The rapid development of digital technologies is leading to enormous changes in healthcare (Hummelsberger et al., 2023). These include, for example, the growing emergence of online medical platforms that use artificial intelligence (AI) to provide medical advice and consultations directly to patients (Haupt, 2019). These platforms have become increasingly prevalent in recent years as they offer several benefits, such as convenience, accessibility, and cost-effectiveness (Bharti et al., 2020). However, despite the growing popularity of these platforms, research on how people accept and interact with them is still limited (Mataczynski et al., 2020). The existing literature suggests that there is positive attitude toward AI in healthcare (e.g., Maassen, 2021; Robertson et al., 2023), but also a preference for human physicians over AI technology (e.g., Lennartz et al., 2021; Yakar et al., 2022). So far, it was not investigated whether the generally positive attitude towards AI technologies in healthcare varies in situations where patients themselves are affected by the technology or when it affects another person. The aim of this study was to close this gap. Based on Construal Level Theory (CLT; Trope & Liberman, 2010), we investigated how diagnoses and treatment recommendations of online medical platforms are perceived compared to human doctors if the situation affects a person directly vs. an average person. The current research is important because although AI-enabled digital health technology has become increasingly prevalent in healthcare, comparatively little is known about how patients view the usage of these tools. Understanding patients’ preferences and concerns is crucial for designing AI-enabled tools that meet their needs and expectations.

The term online medical platform generally refers to all online health services providing consultation and/or treatment of medical conditions through information technology (Jiang et al., 2021). Online medical platforms, sometimes also referred to as telemedicine, offer a variety of services including online medical consultation, counseling, and health management (e.g., El-Sherif et al., 2022; Liang et al., 2021). With the increasing prevalence of AI technology in healthcare, it is essential to anticipate patients’ reactions toward these services as they become a significant aspect of medical care (Richardson et al., 2022). This is especially critical because patients’ appreciation regarding AI may hinder the widespread adoption and utilization of these technologies. Research on non-medical uses of AI has demonstrated that the general public’s perception of AI can vary widely from worries of loss of control of AI or ethical concerns for AI to hopes for AI in healthcare and education (Fast & Horvitz, 2017). Negative attitudes toward AI are reflected by the so-called algorithm aversion described by Dietvorst et al. (2015). Algorithm aversion as a construct means that humans often favor human advice or forecasters over a statistical algorithm (i.e., AI) in making their decision. As such, people usually even prefer human advice when they have experienced that the algorithm outperforms the human. This tendency may be costly, especially for decision-making situations in which algorithms are better forecasters than humans (e.g., Grove et al., 2000; Kaufmann & Wittmann, 2016; Guidotti et al., 2006). Jussupow et al. (2020) differentiate in their review between three occurrences of aversion: First, people can choose between a human and an algorithm that provides advice or performs a task. Second, people can use the assessment of the human or the algorithm to form their own decision. And third, the human or algorithm providing advice can be evaluated differently. All three types of algorithm aversion seem to be relevant in the context of online medical platforms. For example, a systematic review found that many individuals showed reservations toward the use of AI in healthcare and stated instead a preference for human care (Young et al., 2021). A recent study showed how people respond to risk management recommendations from an AI compared to human experts. Participants were asked to make decisions regarding medical risks, among other things, and it was found that participants generally preferred human experts over AI. In the follow-up study, participants were asked to make a decision before receiving recommendations from human experts or AI. The results showed that participants changed their decisions more readily when receiving dissenting recommendations from human experts compared to the AI (Larkin et al., 2022). These findings underscore the significance of patient involvement to ensure that AI technology is integrated into healthcare in a way that promotes public trust and alleviates potential fears (Richardson et al., 2021). So far, in the medical field, many studies have shown that respondents generally have a positive attitude toward AI. For instance, in one German study, more than half of the participants (N = 462 patients) rated the utilization of AI in medicine as positive or very positive, while only a small percentage (4.77%) reported negative or very negative perceptions. Respondents did not express significant concerns regarding AI but strongly agreed that physicians should retain control over AI technology (Fritsch et al., 2022). This ties in with research on different roles of AI. Typically, researchers differentiate between AI as a support system for physicians or health care professionals vs. independently performing diagnoses (Holzinger et al., 2022). Nelson et al. (2020) also found similar results using a qualitative approach (N = 48 patients from the UK) focusing on skin cancer screening. While 75% of patients said they would recommend AI to their friends and family, 94% emphasized the significance of a symbiotic relationship between humans and AI (Nelson et al., 2020), thus advocating AI as a support system instead of an independent actor. Furthermore, a recent review article indicates that patients and the public generally supported the use of AI in healthcare (Young et al., 2021). Contrary to the aforementioned algorithm aversion, these findings might reflect the so-called algorithm appreciation (Logg et al., 2019). Under certain conditions it was shown in a series of experimental studies that participants prefer advice of an algorithm over human advice (Logg et al., 2019; You et al., 2022). A more recent study that built on the results of Logg and colleagues, found that algorithmic aversion and appreciation could be created manipulating the framing of the algorithmic vs. human agent (Hou & Jung, 2021). It is thus interesting to further investigate which factors can explain why people behave in an algorithmic appreciative way in one situation and in an aversive way in another.

