Understanding the Key Differences Between Chatbots and Conversational AI

With the global aging population and the rise of chronic diseases, the remote patient monitoring market is booming as ever. From 2023 to 2024, it grew from $30.97B to $36.31B. And it won't stop here. Insider Intelligence predicts 70.6M US patients will use software for remote patient monitoring by 2025. 

We can't overstate the importance of remote patient monitoring software. Such apps improve access to care, reduce hospital readmissions, and help save up to 60% on hospital visits, which results in higher-quality treatment in the comfort of patients' homes. 

And benefits don't end up here. With ongoing digitalization via IoT and modernization of the healthcare system, don't wait for the best time for remote patient monitoring software development. The best time is now. 

We've detailed remote patient monitoring apps below and covered their benefits, architecture, and top examples. You'll also see how Darly Solutions crafted an app for the health tech company. Read on, and stay tuned.

RPM monitoring software—is a digital tool for wearables, mobile apps, and medical devices that collects, analyzes, and shares patient data with medical personnel. Heart rate, blood sugar, and oxygen levels are among the most vital metrics. RPM software scores help catch health issues early, manage chronic conditions, and aid post-operative recovery.

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How remote patient monitoring software works

From heart rate to blood glucose, each RPM system is designed to monitor a specific aspect of the body. However, elements included in any remote patient monitoring software development are typically the same. This is how the RPM system works:

• Collects and exchanges data.
  Once calibrated, the devices send the data from the patient to the provider. The devices must have a Wi-Fi or LTE adapter for successful transfer. The data can also be sent via Bluetooth to IoT mobile apps via RPM devices.
• Centralizes storage.

The provider securely stores the data collected from various sources, including patient devices, analytics systems, and e-medicine.

• Analyze.
  The data is processed using advanced AI for management and analytics. Alerts are sent to caregivers, providers, and patients as needed, per their preferences.
• Broadcasts.

Remote monitoring systems become telemedicine platforms when integrated with communication features like video conferencing, simplifying doctor-patient interaction.

• Sends emergency notifications.
  When doctors identify health issues requiring daily monitoring, they oversee the data collected by the RPM system. Notably, it can notify emergency services if a patient needs urgent care. For non-urgent cases, patients can receive instructions for the next steps. However, HIPAA and similar regulations may restrict including sensitive patient information in certain notifications to protect privacy.

Why build remote patient monitoring software? Key advantages

Nevertheless, the RPM is part of the broader automation revolution; it has universal healthcare benefits overall. Let's examine the main of them:

Better access to health services

With remote patient monitoring software, medical services have no geographical boundaries. Patients can get timely medical attention at home and manage their health, fostering a greater sense of control under health conditions.

Improved patient outcomes

A study found that RPM cut hospital admissions for chronic care complications by 19% to 41%, which results in improved patient outcomes.

Cost-effective care with reduced readmissions

The average cost of hospital readmission is about $15,200, which makes the prevention of readmissions through remote patient monitoring software financially beneficial for both patients and providers. Even more, it is a well-established fact that early detection generally translates to lower healthcare costs.

Better client engagement and satisfaction

Over 94% of patients are satisfied with RPM's care. It sparks their curiosity about their health and boosts engagement. Such an increased sense of involvement often inspires commitment to treatment plans.

Early detection and timely intervention

The CDC reports that RPM can cut chronic disease complications by 40%. This highlights the need for swift intervention to reduce health risks.

Components of remote patient monitoring software

There is no one-fit RPM soft, as it differs in nature. However, each patient monitoring system generally has 3 to 5 basic components. Let’s delve into them: 

Personal monitoring devices

From continuous glucose and heart rate monitors to blood pressure cuffs, digital scales, and peak flow meters. 

Patient-side mobile applications

Prioritize user experience, making it easy for patients to access their health data. Additionally, they offer features like medication reminders and secure chats with healthcare providers.

Cloud database

Cloud repositories allow healthcare providers to store large patient data securely. They are scalable and accessible, adapting to growing datasets for long-term monitoring.

