Revolutionizing UX/UI Personalization with AI and Deep Q Networks (DQN) in Healthcare Super Apps

Introduction: The Need for UX/UI Personalization:

In today’s digital landscape, user experience (UX) plays a critical role in customer satisfaction, engagement, and business success. Personalization is no longer a luxury but a necessity. Users often struggle to find features or relevant content within an app, leading to frustration and disengagement. This is especially critical for subscription-based services or apps that rely on user retention and upselling.

AI has emerged as a game-changer in UX/UI adaptation, yet many still equate AI solely with chatbots. However, chatbots, while useful in some cases, are often ineffective and can be a turnoff for users. The real power of AI lies in personalized recommendations—creating a UX where users feel valued and catered to.

Despite advances in AI-aided UX/UI design, many developers fall into the trap of frequent software updates for minor feature changes, leading to a buggy and confusing user experience. The challenge is to optimize the UX dynamically, ensuring a seamless and personalized journey without disrupting the user.

The Role of AI and Gen AI in UX/UI

AI and Generative AI (Gen AI) are set to redefine how apps, services, and features are consumed. However, blindly integrating Gen AI (e.g., chatbots) can complicate UX rather than improve it. We need a new thought process in UX/UI design that moves beyond traditional static interfaces to a fully adaptive, AI-driven UX.

Some companies are experimenting with AI-driven UI/UX improvements, but the lack of true personalization remains a challenge. Additionally, AI-based personalization often makes decisions that are difficult to track, backtrack, or explain. Users may not understand why a particular recommendation or UI adaptation occurred, leading to a loss of trust.

AI-Based UX/UI Personalization: The Next Step

Apps like Netflix personalize content recommendations to keep users engaged. For content-based apps, UI optimization typically revolves around screen size and tile-based layouts, which AI can enhance dynamically. Machine Learning (ML) algorithms can learn user preferences and adapt tile sizes, content density, and scrolling behavior in real-time. This involves two key concepts: 1) Content recommendation and 2) Screen optimization.

For example, if a user is about to finish their current content, the UI dynamically shifts to prioritize recommendations for similar content. However, this approach is not universally effective—for e-commerce or feature-rich apps, a different strategy is required.

User habits are dynamic and ever-changing, necessitating an adaptive algorithm that continuously refines UX recommendations or UI. The goal is to enable real-time UI adjustments based on user behavior and intent, ensuring a personalized and frictionless experience.

The Challenge of Multi-Device and Multi-Modality UX

One major flaw in current UX/UI engineering is the lack of multi-modality design. Users today interact with multiple devices—smartphones, tablets, wearables, TVs, and IoT devices—simultaneously. Many UX/UI frameworks fail to take advantage of this.

For instance, in a household with 5-6 connected devices, content consumption spans multiple screens. While some apps attempt multi-device adaptation, AI-driven UX optimization across multiple devices is still underutilized. AI can play a crucial role in optimizing the user experience across devices dynamically, ensuring seamless transitions between screens and personalized interactions.

Building a Fully Adaptive UX/UI for a Healthcare Super App

With this understanding, we set out to build a healthcare super app with fully reactive UX/UI based on user needs.

Problem Statement

Super apps, especially in domains like finance and healthcare, consolidate multiple services into a single platform. However, they often suffer from complex and fragmented user experiences due to the sheer number of features and interdependencies.

For example, in a healthcare super app, key services include:

• Medicine Orders (Prescription management, automatic refills)

• Insurance Management (Health cards, claims, premium payments, policy renewals)

• Financial Services (Medical bill payments, loans for treatments)

• Hospital Management (OPD services, IPD services, reports, consultations, prescriptions)

• Personal Health Tracking (Health records, fitness tracking, medicine adherence)

• Third-Party Integrations (Gym subscriptions, diet planning)

• Lab Services (Lab test bookings, reports management)

• Others…

  
  

Each of these services typically exists as separate pages or apps, requiring excessive navigation, manual data entry, and repetitive tasks, making the experience inefficient and overwhelming for users.

Ideal Solution: Adaptive UI with AI-Driven Contextualization

Ideal Solution: Adaptive UI with AI-Driven Contextualization:

A user starts experiencing symptoms and opens the healthcare super app to seek guidance. The AI-powered UI Agent dynamically adapts based on the user’s current health data, intent, and past interactions, creating a seamless, context-aware experience without unnecessary navigation.

User Journey:

Context-Aware Symptom Assessment

• The user enters symptoms into the app (or the app detects anomalies from connected fitness devices like a smartwatch).

• The UI automatically adjusts, surfacing relevant health insights from past records, fitness data, and medicine history.

• AI provides personalized symptom analysis and potential remedies based on medical knowledge.

Intelligent Hospital Service Recommendations

• Based on symptom severity, the UI Agent predicts the next best action, suggesting:

  • Virtual Consultation with a doctor.

  • Hospital Visit for a physical check-up.

• Instead of forcing the user to manually navigate multiple pages, the UI adjusts dynamically, offering one-click appointment booking with preferred doctors or nearby hospitals.

  
  

Seamless Multi-Service Execution

• If a doctor recommends lab tests, the UI automatically suggests the best nearby lab services, displays costs, and schedules the test.

• If a prescription is generated, the medicine order page automatically adapts, pre-filling the required medicines with price comparisons across providers.

• If the user’s insurance covers the appointment, the UI pre-fills claim forms and initiates the claim process seamlessly.

Adaptive UI for Financial & Insurance Management

• If out-of-pocket expenses are required, the UI suggests:

  • Instant payment via linked accounts.

