AI in Mobile Development

Published on May 7, 2018

Updated on Oct 16, 2019

How hard is it to develop an AI app? – In the realm of AI, it is a constant journey and not a destination. Indeed, AI developers and experts are on a mission of solving the most complex problem – human behaviour. They are on a path to study patterns and produce results that a human being would most likely exhibit.

In the making of all of this fabulous innovation, what kind of challenges does an AI developer face? What are the hindrances in their role? Does AI Development manager approach in a responsible manner? To answer many such question lets dive deep into some of the stories of AI development.

‘AI – Opportunities’ in mobile app development

AI is kind of magic wand to its innovators, true to its nature of being complex it hosts a bunch of opportunities’ for developers to explore the world….

Voice Enablement Helps in understanding customer better and delivering the best

How often have you called up customer care to complain when the internet is not working or DTH not working? The first thing they ask you is – what kind of problem are you facing? While at times the problem is simple, many times the executives try to know the exact steps to reach a particular problem. While manually saying click this, click that could help, voice recognition or voice enablement allows developers in identifying the exact process that was followed.

As the user says OK Google on his phone, followed by instruction check new emails or the weather or the best deal for iPhone, it helps developers in understanding the behaviour of the customers. The kind of apps they use most, what are the instructions provided, what kind of instructions not working. The voice input also helps in understanding customers expectations from an app. I remember when my nephew instructed Google Home “You are useless,” the answer came in was I am sorry to disappoint you, and I would let my engineers know about it.”

Simplifying Complex needs

The most exciting opportunity for an AI app developer is about streamlining complex processes and workflows. Well, indeed otherwise how would the language translation work out? Or how could a chatbot help in resolving human beings technical problems? Or could you fathom of any human being going through thousands of lines of log to look for something suspicious? Or how about commanding Voice assistant to locate the best restaurant near you serving Mediterranean food?

All these are the needs to structure and present data in the simplified form. Thanks to AI app developer.

‘AI – Challenges’ in Mobile App Development

Well, the aim is to simplify lives but what are the challenges faced by developers?

No Standards tools and languages

While Google has launched some of the projects like Teachable Machine and Google AI tools to let users experience how AI works, it is still a challenge for developers to start off. In fact, Quora is flooded with queries like what are the languages or software used to develop an AI app. Many firms use Python due to the benefits it offers but has its limitations like weak in mobile programming and enterprises desktop shops. Similar is the case for other software languages like – Prolog, JAVA, C++ and LISP programming languages for artificial intelligence research

Lots of data create confusion

However, it’s the data that helps in creating the best AI app; the irony is that its also in a massive amount at times challenging to segregate and structure. With big data buzz and data tracking now a trend, developers at times face a hurdle in putting the data sets in a meaningful way.

The new availability and advancement of AI and ML are causing a revolutionary shift in the way that developers, businesses, and users think about intelligent interactions within mobile applications.

As healthcare becomes more patient-centric, the demand for efficient and personalized care continues to grow. One of the key technologies that have gained traction in this domain is Conversational UI (CUI) — a user interface where interactions occur through natural language, often with the help of chatbots. For developers, building a robust CUI in healthcare requires a balance of technical proficiency, understanding of the healthcare landscape, and empathy toward patient needs. Let’s explore how CUI can improve patient interactions through chatbots and what developers should consider during implementation.

Why Conversational UI is Gaining Popularity in Healthcare

From scheduling appointments to answering medical queries, healthcare chatbots have become vital tools for enhancing patient engagement and streamlining healthcare workflows. Conversational UIs enable these chatbots to interact with patients naturally, making them accessible even to non-tech-savvy users. By incorporating AI and NLP (Natural Language Processing), chatbots can now simulate human-like conversations, ensuring patients receive timely, relevant responses.

Image credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/

Key Areas Where Chatbots Are Revolutionizing Healthcare

Appointment Scheduling and Reminders – Chatbots can automatically schedule appointments based on patient availability and send reminders before the visit, reducing no-show rates. For developers, this feature requires integration with hospital management systems (HMS) and calendar APIs. The challenge lies in ensuring secure and real-time data transfer while adhering to healthcare compliance standards like HIPAA.

Medical Query Resolution– Chatbots equipped with NLP can answer common patient questions related to symptoms, medications, and treatment plans. This reduces the burden on healthcare providers, allowing them to focus on more critical tasks. Developers working on this feature need to consider integrating medical databases, such as SNOMED CT or ICD-10, for accurate and up-to-date information.