Hospital-side web app

These applications are part of hospital management software. They ensure compliance, enable integration, and support decision-making. Additionally, they offer reporting, notifications, and analytics to improve patient care.

Other modules

These comprise decision support systems, reporting tools, notification systems, and analytics platforms.

Essential features in remote patient monitoring solutions

Here are the essential features of a remote patient monitoring (RPM) solution:

Real-time tracking of patient vital signs

Wearable sensors, fitness trackers, blood pressure monitors, and glucose meters perfectly gather and handle real-time health metrics. Then, this data is sent to healthcare providers and added to Electronic Health Records (EHRs) for timely interventions and informed decisions preventing complications.

Intuitive interfaces and user-friendly features

Machines must be easy to set up, a breeze to wear, and simple to operate. Meanwhile, patients should access their data effortlessly through a smooth web or mobile app to track their progress. This connection allows seamless communication with healthcare providers, ensuring everyone's on the same page.

Accessibility features

Ultimately, create a system with all users in mind. Consider accessibility guidelines like WCAG and CVAA to pave the way for inclusive innovation. Encompass voice control for effortless data entry, navigation, and text-to-speech features to support people with dyslexia and other disabilities. Moreover, a patient-first design shines with screen reader compatibility, high-contrast modes, and a crystal-clear interface.

Personalized risk alerts and reminders

For swift and customizable alerts, the RPM system must adjust thresholds. For example, alerts can be sent when vital signs fall outside preset limits. This enables timely intervention to reduce the risk of adverse events.

Safe data transmission and storage

Patient data security is a top priority in remote patient monitoring software development. Thus, apps must comply with all regulations like HIPAA, as they protect the privacy and security of patient data and save from hefty fines.

Smooth EHR integration

EHR integration allows the automatic transfer of RPM data, giving healthcare providers access to complete patient histories for better decision-making and outcomes.

Reports and analysis of patient health data

Advanced analytics and reporting in remote patient monitoring software development provide valuable data insights for healthcare professionals (for data-driven decisions) and patients (to motivate healthier choices).

The architecture of a remote patient monitoring system

Remote health monitoring systems are designed with a multi-layered architecture. Basically, it has five core components that enable uninterrupted data flow within the system. Let’s break into the point of the whole structure:

Smart-connected sensors for health tracking

The sensor layer comprises remote monitoring devices such as EKGs, sleep trackers, smartwatches, and blood pressure monitors. Then, they transmit this data to the dedicated mobile app via Bluetooth or to the cloud via the Internet.

Patient interface

Patient-facing RPM apps collect real-time health data from connected medical devices, allowing manual entry of symptoms and medication intake. RPM apps often include data visualizations for tracking health trends and may have integrated telemedicine features, such as video conferencing for appointments, follow-ups, prescriptions, and virtual consultations.

Cloud RPM server

Cloud services form the core of RPM systems, providing secure storage, processing, and data analysis from devices and the RPM app. This engine also integrates with EHR systems to access patient health information (PHI) in FHIR format. It can identify trends, such as early signs of complications.

Medical staff dashboard

Patient data flows bidirectionally between the medical staff interface and the cloud RPM server, enabling effective management by care teams.

• Data visualization module.
  Via charts and graphs that show trends in vital signs, medication adherence, and other data, the module gives providers a 360-degree view of the patient. 
• Alert management module.
  The system promptly alerts clinicians to critical situations and device failures in real-time.
• Reporting module.
  The module generates custom reports on patient data, which hugely help track progress and gain insights over time.
• Communication module.
  This module has two-way messaging, video chat, and/or telephony integration and is made for secure, remote communication between clinicians and patients.
• Appointment management module. 

This module handles appointment scheduling and patient-provider communication, allowing both patients and providers to request appointments.

Depending on the organization's needs, a doctor-facing RPM interface can include other modules, such as patient management, care plans, and patient education.

Admin web panel

A key part of the RPM architecture is a control dashboard. It helps with admin tasks like configuring roles, managing patients, and setting alarms.