  • Medical loans or EMI options based on financial profile.

  • Checking insurance coverage & automatic claim processing.

• The user does not need to switch between multiple tabs—the UI dynamically adapts to integrate financial services within the same flow.

What is the Goal of the ML algorithm or adaptive UI:

• Eliminates Complex Navigation – No need to switch between multiple pages to complete a task.

• Context-Aware UI Adjustments – The UI proactively surfaces relevant actions based on real-time data.

• Automated Service Invocation – The app predicts & triggers necessary services, reducing manual effort.

• Seamless Multi-Vendor Integration – The user doesn’t see backend complexities; AI handles vendor coordination.

• Adaptive Recommendations – Over time, the system learns user preferences and optimizes the experience.

We explored multiple approaches, including traditional tile-based designs build with ML algorithms, but found them insufficient. This led us to explore Deep Q Networks (DQN), an advanced reinforcement learning technique that enables AI-driven UI adaptation based on user behavior. We coupled Du-DQN (Dueling DQN) algorithm with hierarchical visual design methodologies to update the UI designs or actions.

Deep Q Networks (DQN):

DQN is widely used in agent-based learning, where an AI agent learns the best actions for a given state based on rewards. This makes it ideal for UI optimization, as it can:

1. Predict the best user actions based on context, intent, and history.

2. Adapt UI elements dynamically to help users achieve their goals.

3. Minimize unnecessary clicks and cognitive load, leading to a smoother experience.

   
   

DQN is build with basic of Q table reinforcement learning algorithms. Q learning takes the current state of the app/screen/user actions and finds all possible actions and evaluates Q value. Goal of the algorithm to maximise the Q such are for action maximise the rewards. The typical algorithm works as,

DQN uses neural networks rather than Q-tables to evaluate the Q-value, which fundamentally differs from Q-Learning. In DQN, the inputs are states while the outputs are the Q-values of all actions.

Deep Q-Network (DQN) Loss Function

where,

The Q target network calculates the target Q for the actions which are back propagated and updates the policy network.

The primary goal of the target network is to prevent the Q-value estimation from oscillating during training, which can occur when the target values are updated too frequently. 

• The target network is a copy of the main Q-network (the "online network"). 

• The target network's parameters are updated less frequently than the online network's parameters, typically after a certain number of training steps. 

• The target network is used to calculate the target Q-values during the Q-learning update step. 

Dueling DQN Q-Value Decomposition

Dueling DQN is an enhanced version of the standard Deep Q-Network (DQN) that improves performance by decomposing the Q-value function into two streams: a value stream (V) estimating the state's value, and an advantage stream (A) estimating the advantage of each action in that state. 

• Decomposes the Q-value function into two streams: a value stream (V) estimating the state value and an advantage stream (A) estimating the advantage of each action in that state. 

• This allows the model to learn the value of a state independently of the actions, and the advantage of each action relative to others. 

• This can lead to more efficient learning and better performance, especially in complex environments. 

We have selected Du-DQN for various reason, mainly few of the following,

• Improved Learning Efficiency: By separating the state value and action advantage, Dueling DQN can learn more effectively, especially in complex environments where the actions may not always affect the environment in meaningful ways. Mainly the the UI user actions now all actions effect the final goal of the users.

• Better Generalization: The ability to learn the state value and action advantage separately can lead to better generalization across different states and actions. 

• More Accurate Value Estimation: By explicitly modeling the state value and advantage, Dueling DQN can estimate the Q-values more accurately. 

How We Implemented AI-Driven UI Adaptation

We designed a UI Agent powered by Dueling DQN to optimize the interface based on:

• User context (preferences, health status, usage patterns)

• Current app state (open features, active modules)

• User intent (goal-driven behavior, user actions like touch, click, press)

• Previous interactions (historical actions and outcomes)

This UI Agent learns the best actions for a given user state and dynamically updates the interface using Effective Visual Hierarchy Design Principles:

• F-Pattern & Z-Pattern Design for intuitive content placement.

• Context-aware UI adjustments to avoid information overload.

• Predictive UX that anticipates user needs and minimizes friction.

Hierarchical Q-Learning (For Multi-Level UX/UI Adaptation)

We build the hierarchical Du-DQN. Here main aim to optimize and further simplify the user goal into sub goals. So first Du DQN learns the user sub goal which is fed again to next Du-DQN for learning the best action(s). If needed we can go further and run it one more iteration for further diving it into DQN. This is depends on the complexity of the state.

Where,

Optimizations & Multi-Modal AI UX

While Dueling DQN-powered UI Agent significantly enhances personalization, further optimizations were needed:

• Hierarchical Deep Q Networks (HDQN): Using multiple DQNs to define sub-goals and actions for even faster, more efficient UI adaptations.

• Multi-modal UX adaptation: AI-driven seamless transitions across multiple devices (e.g., start a healthcare session on a phone, continue on a smartwatch).

• Privacy & On-Device AI: Optimizing edge AI models to process UI personalization locally for privacy-conscious users.

Conclusion

We have seen promising results with hierarchical DDQN reinforcement learning, and learning is still in progress.

Every single user click, touch, and interaction is helping us refine AI-driven UX further. This approach is not just limited to mobile apps—it can be easily extended to:

Multi-modal AI experiences (gesture, voice, haptics). On-device AI for smartphones, wearables, and AI-powered smart environments.Personalized real-time UX for future AI-driven mobile OS.

The future isn’t just AI-powered devices—it’s AI-first experiences that adapt, evolve, and personalize UX dynamically. Who’s ready to make that leap?