Patient Monitoring and Follow-ups – Post-discharge, chatbots can monitor a patient’s condition by regularly asking for health updates (e.g., vital signs or medication adherence). Developers can integrate IoT devices, such as wearable health monitors, with chatbot platforms to collect real-time data, providing healthcare professionals with actionable insights.

Mental Health Support – Chatbots have shown promise in offering mental health support by providing patients with an outlet to discuss their feelings and receive advice. Building these chatbots involves training them on therapeutic conversational frameworks like Cognitive Behavioral Therapy (CBT), ensuring they offer relevant advice while recognizing when a human intervention is required.

Key Considerations for Developers

1. Natural Language Processing (NLP) and AI Training

NLP plays a pivotal role in enabling chatbots to understand and process patient queries effectively. Developers must focus on the following:

Training Data: Start by gathering extensive datasets that include real-life medical queries and patient conversations. This ensures that the chatbot can recognize various intents and respond appropriately.

Multi-language Support: Healthcare is global, so building multi-lingual capabilities is critical. Using tools like Google’s BERT or Microsoft’s Turing-NLG models can help chatbots understand context in different languages.

Contextual Understanding: The chatbot must not just respond to individual queries but also maintain the context across the conversation. Developers can use contextual models that preserve the state of the conversation, ensuring personalized patient interactions.

2. Security and Compliance

Healthcare chatbots handle sensitive patient information, making security a top priority. Developers must ensure compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) in the U.S. and GDPR (General Data Protection Regulation) in Europe. Key practices include:

Data Encryption: All communication between the chatbot and the server must be encrypted using protocols like TLS (Transport Layer Security).
Authentication Mechanisms: Implement two-factor authentication (2FA) to verify patient identity, especially for sensitive tasks like accessing medical records.
Anonymization: To avoid accidental data breaches, ensure that the chatbot anonymizes data where possible.

3. Seamless Integration with EHR Systems

For chatbots to be truly effective in healthcare, they must integrate seamlessly with Electronic Health Record (EHR) systems. This requires a deep understanding of healthcare APIs like FHIR (Fast Healthcare Interoperability Resources) or HL7. Developers should aim to:

Enable Real-time Updates: Ensure that chatbot interactions (e.g., new appointment schedules, and symptom checks) are instantly reflected in the patient’s EHR.
Avoid Data Silos: Ensure that all systems (EHR, chatbot, scheduling system) can communicate with each other, eliminating data silos that can lead to fragmented patient information.

4. Scalability and Performance Optimization

In healthcare, downtime can be critical. Developers need to ensure that chatbots are scalable and capable of handling thousands of patient interactions simultaneously. Using cloud-based platforms (AWS, Google Cloud) that offer auto-scaling capabilities can help. Additionally, performance optimization can be achieved by:

Caching Responses: Store frequently used responses (such as FAQs) in memory to speed up interaction times.
Load Balancing: Implement load balancers to distribute incoming queries across servers, ensuring no single server is overwhelmed.

Image Credit: https://www.analytixlabs.co.in/blog/ai-chatbots-in-healthcare/

Tools and Platforms for Building Healthcare Chatbots

Several tools and platforms can aid developers in building healthcare chatbots with conversational UIs:

Dialogflow (Google): Offers pre-built healthcare intents and integrates with Google Cloud’s healthcare APIs.
Microsoft Bot Framework: A scalable platform that integrates with Azure services and offers AI-driven insights.
Rasa: An open-source NLP tool that provides flexibility in creating highly customized healthcare bots.

Conclusion

Conversational UI in healthcare is transforming patient care by offering real-time, scalable, and personalized interactions through chatbots. However, for developers, building these systems goes beyond programming chatbots — it involves understanding the unique challenges of healthcare, from regulatory compliance to seamless integration with hospital systems. By focusing on NLP capabilities, ensuring security and privacy, and integrating with existing healthcare infrastructure, developers can create chatbots that not only enhance patient interaction but also alleviate the burden on healthcare providers.

References

NLP in Healthcare: Opportunities and Challenges
HIPAA Compliance for Chatbots

About the Author:

Shristi is a creative professional with a passion for visual storytelling. She recently transitioned from the world of video and motion graphics to the exciting field of product design at Mantra Labs. When she’s not designing, she enjoys watching movies, traveling, and sharing her experiences through vlogs.