In summary, there seems to be a positive attitude toward AI in healthcare, but also a preference for human physicians over AI technology. A question that arises in this context is whether male and female physicians are equally preferred to AI. Numerous studies suggest that female and male physicians are perceived differently (Hall et al., 2011; Mast, 2007). For example, patients talk differently to male vs. female physicians (Hall & Roter, 2002), and rate both the nature of their interactions (Mast & Kadji, 2018) and their competence (Hall et al., 2015) as well as the satisfaction with the physicians (Duberstein et al., 2007) differently. Similar results were also found in a study that asked participants to imagine a virtual medical visit (Mast et al., 2007). Hall et al. (2015) investigated whether patients rated male and female physicians differently based on their patient-centered skills. The results showed that male physicians were judged more positively when they displayed higher patient-centeredness, while female physicians did not receive the same credit for this behavior. The study suggests that female physicians may not be seen as competent when displaying patient-centeredness, as it is expected of them. Therefore, it is interesting not only to examine the difference between human and machine, but also to distinguish between the gender in the human condition.

In the context of online medical platforms and AI technology in healthcare, it was not yet investigated whether the generally positive attitude towards AI technologies in healthcare varies in situations where patients themselves are affected by the technology or when it affects another person. Construal Level Theory (CLT) offers a useful theoretical framework for this. CLT posits that the way individuals mentally construct and perceive events and objects is significantly influenced by their perceived psychological distance (PD) from these entities (Trope & Liberman, 2010). PD describes the extent to which a circumstance is “not part of one’s direct experience” (Trope et al., 2007, p. 2). Objects that are psychologically distant from the subject are represented abstractly and theoretically. Peng et al. (2013) figuratively compare a high construal level (i.e., high PD) to the perspective of a bird viewing a sprawling forest from above. In contrast, they analogize a low construal level to an animal that closely sees single trees at the bottom of the forest. CLT proposes that the subjective PD to an event or object influences whether one thinks about it more concretely or abstractly (Lermer et al., 2015; 2016b). CLT has been applied in various contexts (e.g., climate change: Brügger, 2020, Wang et al., 2019; smartphone usage while driving: Lim et al., 2021; autonomous shuttle busses: Schandl et al., 2023; attitude toward physical exercise: Wang et al., 2022) and has a wide reach in evaluating risks in different domains (Lermer et al., 2016a). For example, studies on risk assessment have shown, that risk perceptions vary depending on who is at risk. Lermer et al. (2013) found that risks that affect oneself are perceived lower than when they affect other people (e.g., an average citizen). One assumption is that the phenomenon of unrealistic optimism (Harris & Hahn, 2011) is decisive for this effect. According to the motto “it won’t happen to me”, most people consider the probability that a negative event will occur lower for oneself than if it affects someone else (Wills, 1981). However, according to the Construal Level Theory (CLT; Trope & Liberman, 2010), risks that affect oneself should be rated higher than risks that affect others, because here the psychological distance (PD) is lower. Consequently, studies on risk perception have shown that more concrete thinking leads to higher risk assessments than abstract thinking (Lermer et al., 2016a). It is unclear whether these research findings can be applied to the context of online medical platforms. Unrealistic optimism would suggest that risk assessment is lower for oneself than for others. Therefore, if the use of AI is expected to involve risk, then this risk should be estimated higher for others than for oneself. However, the opposite assumption would be made according to the CLT. Other relevant variables to capture the attitude toward AI in healthcare represent the evaluation of diagnosis and treatment recommendation. Trust in a diagnosis and the belief that it is correct is crucial for the relationship between the patient and the treating contact and, not least, for the course of treatment (Lu et al., 2018; Nguyen et al., 2009). To the best of the authors’ knowledge, there are no studies that investigated the effect of perspective on evaluation of diagnosis and treatment recommendation.

