How has AI impacted healthcare UX and UI Design in 2024, and what’s on the horizon for 2025?

Streamlining research and ideation for patient-centric design

When designing digital solutions within pharma and healthcare, user research plays a critical role in ensuring digital products meet the needs of diverse user groups, including patients, caregivers, and HCPs. In 2024, AI tools have significantly enhanced the research process by:

• Processing large data sets: AI tools like ChatGPT and Miro AI have helped designers analyse patient feedback, clinical trial data, and market trends, offering actionable insights in record time.

• Generating user personas: AI-powered platforms like the AI Toolbox for Innovators and DALL-E 2 have enabled the creation of detailed personas and visual representations, tailored to specific user groups such as elderly patients managing chronic diseases or clinicians navigating complex dashboards.

• Supporting accessibility: By synthesising insights from diverse datasets, AI tools have facilitated the ideation of inclusive designs for users with different levels of tech literacy, disabilities, or medical conditions.

Example:
When creating a telehealth platform for diabetic patients, the design team could use AI to analyse anonymised health data, generate personas reflecting real-world user needs, and identify gaps in the competitive landscape.

Limitations:
AI tools often rely on generalised datasets, which may overlook nuances in patient behavior, cultural contexts, or rare medical conditions. This makes real-world user validation a non-negotiable step in the design process.

Enhancing UX writing for compliance and trust

In pharma and healthcare, language matters. Whether addressing patients or HCPs, the interface must convey clarity, accuracy, and empathy. AI-powered writing assistants like ChatGPT and Frontitude UX Writing Assistant have been instrumental in generating microcopy that aligns with these requirements.

Applications:

• Patient education: AI tools help create step-by-step instructions for using medical devices or navigating insurance platforms.

• Regulatory compliance: By incorporating legal and medical terminology, AI-generated microcopy ensures adherence to strict regulatory guidelines like HIPAA or GDPR.

• Error messaging: AI tools have supported the development of user-friendly error messages, minimising confusion in high-stakes situations (e.g., a missed medication dose).

Limitations:
AI struggles to emulate human empathy fully. While it can simplify medical jargon, designers still need to refine AI-generated content to ensure it resonates emotionally with users.

AI-powered testing for usability and accessibility

AI has introduced new efficiencies in testing, allowing design teams to evaluate usability and accessibility before involving real users. Tools like Attention Insight and Predict by Neurons have provided predictive heatmaps and automated feedback, helping teams anticipate how users will interact with digital products.

Applications in pharma and healthcare:

• Predicting user behavior: Predictive heatmaps highlight where users are most likely to focus, helping designers optimise layouts for critical features like emergency contact buttons or dosage instructions.

• Accessibility testing: AI tools have flagged issues like low contrast ratios or complex navigation paths, ensuring compliance with accessibility standards.

Example:
A hospital implementing a clinician-facing EHR (Electronic Health Record) system used AI tools to simulate workflows, ensuring key functions like chart updates and lab order placements were intuitive and fast.

Limitations:
While AI can predict usability issues, it cannot account for the complexities of human behavior in high-pressure healthcare environments. Real-world usability testing with end-users remains essential.

Addressing ethical challenges in AI-driven design

The integration of AI into healthcare and pharma design has introduced new ethical challenges in 2024. Bias in AI outputs remains a concern, especially when models are trained on datasets that fail to represent diverse patient populations adequately.

Additionally, ensuring that AI tools comply with stringent industry-specific regulations adds complexity to their adoption. To mitigate these risks, organisations must prioritise human oversight, transparent data practices, and ethical design guidelines, ensuring that AI complements rather than compromises patient-centric goals.

As AI continues to evolve, its impact on healthcare and pharma UX/UI design is expected to deepen in several key areas:

Smarter, context-aware AI tools

Next-generation AI systems will likely have a better grasp of regulatory and ethical complexities. For instance, they could suggest design solutions that automatically comply with GDPR or FDA guidelines, or provide culturally sensitive recommendations for global healthcare products.

Hyper-personalised digital experiences

AI will enable adaptive interfaces that cater to individual user needs. This could include patient portals that customise dashboards based on conditions or apps that predict medication refills and offer wellness tips by analysing historical data.

Seamless cross-team collaboration

AI will further enhance collaboration between designers, developers, and healthcare professionals. Imagine real-time translation of user data into actionable design insights or AI-driven co-creation tools that empower multidisciplinary teams to prototype solutions together.

Advanced usability testing

Testing capabilities will become even more sophisticated, with AI simulating user behaviour across diverse demographics, including those with rare medical conditions. Tools could also perform sentiment analysis during video-based usability tests to assess emotional responses to designs.

Ethical AI design standards

As AI plays a larger role, the demand for ethical guidelines will increase. Future standards may address challenges such as ensuring AI-generated personas represent diverse populations accurately and mitigating algorithmic bias in patient-facing applications.

These advancements will not only enhance the design process but also improve patient outcomes, making healthcare products more inclusive, efficient, and user-friendly.