Co-Designing a DSM-5-Based AI-Powered Smart Assistant for Monitoring Dementia and Ongoing Neurocognitive Decline: Development Study
Abstract
1. Introduction
2. Methods/Theoretical Framework
3. Results of the Co-Design Process
4. Discussion and Implications
- Key Takeaways from the Co-Design Process:
- Ethical Considerations and Responsible AI:
- Usability and Accessibility:
- Limitations and Opportunities:
- Theoretical and Practical Contributions:
- Future Research Directions:
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Participant | Discipline | Area | Occupation | Experience |
---|---|---|---|---|
A1 | Computational Technology | Artificial Intelligence | Academic Researcher | 10–15 Years |
C1 | Health Sciences | Clinical Psychology | Academic Researcher | 10–15 Years |
C2 | Health Sciences | Mental Health | Principal Psychiatrist | 10–15 Years |
C3 | Health Sciences | Public Health | General Physician | 10–15 Years |
D1 | Design and Development | Mobile Applications | Software Developer | 5–10 Years |
D2 | Design and Development | Mobile Applications | User Interface Design | 5–10 Years |
D3 | Design and Development | Mobile Applications | Mobile App Designer | 5–10 Years |
S1 | Computational Technology | Artificial Intelligence | Postgraduate Student | - |
S2 | Computational Technology | Artificial Intelligence | Postgraduate Student | - |
S3 | Computational Technology | Artificial Intelligence | Postgraduate Student | - |
Workshop | Feedback Category | Feedback Description | Actions Taken/Response |
---|---|---|---|
Workshop 1 | Appearance | Clinicians (C1 and C2) noted the prototype design presented by the Student team (S1, S2 and S3) to be not elderly-friendly and potentially confusing for some colour-conscious users. Academic Researcher (A1) agreed. | From this feedback, the Design team (D1, D2 and D3) was assigned to implement a large front-based and simpler coloured interface design update. S1 was assigned to record the changes, and S2 and S3 were assigned to support D1–3 in the change. |
Execution Flow | After answering each question from the DSM-5 questionnaire, the user needed to click the ‘Back’ button every time and then select the next questions. A1 noted it might be irritating, and C1 and C2 agreed. | D3 suggested that a ‘Next’ button can be enabled after the completion of each question. D1 and D2 agreed, and C1–3 and A1 appreciated the suggestions. | |
C2 commented on the signup option and recommended alternatives, as patients may need help with this process. | D2 was assigned to work on this to bring flexibility such as signup with other options, i.e., a Google account, etc., and train S1–3 alongside. | ||
C1 suggested flexibility while answering the questions, as responding to all 24 questions at a time might not be easy for all. C2 commented that it might be sometimes helpful to attempt all the questions in a single session in order to keep track of the state/condition of cognitive function over time. | A1 moderated the suggestions to keep it open and adjusted the design to save the response with time series and provide that data to the clinician side, as well as a summary of the responses. A1 also suggested adding the notification option to remind the user to re-attempt the remaining questions. D1–3 approved the possibility. C1 and C2 agreed to keep the notifications at least 6 h apart. | ||
Comments | The overall feedback was positive, with some comments for further improvements. A1 concluded the initial prototype design was ‘moderately matching the clinician’s expectations’. | S1–3 were appreciated for their efforts, and D1–3 were thanked for their support and training. All the Sprint Retrospectives and noted for the next Sprints. | |
Workshop 2 | Appearance | C1–3 and A1 appreciated the change in the interface layout to focus more on the data and execution, rather than fancy design and appearance. | D1–3 and S1–3 appreciated the acceptance. The effectiveness of DDM and the Scrum Sprint was discussed and positive comments were endorsed by A1. |
C3 appreciated removing the elderly image from the profile menu as it might be offensive to some. | D2 appreciated C3’s time providing to-the-point feedback. A1 assigned S1–3 to keep track of the formal/informal feedback. | ||
Execution Flow | A1 suggested having a line graph as a visual output to track the NCD status of the patients, rather than all textual information. | C2 highly endorsed that idea. D3 was assigned to implement that in the next Sprint. | |