Thus, the current state of research and the gaps identified here lead to the following two research questions (RQ) explored in this study. What influence do (RQ-1) perspective (target person: self vs. average person) and (RQ-2) source of advice (AI vs. male physician vs. female physician) have on the evaluation of the A) diagnosis, B) treatment recommendation, C) risk perception?

Overall, this study aims to contribute to a better understanding of patients’ perspectives on modern digital health technology by identifying relevant factors that influence their perceptions.

The experimental design and the research questions were preregistered on Open Science Framework (https://osf.io/6kzxf; this is a temporary and anonymous link for the peer-review process. The repository will be made public upon acceptance). We conducted a 2 (perspective) x 3 (source of advice) between-subjects online experiment. Participants were randomly assigned to one of the six conditions. Perspective as the first independent variable had two levels. Accordingly, participants were asked to imagine

Source of advice, as the second independent variable had three levels: AI, male physician, and female physician. As the scenario continued, participants had to imagine the following:

[condition average person]:

At the end of the scenario, all participants received the same diagnosis and treatment recommendation:

In addition to the text of the scenario, source of advice was accompanied by graphic illustrations. For the male/female physician condition, participants were shown a figure with a stethoscope that looked either male or female. In the AI condition, an abstract image of a human head with computer neurons was shown. We decided to use the vaccination scenario as a cover story since it was a very present and common topic at the time of data collection due to vaccinations against COVID-19. In addition, we were looking for a description of symptoms that sounded severe enough (i.e., “severe discomfort”) to make people believe it would make sense to seek medical advice without at the same time stressing the participants in an unethical way or giving them the impression that they needed to see an emergency doctor immediately. All study materials can be found in the online repository (https://osf.io/vwxcj/). Following the scenario, participants were asked to rate the dependent variables (i.e., evaluation of the diagnosis, treatment recommendation, and risk perception), and complete a survey assessing psychometric scales and demographics.

To examine the three dependent variables (i.e., evaluation of the diagnosis, treatment recommendation, and risk perception), we developed eight items. Four items were used to measure the evaluation of the diagnosis. This variable captured both participants’ trust in the diagnosis (e.g., “I trust the diagnosis”) as well as their trust in the medical expertise of the source of advice (e.g., “I trust the medical expertise of [condition]”; condition refers to female/male doctor or Symptom-Checkbot) on a 7-point Likert scale from “strongly disagree” to “strongly agree”. These items were averaged to an index of evaluation of diagnosis (α = 0.89). Two items examined the evaluation of treatment recommendation. This variable measured the extent to which individuals felt comforted by the recommendation and whether they would adhere to it (e.g., “I feel comforted by the treatment recommendation”). Both items were measured on a 7-point Likert scale from “strongly disagree” to “strongly agree” and were averaged and resulted in an index of evaluation of treatment recommendation (α = 0.80). Finally, two items were used to measure risk perception. This variable measured the perceived risk that the diagnosis and the treatment recommendation could be wrong (e.g., “How high do you estimate the risk that the diagnosis could be wrong?”) on a scale from 0% to 100%. The average of these two items showed excellent internal consistency α of 0.92.

In addition, the following control variables were considered: attitude towards artificial intelligence, health anxiety, technology commitment, and socio-demographic variables (gender, age, social status, and health insurance status).

Attitudes toward AI was measured with the General Attitudes towards Artificial Intelligence Scale (GAAIS; Schepman & Rodway, 2020). Participants had to answer 20 items (e.g., “Artificially intelligent systems can help people feel happier”) on a 5-point Likert scale from “strongly disagree” to “strongly agree”. Items were averaged to two subscales (positive attitude: α = 0.67 and negative attitude: α = 0.43). Also, an attention check item was part of the scale (“I would be grateful if you could select agree”). Participants who failed to answer the attention check correctly were excluded from the analysis.

Health anxiety was examined using the health anxiety inventory (MK-HAI) developed by Bailer and Witthöft (2006). This scale assesses a person’s tendency for health-related concerns with 14 items (e.g., “I spend a lot of time worrying about my health”) on a five-point Likert scale from “strongly disagree” to “strongly agree”. All items were averaged to an index of health anxiety (α = 0.92).

