Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial
BackgroundClinical decision support systems leveraging artificial intelligence (AI) are increasingly integrated into health care practices, including pharmacy medication verification. Communicating uncertainty in an AI prediction is viewed as an important mechanism for boosti...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-01-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e59946 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832575959473061888 |
|---|---|
| author | Chuan-Ching Tsai Jin Yong Kim Qiyuan Chen Brigid Rowell X Jessie Yang Raed Kontar Megan Whitaker Corey Lester |
| author_facet | Chuan-Ching Tsai Jin Yong Kim Qiyuan Chen Brigid Rowell X Jessie Yang Raed Kontar Megan Whitaker Corey Lester |
| author_sort | Chuan-Ching Tsai |
| collection | DOAJ |
| description |
BackgroundClinical decision support systems leveraging artificial intelligence (AI) are increasingly integrated into health care practices, including pharmacy medication verification. Communicating uncertainty in an AI prediction is viewed as an important mechanism for boosting human collaboration and trust. Yet, little is known about the effects on human cognition as a result of interacting with such types of AI advice.
ObjectiveThis study aimed to evaluate the cognitive interaction patterns of pharmacists during medication product verification when using an AI prototype. Moreover, we examine the impact of AI’s assistance, both helpful and unhelpful, and the communication of uncertainty of AI-generated results on pharmacists’ cognitive interaction with the prototype.
MethodsIn a randomized controlled trial, 30 pharmacists from professional networks each performed 200 medication verification tasks while their eye movements were recorded using an online eye tracker. Participants completed 100 verifications without AI assistance and 100 with AI assistance (either with black box help without uncertainty information or uncertainty-aware help, which displays AI uncertainty). Fixation patterns (first and last areas fixated, number of fixations, fixation duration, and dwell times) were analyzed in relation to AI help type and helpfulness.
ResultsPharmacists shifted 19%-26% of their total fixations to AI-generated regions when these were available, suggesting the integration of AI advice in decision-making. AI assistance did not reduce the number of fixations on fill images, which remained the primary focus area. Unhelpful AI advice led to longer dwell times on reference and fill images, indicating increased cognitive processing. Displaying AI uncertainty led to longer cognitive processing times as measured by dwell times in original images.
ConclusionsUnhelpful AI increases cognitive processing time in the original images. Transparency in AI is needed in “black box” systems, but showing more information can add a cognitive burden. Therefore, the communication of uncertainty should be optimized and integrated into clinical workflows using user-centered design to avoid increasing cognitive load or impeding clinicians’ original workflow.
Trial RegistrationClinicalTrials.gov NCT06795477; https://clinicaltrials.gov/study/NCT06795477 |
| format | Article |
| id | doaj-art-6f382f07a9524ce0b2d0c821491e4efe |
| institution | Kabale University |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-6f382f07a9524ce0b2d0c821491e4efe2025-01-31T15:45:52ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-01-0127e5994610.2196/59946Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled TrialChuan-Ching Tsaihttps://orcid.org/0009-0005-0992-1986Jin Yong Kimhttps://orcid.org/0009-0002-1626-7204Qiyuan Chenhttps://orcid.org/0009-0006-4112-014XBrigid Rowellhttps://orcid.org/0000-0002-8469-899XX Jessie Yanghttps://orcid.org/0000-0001-6071-0387Raed Kontarhttps://orcid.org/0000-0002-4546-324XMegan Whitakerhttps://orcid.org/0009-0006-8894-5165Corey Lesterhttps://orcid.org/0000-0001-8774-793X BackgroundClinical decision support systems leveraging artificial intelligence (AI) are increasingly integrated into health care practices, including pharmacy medication verification. Communicating uncertainty in an AI prediction is viewed as an important mechanism for boosting human collaboration and trust. Yet, little is known about the effects on human cognition as a result of interacting with such types of AI advice. ObjectiveThis study aimed to evaluate the cognitive interaction patterns of pharmacists during medication product verification when using an AI prototype. Moreover, we examine the impact of AI’s assistance, both helpful and unhelpful, and the communication of uncertainty of AI-generated results on pharmacists’ cognitive interaction with the prototype. MethodsIn a randomized controlled trial, 30 pharmacists from professional networks each performed 200 medication verification tasks while their eye movements were recorded using an online eye tracker. Participants completed 100 verifications without AI assistance and 100 with AI assistance (either with black box help without uncertainty information or uncertainty-aware help, which displays AI uncertainty). Fixation patterns (first and last areas fixated, number of fixations, fixation duration, and dwell times) were analyzed in relation to AI help type and helpfulness. ResultsPharmacists shifted 19%-26% of their total fixations to AI-generated regions when these were available, suggesting the integration of AI advice in decision-making. AI assistance did not reduce the number of fixations on fill images, which remained the primary focus area. Unhelpful AI advice led to longer dwell times on reference and fill images, indicating increased cognitive processing. Displaying AI uncertainty led to longer cognitive processing times as measured by dwell times in original images. ConclusionsUnhelpful AI increases cognitive processing time in the original images. Transparency in AI is needed in “black box” systems, but showing more information can add a cognitive burden. Therefore, the communication of uncertainty should be optimized and integrated into clinical workflows using user-centered design to avoid increasing cognitive load or impeding clinicians’ original workflow. Trial RegistrationClinicalTrials.gov NCT06795477; https://clinicaltrials.gov/study/NCT06795477https://www.jmir.org/2025/1/e59946 |
| spellingShingle | Chuan-Ching Tsai Jin Yong Kim Qiyuan Chen Brigid Rowell X Jessie Yang Raed Kontar Megan Whitaker Corey Lester Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial Journal of Medical Internet Research |
| title | Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial |
| title_full | Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial |
| title_fullStr | Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial |
| title_full_unstemmed | Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial |
| title_short | Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial |
| title_sort | effect of artificial intelligence helpfulness and uncertainty on cognitive interactions with pharmacists randomized controlled trial |
| url | https://www.jmir.org/2025/1/e59946 |
| work_keys_str_mv | AT chuanchingtsai effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial AT jinyongkim effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial AT qiyuanchen effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial AT brigidrowell effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial AT xjessieyang effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial AT raedkontar effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial AT meganwhitaker effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial AT coreylester effectofartificialintelligencehelpfulnessanduncertaintyoncognitiveinteractionswithpharmacistsrandomizedcontrolledtrial |