Remote patient monitoring software development: Key steps

Once you have a clear idea for your software product, find an experienced dev company to implement it. When contacting your tech partner, you don't need detailed software requirements. A high-level understanding of the functionalities is enough to get started for any custom healthcare software development services stages. Let’s break them down:

Step 1: Requirement collection and analysis 

The process starts by refining your product vision. Then, define project goals based on the health conditions your RPM solution will address. Your team will then identify core functions, pick the best tech stack, and plan the app's architecture. If integration with systems like EHRs is required, your tech partner will ensure compatibility.

Step 2: Outlining security and compliance requirements

Remote patient monitoring software must follow HIPAA and FDA rules. The development team implements audit trails, encryption, secure cloud storage, and APIs for secure communication to ensure data security and privacy. They also define a testing strategy for the pre- and post-production phases.

Step 3: Prototyping and design

To create an intuitive, user-first staff interface, UX/UI teams conduct user research to define user personas and journeys. Based on this research, designers create a prototype reflecting both the initial vision and user needs, gathering feedback on the user experience. The team prioritizes a clean, engaging, and accessible design. Based on feedback, they will refine the prototype and send the final designs to the dev team.

Step 4: Development and integration

Tech experts will use best practices to develop your RPM system iteratively to meet regulatory compliance. This work may include front and backend components, patient and doctor apps, and integration of medical devices.

Step 5: Testing

When it comes to RPM development—thorough software testing is as important as the development itself. After testing, developers should prioritize IoMT device compatibility. They must also ensure smooth integration with the RPM system for the most stability.

Step 6: Ongoing improvements 

After deployment, the dev team monitors and tunes the RPM system to meet business and tech needs. They provide training and support for patients and healthcare professionals.

Beyond monitoring and troubleshooting, your tech team will prepare a roadmap for software updates. This will enhance the system's features and security in the future.

RPM Software Deployment: Challenges and Practical Solutions

Let us explore the most notable challenges organizations encounter while deploying their RPM. 

Regulatory compliance

RPM's compliance with HIPAA and GDPR relies on strong data security and privacy. It must meet standards like ISO 27001 and IEC 62304 for this.

What to do:

• Develop a comprehensive compliance framework according to all requirements like HIPAA, GDPR, FTC (Federal Trade Commission), FDA regulations, and more.
• Conduct regular audits and assessments outlining clear policies and protocols for staff.
• Provide ongoing staff training to ensure they are aware of their responsibilities.

Data security and privacy

Healthcare organizations handle sensitive patient data (vitals, medical history, personal details, reports, treatment plans). Unauthorized access poses the risk of data breaches and subsequent fines that could total millions of dollars.

What to do:

• Use encryption techniques of RPM-collected data, telehealth appointment recordings, and other sensitive patient health data.
• Implement strong authentication and role-based access.
• Regular audits and security assessments.
• Conduct employee and patient training.
• Adopt role-based access control (RBAC)

Technology integration and compatibility

Outdated systems may lack compatibility, hindering integration and impacting data accuracy and security. Integrating EHR and clinical systems can also be difficult, affecting data sharing and patient care. Ensuring interoperability for seamless data exchange adds another layer of complexity.

What to do:

• Assess current infrastructure compatibility.
• Follow interoperability standards and protocols.
• Adopt HL7 and FHIR standards.

Cost-benefit analysis

RPM implementation presents a cost-benefit challenge for healthcare organizations. While offering benefits like improved outcomes and enhanced care, the substantial upfront investment in hardware, software, infrastructure, licensing, staff training, regulatory compliance, and data security must be carefully weighed.

What to do:

• Conduct thorough cost assessments.
• Estimate ROI and potential cost savings.
• Strategically allocate resources.

Data precision and accuracy

Inaccurate data transmission from RPM devices (e.g., wearables) poses a significant challenge, potentially leading to misdiagnosis and compromised treatment. Several factors can affect data accuracy, including device calibration, signal interference, network issues, and device quality.

What to do:

• Choose quality sensors and wearables.
• Regularly calibrate devices.
• Implement quality assurance measures.

Lack of patient engagement

Some patients fear switching to modern healthcare and its tech just because they are used to in-person hospital visits. Thus, using the devices, checking vital signs, and messaging the doctor may be hard for the patients. 