Technology commitment was measured with the Technology Commitment Short Scale (Neyer et al., 2016). This construct captures the personal attitude toward and handling of modern technology. Participants had to answer 12 items (e.g., “I am very curious regarding new technical developments”) on a 5-point Likert scale from “not true at all” to “completely true”. Items were averaged to calculate technology commitment (α = 0.82).

The of MacArthur Scale of Subjective Social Status (Adler et al., 2000; Hudecek et al., 2022) was used to assess subjective social status. Participants placed themselves on a given drawing of a ladder with ten rungs according to the following description: “Think of this ladder as representing where people stand in our society. At the top of the ladder are the people who are the best off, those who have the most money, most education, and best jobs. At the bottom are the people who are the worst off, those who have the least money, least education, and worst jobs or no job”.

We measured a set of socio-demographic variables. Participants were asked to report their age, gender, formal education in terms of German educational achievement levels (ranging from no educational attainment at all to a university degree), and their average working hours per month. We assessed participants’ current health condition and their attitude toward vaccinations as well as their vaccination status against COVID-19. We also asked participants to provide their height and weight to calculate their Body mass index (BMI). Moreover, we assessed participants’ health insurance status (public vs. private health insurance).

A manipulation check was included at the end of the survey. For this, participants had to indicate from whom they had previously received the diagnosis and treatment recommendation on the online medical platform as part of the scenario. Individuals who failed to answer the manipulation check correctly were excluded from the analysis.

The data were collected using pools from three universities across Germany. Participants were informed about the purpose of the study and gave consent prior to participation. The consent also included that the data would be used for publication and stored as open data in the Open Science Framework. Participants received course credit for participation. The online questionnaire was set to “require” answers from participants apart from the demographic variables also including the COVID-19 vaccination status. Thus, there are no missing values on the psychological measures in the dataset.

In total, 350 participants completed the survey. Before running the analyses, we excluded certain participants from the dataset according to predefined criteria that were part of the preregistration. The questionnaire was programmed that only those participants who answered the attention check item correctly could complete the questionnaire. However, all subjects who failed to answer the manipulation check correctly were excluded (N = 69). Moreover, as stated in our preregistration, participants who had negative attitudes toward vaccination and were not vaccinated against COVID-19 were excluded from the analyses (N = 15). The reason for doing so is that the experimental scenarios required the participants to imagine that they had recently been vaccinated. The final sample comprised N = 266 persons. Participants were 74% female students with an average age of M = 24.83 years (SD_age = 5.81, Range_age = 18–51). Regarding the educational level, 9% had completed vocational training, 86% had a university of applied sciences entrance qualification (i.e., German Fachhochschulreife, N = 40) or a university entrance qualification, i.e., German Abitur N = 190), and 5% of the respondents already had an academic degree. The majority of the participants worked at least part-time with an average of over 25 h per week (SD = 14.66), which clearly distinguishes these participants (63% of the total sample) from classic student samples.

Data were analyzed using R Studio (version 2022.07.1 on macOS, R version 4.2.1). We used two-way ANOVAs to analyze the effect of perspective and source of advice on the dependent variables. We checked the statistical assumptions using the Shapiro-Wilk test (normality assumption) and the Levene test (homoscedasticity). As the assumption of normal distribution was violated for all dependent variables and across all conditions and homoscedasticity could only be fully established for treatment recommendation, we calculated robust ANOVAs using the WRS2 package by Mair and Wilcox (2020). The calculation of robust ANOVAs is based on 20% trimmed means. As Mair and Wilcox (2020) describe, the “appeal of a 20% trimmed mean is that it achieves nearly the same amount of power as the mean when sampling from a normal distribution” (p. 1). It is important to note that the robust two-way ANOVA does not report any degrees of freedom since an adjusted critical value is used to determine significance, instead of the F-value, a Q statistic is reported (Mair & Wilcox, 2020). After running the ANOVA, we calculated robust two-sided pairwise comparisons to analyze the simple main and interaction effects.

To analyze the effect of the control variables, we computed multiple linear regressions for each dependent variable with the independent and the control variables as predictors. As Cook’s barplots indicated several outliers for the dependent variables, we calculated robust regression analyses using the robustbase package (Maechler, 2022).  

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Fig. 1a. Interaction plot for evaluation of diagnosis (EOD).

Note. Figure shows trimmed means (20%).

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Fig. 1b. Interaction plot for evaluation of treatment recommendation (EOT).

Note. Figure shows trimmed means (20%).

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Fig. 1c. Interaction plot for risk perception.

Note. Figure shows trimmed means (20%).