There were no total scores calculated on the application, and they suggested implementing the feature to calculate the total score of NCD. | Features calculating total NCD score were implemented based on feedback received from participants. | ||
S2 shared an option of keeping the data on the cloud to make SmartApp more accessible to the general public. | D3 suggested using online tools to store and manage the database, i.e., FireBase engine, etc. Roadmap, to conduct the switchover as discussed and assigned. | ||
The idea of utilising machine learning for predictive analysis was discussed. A1 presented the potential benefits. C1–3 agreed with the possible use in clinical settings. | D2 shared concerns about the possible data limitation, to which A1 suggested a possible way around working with synthetic data for smoother testing and validation. | ||
Comments | General feedback received from clinicians C1–3 was highly positive as they were impressed with the frequency of change updates and the responsiveness of the Design team. A1 suggested ensuring confidentiality and data security measures. | Positive feedback was noted by S1–3. S2 suggested the login information be added in the next Sprint Output for the clinicians to be able to access the data from their end. C2 appreciated the next step. | |
Workshop 3 | Appearance | C2 appreciated the ability to see the comparison of two different patients via visual representation in a line graph. | D2 informed that it was the most time-consuming task in the last Scrum Sprint to get this working. C1–3 and A1 appreciated the efforts. |
C1 commented on the ability to search the patients by name rather than using a drop-down list, as the number of patients will substantially grow in the long run. | S2 advised to work with S1–3 to add this feature, which would support the training for continued change implementation in the future. | ||
A1 suggested including the toll tip text to show the attempts of the users, which will be helpful for the users who have multiple attempts in the system. | D3 suggested it would be an easy change, and S1–3 were assigned to carry it out. | ||
Execution Flow | C1–3 and A1 were presented with the updated layout incorporating all the previous Sprint Retrospectives. | D3 confirmed that the SQL database was moved to FireBase to bring portability. A1 suggested sharing the testing results as well, and S1–3 and D1–3 confirmed the SmartApp’s execution flow accuracy. | |
A1 suggested showing machine learning (ML) prediction results for NCD on the results page. A1 suggested keeping it to only one ML model for this version of the app, with a plan to incorporate alternative and more recent ML models for future updates of SmartApp. | C2 advised keeping the prediction visible only to the clinicians, as it may create panic for the user if they do not have an understanding of the functionality. D2 appreciated the idea of rolling over a plan to incorporate future updates regarding the ML model, given the time constraints for this project. | ||
Comments | Overall feedback for the beta phase was positive, with some suggestions for future enhancement, for instance, implementation of IoT sensors and GPS tracking. A1 and C1–3 were generally happy with the current progress. | S1–3 were assigned to share the final set of documentation with the team overall, including the user manual and transition documentation for future maintenance. |
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Ud Din, F.; Giri, N.; Shetty, N.; Hilton, T.; Shafiabady, N.; Tully, P.J. Co-Designing a DSM-5-Based AI-Powered Smart Assistant for Monitoring Dementia and Ongoing Neurocognitive Decline: Development Study. BioMedInformatics 2025, 5, 49. https://doi.org/10.3390/biomedinformatics5030049
Ud Din F, Giri N, Shetty N, Hilton T, Shafiabady N, Tully PJ. Co-Designing a DSM-5-Based AI-Powered Smart Assistant for Monitoring Dementia and Ongoing Neurocognitive Decline: Development Study. BioMedInformatics. 2025; 5(3):49. https://doi.org/10.3390/biomedinformatics5030049
Chicago/Turabian StyleUd Din, Fareed, Nabaraj Giri, Namrata Shetty, Tom Hilton, Niusha Shafiabady, and Phillip J. Tully. 2025. "Co-Designing a DSM-5-Based AI-Powered Smart Assistant for Monitoring Dementia and Ongoing Neurocognitive Decline: Development Study" BioMedInformatics 5, no. 3: 49. https://doi.org/10.3390/biomedinformatics5030049
APA StyleUd Din, F., Giri, N., Shetty, N., Hilton, T., Shafiabady, N., & Tully, P. J. (2025). Co-Designing a DSM-5-Based AI-Powered Smart Assistant for Monitoring Dementia and Ongoing Neurocognitive Decline: Development Study. BioMedInformatics, 5(3), 49. https://doi.org/10.3390/biomedinformatics5030049