What to do:

• Prioritize a user-friendly interface.
• Craft tutorials for device usage and RPM data analytics interpretation.
• Incorporate gamification elements (e.g., badges, progress tracking) to incentivize engagement.
• Bridge language barriers for diverse patient populations by providing support in multiple languages.

RPM software development cost

The cost of developing Remote Patient Monitoring (RPM) software varies based on complexity, features, and the development team's location. Here’s a breakdown of the estimated costs associated with RPM software development:

• Basic RPM application ranges from $50K–$70K. 
• Standard RPM software ranges from $100K–$400K or higher, depending on the specific requirements and features.
• Costs can be $500K or more for advanced solutions with complex systems and integrations.

Both building an in-house tech team and outsourcing software development come with significant costs: salaries/recruitment expenses versus hourly rates and vendor fees. Without proper research, you risk overpaying for a software solution—whether through in-house hiring costs or outsourcing fees—or settling for a product that doesn't meet your requirements.

To calculate the precise budget for your RPM software development idea—please fill out this form. We will contact you shortly.

Best RPM software

Here are some popular remote patient monitoring use cases that might inspire you for your own:

CoachCare

CoachCare was founded over a decade ago, now supports over 3,000 organizations, and serves over 150,000 patients. Boasting a remarkable RPM, it claims a success rate exceeding 95%. It advocates for RPM coverage and collaborates with groups like CMS and AMA. CoachCare's tech lets patients log vital health metrics via an app. Its simplicity boosts engagement. Experts recognize its support of the healthcare industry.

Healee

Healee is a health tech company founded in 2017, now handles over 700,000 visits per month, both onsite and virtual, and supports 5,000 physicians across 200 clinics. With EHR, e-prescription, and billing integrations, the Healee platform streamlines healthcare delivery in wellness, mental health, chronic care, and women's health.

Doctolib

Founded in Paris in 2013, Doctolib is a top healthcare booking platform. It serves over 400,000 providers and 50 million users. It handles 70 million monthly bookings in France, Germany, and Italy. It offers appointment scheduling, telemedicine, and patient messaging. These services reduce missed appointments by 60% and save doctors 1.5 hours per week on administrative tasks.

Senseonics

This company revolutionizes blood glucose monitoring. The system features a sleek, removable transmitter and a sensor expertly implanted under the skin by a healthcare pro. Connected to a mobile app, the sensor offers real-time insights, alerting patients through their smartphones or smartwatches when their glucose levels are too high. Sharing data with healthcare providers and family members becomes a breeze, ensuring optimal treatment strategies.

Currently, around 6,000 users benefit from the Eversense CGM system. By 2025, Senseonics aims to double that number.

Teladoc

Established in 2006, this Hoboken, New Jersey-based powerhouse is revolutionizing healthcare. With an extensive network of licensed professionals, HRS proudly claims the #1 spot in KLAS’ ‘Top Remote Patient Monitoring Companies’ from 2020 to 2023.

HRS offers its innovative PatientConnect platform, featuring over 90 tailored RPM plans for healthcare providers. Beyond monitoring, HRS embraces holistic care, providing behavioral health services like therapy and psychiatry, ensuring a comprehensive touch on every aspect of patient wellbeing.

Improving patient outcomes with RPM software: A case by Darly Solutions

In particular, the health tech company approached us about remote patient monitoring software development. They faced challenges with patient tech literacy, especially among the elderly. There was a risk of data overload for providers. Patient engagement was inconsistent. There was a need for system integration, like with EHRs. Plus, they had to ensure data privacy and security (HIPAA compliance).

Since the deadline was tight and there were a lot of high-precision requirements—we created a detailed plan for the project, assigned UX/UI design, web development, QA, and project management tasks, and pinpointed the tech stack.

Technology stack 

We understood that choosing the right technology stack was crucial for success. After thorough consideration, we selected:

• Angular: For building dynamic web applications.
• HTML/CSS: For structuring and formatting the user’s interface.
• Docker: To streamline deployment through containerization.
• ngRx: For effective state management.
• TailwindCSS: To enable rapid UI development.
• NGX Pagination: For efficient handling of large datasets.
• JSON: For smooth and efficient data transfer between client and server.

