Android Developers: 3 latest new features in Android

Published on May 29, 2017

Updated on Apr 17, 2020

Many new updates happened for Android developers lately after Google I/O. Initially there was no restriction on some features but now they have updated them with some restrictions.

We have covered new features and the old features as well with new restrictions.

Here are the old features with new restrictions:

• Background Execution Limits

Whenever an app runs in the background, it consumes some of the device’s limited resources, like RAM. This can result in an impaired user experience, especially if the user is using a resource-intensive app, such as playing a game or watching a video.
To lower the chance of these problems, Android O places limitations on what apps can do while users aren’t directly interacting with them. Apps are restricted in two ways:

Background Service Limitations: When an app’s service is running in the background might consume device resources which may lead to bad user experience, to avoid these type of issues Android system applies a number of limitations on background services, this does not apply to foreground services, which are more noticeable to the user.
Broadcast Limitations: Apps targeted Android O can not use their manifest to register for implicit broadcasts. They can still register for these broadcasts at runtime, and they can use the manifest to register for explicit broadcasts targeted specifically at their app.

Note: The restrictions are applied by default applied to apps which are targeting Android O and in terms of other applications users can enable these restrictions from the Settings screen even if the app has not targeted Android O.

• Android Background Location Limits

Considering battery usage and user experience , background apps which are using Android locations APIs to fetch the user’s location will receive location updates less frequently when the app is being used in a device running Android O, developers who are using Fused Location Provider (FLP), Geofencing, GNSS Measurements, Location Manager, Wi-Fi Manager will get affected by this change.

• Notifications

Notification Badges

Notification Badges are the new way of notifying users regarding the new notifications arrived for a particular app, this will display badges on app icons in supported launchers which show notifications associated with one or more notification channels in an app, which the user has not yet dismissed or acted on.
Notification Channels

Using Notification channels developers can group their application’s notifications by category so that the user can apply few characteristics basing on the notification category. When you target Android O, you must implement one or more notification channels to display notifications to your users. If you don’t target Android O, your apps behave the same as they do on Android 7.0 when running on Android O devices.

Google says that the following characteristics can be applied to notification channels and that when the user assigns one of these, it will be applied channel- wide and they are as follows

Importance
Sound
Lights
Vibration
Show on lock screen
Override do not disturb

Here are some new features:

• New in UI and Styling

There are bunch of new features of UI and Styling are introduced in Android O and are as follows

1. Fonts

Android introduced fonts in XML through which we can use custom fonts as resources, You can add your custom font file in res/font/ folder to bundle fonts as resources and can access as a normal resource file and Android Support Library 26 introduce support for APIs to request fonts from a provider application instead of bundling files into your project which helps in reducing your application size
To use these font features on devices running Android API version 14 and higher, a developer needs to use the Support Library 26.

2. Auto Sizing Textviews

By using Support Library 26 Beta developers can now instruct to their app’s Textview to automatically increase or decrease the size to fit perfectly within the boundaries of the Textview.

3. Adaptive Icons

Adaptive icons can display app’s launcher icons in a variety of shapes across different devices for instance in Google Nexus the launcher icon might be in circular and in some Samsung device it might be squircle. Google says that with Android O, each device can provide a mask for the icon, which the OS can use to render all icons with the same shape. This will likely be embraced by OEMs(Original Equipment Manufacturer) who would like to have some unique looking home screens.

4. Autofill Framework

This framework will help the user by pre-filling the user information and user can save time as Filling out forms is a time-consuming and error-prone task. Users can easily get frustrated with apps that require these type of tasks. The Autofill Framework improves the user experience by providing the following benefits:

Less time spent in filling fields Autofill saves users from re-typing information.
Minimize user input errors Typing is prone to errors, especially on mobile devices. Removing the necessity of typing information also removes the errors that come with it.

• Picture in Picture Mode

In Android 7.0, Android TV users can now watch a video in a pinned window in a corner of the screen when navigating within or between apps whereas it was not available to other devices whereas from Android O Picture in Picture is available to all the devices, not just the Android TV.

• Kotlin For Android

Java is the mostly used programming language for the development of Android, When you run a Java application, the app is compiled into a set of instructions called Bytecode and runs in a virtual machine. Many alternative Languages has been introduced to also run on the JVM through which the resulting app looks the same for the JVM
JetBrains, known for IntelliJ IDEA (Android Studio is based on IntelliJ), has introduced the Kotlin language.Kotlin is a statically-typed programming language that runs on the JVM. It can also be compiled to JavaScript source code.

Why Kotlin For Android?

Interoperability with Java
Intuitive and easy to read
Good Android Studio Support
Safe to avoid entire classes of errors such as null pointer exceptions.
Less to write compared to Java
Safe to avoid entire classes of errors such as null pointer exceptions.
Versatile for building server-side applications, Android apps or frontend code running in the browser.

Stay tuned for more new updates on Android.

Check out these articles to catch the latest trends in mobile apps:

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.