Some limitations of the study need to be mentioned. First, we used a scenario-based design which might affect the validity of the results. Although it is known from different contexts of psychological research that the results of scenario-based experiments are comparable to real in-field experiments and show similar validity (Chen et al., 2019; Weyrich et al., 2020; Zhang et al., 2023), future studies should replicate the design in a more realistic setting. Second, the psychometric quality of one moderator variable capturing participants’ general attitude towards AI was not adequate, as the value of Cronbach’s alpha for negative GAAIS was significantly below the acceptable threshold of .70 (Schermelleh-Engel & Werner, 2012). We have no plausible explanation for this low reliability since this instrument is a validated measure that has already shown good reliability values in other studies (e.g., Carolus et al., 2023). In the present case, the validity of the results might therefore be limited with regard to this variable. Future studies should therefore replicate the results on the basis of the current instruments and additionally use alternative measures to capture participants’ general attitude toward AI (e.g., the ATAI scale of Sindermann et al., 2021) to check for potential differences. Third, as in many psychological studies, the sample is biased towards female participants and has a low mean age of 24.83 years. However, controlling for gender and age had no effects on the dependent variables. In addition, it must be noted that the sample was skewed towards a higher education level, which might have impacted the participants’ perception of artificial intelligence. Future studies should replicate the current findings and aim for a sample with a more heterogeneous educational background. In general, a higher education level is associated with higher acceptance rates of new technologies (Czaja et al., 2006; Rice et al., 2019). However, a recent study with a sample from Israel found that education level was not a significant predictor for patients’ acceptance of AI-based technology in primary care (Chalutz Ben-Gal, 2023). Another aspect that future studies should address refers to the design of the scenario. The symptoms in the current study were framed as “severe discomfort”. It seems likely that the results can be generalized to cases or diseases where a severe discomfort is typical of the course of the illness (e.g., having a cold, flu). However, for other diseases or cases (e.g., emergency situations), the study should be replicated to test the generalizability of the results. In addition, the current design could also be used to test the effect for existing AI-assisted applications and symptom checkers (e.g., screen cancer screening).

The results of our study suggest some practical implications. First, considering that diagnoses, treatment recommendations, and risk perception of AI compared to human physicians are perceived more negatively in general, the main point of contact for patients should remain a real human. Therefore, online medical platforms providing the possibility for patients to check symptoms should not only be developed in a way that they are used by the patients directly. Instead, such symptom checkers could be developed for the use of physicians or healthcare organizations. Thus, humans will not be replaced by AI, but AI will be able to support physicians or healthcare organizations while patients are still in contact with real humans. When AI applications in healthcare become more widespread and accepted in the future, symptom checkers can also be increasingly developed for direct use by patients. Second, our results stress that when it comes to judging the situation for other people, AI is no longer perceived more negatively than human physicians. This effect could provide some opportunities for the design of online medical platforms. When patients use these services and interact directly with them, the symptom checker could refer to others. For example, when recommending actions or providing explanations, it could not only address the user directly, but also emphasize that “other people” with the same symptoms have benefited from certain measures or behaviors. In this way, the psychological distance could be increased, which in turn – according to the results of the current study – should have a positive effect on the perception of the diagnoses and recommendations. However, it must be noted that these considerations still need to be tested in future studies. Third, we found that patients with higher health anxiety tend to perceive greater risks of incorrect diagnoses and treatment recommendations. Therefore, we suggest in line with precision medicine and personalized health care (e.g., Johnson et al., 2021), developing AI-based symptom checkers that can recognize signs of health anxiety in patient interactions (e.g., through natural language processing of patient queries) to respond appropriately. For example, once the symptom checker has identified patients with increased health anxiety, it could provide additional information to alleviate the health anxiety or involve a human doctor or healthcare professional in the conversation.

In our preregistered scenario-based 2×3 experiment, we examined the influence of perspective (self vs. average person) and source of advice (AI vs. male physician vs. female physician) on the perception of a medical diagnosis and corresponding treatment recommendations and risk perception. In addition, we assessed the importance of several control variables such as health anxiety, general attitude towards artificial intelligence, and socio-demographic variables (e.g., social status). Results show that people prefer the advice of human doctors compared to an AI when it comes to their own situation. In contrast, there are no differences in terms of the source of advice for judging the situation of an average person. Our study contributes to a better understanding of the patient’s perspective of modern digital health tools. As results indicate that the perception of AI-enabled tools is more critical when it comes to yourself, future research should examine the relevant factors that influence this perception.