Our efforts paid off. We launched the product on time, meeting all design, functionality, and quality standards. Our client now has a competitive product that is well-placed to thrive in the innovative telemedicine sector.

Read the details of the case here.

Wrapping up

Rising public interest, health concerns, and tech advances drive the RPM market to new heights in solutions and revenue. As we noted earlier, these technologies enable 24/7 health monitoring. They also provide remote populations with access to quality treatments.

Doctors, nurses, and other healthcare staff should focus less on routine, manual tasks in the next few years. They should focus on saving lives and providing treatment, which RPM can support perfectly. It can greatly reduce burnout and ease stress on global healthcare systems while driving market share to higher results and stakeholder benefits.

This is a great time if you're considering developing your RPM solution.

Today, web applications are changing how we approach education, making learning more accessible and engaging for everyone. These digital tools are proving invaluable for educational organizations and learners alike, offering numerous benefits:

• Customized Learning. Web apps adjust to each student’s learning speed and preferences, making lessons more engaging and successful.

• 24\7 Availability. Students can study whenever and wherever they like, overcoming obstacles like distance and scheduling conflicts.

• Cost-Efficiency. By using less physical space and materials, web apps help schools save money, which could also lower costs for students.

• Data-Driven Teaching. Teachers get detailed reports on how students are doing, helping them provide the right support and improve their teaching methods.

• Interactive Collaboration. Web apps create a community where students and teachers can easily share ideas and help each other, making learning a shared effort.

Why trending websites and apps are popular with tens of thousands of companies nowadays? Including those that provide educational services in various sectors.

Today's advancements in computer technology are reshaping every aspect of our lives, including education. IT companies are constantly expanding their offerings, providing applications and websites that make learning more accessible than ever. With these tools, you can master foreign languages, dive into various programming languages, explore themed literature, or even learn to play the piano.

Types of Teaching Platforms

As education adapts to digitalization, our company keeps pace by providing web solutions for all kinds of educational needs. We create platforms for corporate training, online courses, and live webinar services, all designed to meet the unique needs of today's learners and educators:

• Corporate Training. Designing platforms for the corporate sector, focusing on staff training with features for conducting webinars, such as iSpring Learn and Teachbase.

• Distance Learning Course Creation. Developing sites for high-quality distance learning courses tailored for educational institutions, similar to Moodle and Google Classroom.

• Webinars and Online Lessons. Creating platforms for hosting webinars, online lessons, and open seminars, with options for managing and checking homework, comparable to GetCourse.

Our Development Approach

We focus on really understanding what our customers need. We pick the best technologies for each project, making sure our solutions work well and aim for the highest success. We show our dedication to quality by:

• Technology Selection. We carefully choose technologies that align with our client's specific needs, aiming for solutions that are both practical and effective.

• UI/UX Design Excellence. Our projects stand out for their user-friendly and engaging interfaces, a testament to our commitment to superior UI/UX design.

• Benchmarking Against the Best. We benchmark our work against industry standards, aspiring to match the design quality and functionality of leaders like ZeBrains and Wezom.

• Creativity and Style. We infuse each platform with creative designs and stylish patterns, aiming to enhance user engagement and experience.

Examples

Explore how digital education is changing the way we learn with our selection of innovative platforms. These examples cover everything from general education and coding to language learning and creative arts, showing the impact of technology on making learning more accessible and engaging. See how education is evolving and how our experience can help you successfully launch your educational IT product.

Khan Academy

Khan Academy, based in Mountain View, USA, provides free, comprehensive educational resources online. Covering subjects from math to humanities, its interactive exercises and videos allow learners of all ages to study at their own pace. Khan Academy is dedicated to making education accessible to everyone, anywhere.

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Sputnik ATX

Sputnik ATX VC’s 13-week accelerator teaches startups how to sell and gain traction. They help founders reach their full potential by creating an ecosystem to attract hard-working nerds, connecting them with investors, and training them for success. Their program provides start-up training, mentoring, and support to successful applicants.

