culture (Power distance, Uncertainty Avoidance, Individualism-Collectivism, Masculinity-Femininity) given by Hofstede (1980) for their study of the determination of behavioural intentions in AI in healthcare.

Types of Service Encounters

In general, AI service encounters with customers are of three types: AI supported, AI augmented, and AI performed (Ostrom et al., 2019). AI is in a supporting role in which AI will be in the background, like IBM Watson used by the clinicians; AI is in the augmenting role if the AI is visibly helping a clinician, like in the case of Robot-assisted-surgery and AI is in a performing role if a robot is performing surgery or chatbots are providing therapy in the case of mental health care. In all three scenarios, the perception of patients or the public regarding service encounters will differ. In the area of healthcare AI, the role played by AI will have a significant impact on customer adoption. Future researchers should incorporate the different roles played while establishing relationships between factors affecting customer acceptance of AI and outcome variables like adoption and usage intentions.

Mediators

Trust and Emotions are kept as possible mediator constructs in the current model. Both constructs are defined and explained earlier in the review. Several studies have used trust as a mediator variable in determining customer technology adoption. Balakrishnan and Dwivedi (2020) determined that technology continuation intention is achieved through user trust as a mediating variable. Kim et al. (2021) have explained the relationship between the preciseness of information and customer response through the mediating role of trust. Arfi et al. (2021) identified trust as one factor affecting customers' adoption of the Internet of Things (IoT) in healthcare. Gursoy et al. (2019) have proposed the AIDUA model using the Lazarus cognition-motivation-emotion framework. Many studies have utilized emotion as a mediator while determining customer acceptance of AI. Hence, the construct of emotion has been kept as a mediator in the context of customer adoption of AI in healthcare.

Probable Moderators

Individual characteristics like personality, technology anxiety, and self-efficacy can be used as moderators since several studies related to technology acceptance have used these constructs as moderators. In the formation of UTAUT, demographic variables were used as moderators in the context of customer acceptance of AI in healthcare. Also, demographic variables can be used as moderators since people from different demographic profiles will have different levels of expectations from healthcare AI solutions.

Outcomes

Outcome constructs are adoption, usage intention, the continuation of usage intention, and rejection. The first three constructs are synonyms since the conceptual model has been developed to provide direction for future research, and the opportunity to explore different terms is offered to future researchers.

Future researchers are recommended to empirically test the proposed ASO framework in the area of AI in healthcare.

The perspective of humans related to AI has started gaining prominence in the academic literature in the last two decades (Glikson & Woolley, 2020). Customer adoption of AI in healthcare is reviewed in this article by utilizing the TCCM framework and conducting a systematic literature review. Theories applied in the studies selected for the reviews were analyzed. AIDUA framework is recommended to be used by future researchers of customer adoption AI in healthcare. Most of the studies were found to be conducted with the responses collected from USA, UK, and China, so future researchers are recommended to get responses from countries other than those mentioned earlier to get a complete overview of the perception of customers worldwide. Regarding methodology, Text analysis, and Sentiment analysis are recommended along with traditional methods like interviews, surveys, and experimental designs for future research. ASO framework for antecedents, service encounters, and outcomes are proposed for future research in customer adoption of AI in healthcare.

Authors’ contributions: All the authors contributed equally to the conception and design of the research. Ajay Kumar, Pankhuri Sinha, and RU Megha did the literature search. All the authors prepared the first draft. Daruri Venkat Srinivas Kumar did the first revision. All the authors prepared the final draft.

Data availability statement: No data has been used for this study. The authors will provide the search strings and records of the database search by email at request.

Funding Declarations

This paper is a part of the project titled “Customer Acceptance of Artificial Intelligence (AI) in Healthcare Services- A Study in the Indian Context,” which is funded by the Institution of Eminence (IoE), University of Hyderabad (UoH), Hyderabad, Telangana, India, 500046. The sanction number of this project is UoH-IOE-RC3-21-023.

Acknowledgment

Declaration of Interest Statement

On behalf of all the authors, I, Ajay Kumar, declare as the corresponding author of the manuscript titled “Customer Acceptance of Artificial Intelligence in Healthcare: A Review of Literature and Proposition of Conceptual Framework for Future Research.” The authors do not have any competing interests which could have influenced the work.

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Customer Acceptance of Artificial Intelligence in Healthcare: A Systematic Literature Review and Proposition of Conceptual Framework for Future Research

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