Views on mobile health apps for skin cancer screening in the general population: an in‐depth qualitative exploration of perceived barriers and facilitators*

Lack of human presence was in some cases seen as a benefit due to anonymity in sharing intimate or uncomfortable health concerns.

“By contrast, the lack of human presence was, in some cases, seen as a benefit due to the anonymity in sharing intimate or uncomfortable health concerns [39,42].”

Revealing the process of how the algorithm was developed, who was involved in the development process, whether clinicians were consulted, and how the data were processed would enable acceptability.

“Furthermore, revealing the process of how the algorithm was developed, who was involved in the development process, whether clinicians were consulted, and how the data were processed would enable acceptability [42,43].”

Ease of use; complex interfaces; and inconsistent performance, for example, due to false positives and negatives, add burden to the workflow, creating more work for clinicians, thereby impeding adoption.

“Ease of use; complex interfaces; and inconsistent performance, for example, due to false positives and negatives add burden to the workflow, creating more work for clinicians, thereby impeding adoption [42,52,57-59].”

In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole.

“In some studies, patients reported the need to assess the usefulness of the AI before using it and had concerns around the quality and accuracy of the output, thereby questioning the value of AI as a whole [39,42,52-55].”

Lack of accuracy, doubts about unsafe results, privacy breaches, patients' perceptions and acceptance of automation, and uncertainty about developers' reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust.

“Lack of accuracy; doubts about unsafe results; privacy breaches; patients’ perceptions and acceptance of automation; and uncertainty about developers’ reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust [39,42,47,50,53,54,69,70].”

When an AI algorithm is too sensitive and provides false positive results, it may add work and costs to the treatment process.

“However, in cases where the algorithm is too sensitive, thereby providing false positive results, it may add work and costs to the treatment process [39,42,57,61,68].”

Lack of accuracy of AI output is considered by clinicians and patients to pose a potential risk of harm.

“The lack of accuracy of AI output was also considered to pose a potential risk of harm by both clinicians and patients, as, in some cases where the algorithm may output false negative results, it may provide an incorrect sense of reassurance and cause a delay in treatment.”

Patients see value in AI technology that can be used from home for minor consultations, such as skin cancer detection using an AI-enabled app.

“From a patient’s perspective, there is value if an AI technology can be used from home for minor consultations, such as skin cancer detection using an AI-enabled app [42].”

For AI apps, data not being integrated into the health care system or workflow was seen as a barrier for patients to adopt the solution.

“For other types of AI solutions, such as apps, the data not being integrated into the health care system or workflow was seen as a barrier for patients to adopt the solution [42,53].”

Facilitators of trust include endorsement of the technology by experts and academically backed clinicians (including regulating bodies such as the government), evidence of output accuracy based on evaluation of AI, and positive opinions from trusted thought leaders in clinical fields.

“Facilitators of trust included the endorsement of the technology by experts as well as academically backed clinicians, including regulating bodies such as the government; evidence of output accuracy based on the evaluation of AI; and positive opinions from trusted thought leaders in the respective clinical fields [42,58,60].”

Data security was identified as a major contributor toward hesitancy, with concerns around cybersecurity relating to both training and testing data as well as fear of trackers and spyware obtaining unsolicited data.

“Data security was identified as a major contributor toward hesitancy, with concerns around cybersecurity relating to both training and testing data as well as fear of trackers and spyware obtaining unsolicited data [39,41,42,45,48,77].”