CodeCraft

CodeCraft Academy, based in San Francisco, excels in digital education with its coding and software development courses. Offering interactive challenges, hands-on projects, and mentorship from experts, it serves all levels of learners, from beginners to advanced. Its focus on web development and data science is complemented by a unique portfolio-building feature, enabling students to showcase their skills to potential employers.

DuoLingo

Duolingo, a renowned language learning app from Pittsburgh, USA, stands out for its engaging and scientifically-backed approach. It uses gamification, brief lessons, and instant feedback to make language learning enjoyable and effective. Offering courses in over 30 languages, including both popular and lesser-known ones, Duolingo serves millions of users globally, making it an essential resource for language learners and professionals.

MasterClass

MasterClass delivers unmatched online learning in arts, music, writing, cooking, and more, with courses led by famous experts and celebrities. Its piano lessons, for example, are taught by renowned musicians. High-quality videos, interactive tasks, and community engagement let learners progress at their own speed, connecting with others. MasterClass makes top-tier education accessible worldwide, offering inspiration and expertise directly to learners' homes.

To find out how you can successfully launch your educational IT product – just contact us. Our team has great experience in this area, and we are also time-efficient, result-oriented, budget-friendly, and our staff work at an expert level. Our motto is “Less hassle, more work”!

If you regularly think about how to optimize your business operations by speeding them up, increasing their accuracy, reducing related expenses, and eliminating risks associated with the human factor, perhaps the introduction of artificial intelligence will be the best option. 

Specifically, according to the CompTIA IT Industry Outlook 2024 report, today, about 22% of companies are actively seeking to integrate AI into most of their technological solutions and processes, and 45% of companies are considering its implementation. If you are still in the second category, it's time to figure out which programming language will be able to form the basis of your new highly intelligent software tools.

The term “Artificial Intelligence” describes software algorithms and systems that are aimed at solving problems typical to the human mind—such as learning, as well as pattern generation and recognition. Such algorithms are based on colossal amounts of disparate data collected from various sources. Thus, these algorithms form self-learning software solutions capable of analyzing this data and extracting valuable insights from it.

Top 10 Programming Languages for Artificial Intelligence in 2025 

It is worth noting that AI-powered solutions may not be based on any software language—this can be explained by the high resource consumption of the AI algorithms that form them, as well as the lack of high-level functions. At the same time, there are ten languages that are most often used in AI programming. Which one of them? best programming language for AI?

Python 

At the top of our list of programming languages for artificial intelligence is Python. This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold, open-source code and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide. Beginners can easily find support and useful resources on platforms such as GitHub and Stack Overflow.

As for the tools for artificial intelligence programming, there are a lot of Python-compatible frameworks, libraries, and APIs, aimed at simplifying and speeding up this process. Among them are TensorFlow, PyTorch, Keras, SciPy, NumPy, Scikit-learn and Matplotlib and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems. This significantly saves time since, instead of creating everything from scratch, you can simply import ready-made tools.

One of the factors that makes Python one of the most popular AI programming languages is its simple and intuitive syntax, which ensures ease of learning.

Python is also known for its versatility, making it a top programming language for artificial intelligence among data analysis scientists and software developers. For example, it is actively used to create neural networks and machine learning algorithms with frameworks like TensorFlow and PyTorch. Additionally, Python is widely applied in data analysis, helping to identify patterns, trends, and correlations.

Best suited for: AI development, data analysis, and machine learning.

Tip: If your project involves resource-intensive computations and requires a detailed analysis of requirements, it is worth considering other AI programming languages.

R

R, being a statistical programming language, is great for data analysis and visualization. Even though developing artificial intelligence and machine learning solutions is not the primary use case for R, it is still adept at handling very large numbers. So, where popular Python may fall short, particularly in Data Science and Data Analysis solutions, R may be a better alternative.

Additional benefits of this developer tool include support for functional programming, vector computing, and OOP, as well as a number of packages specifically created for AI and ML development (for example, gmodels, RODBC, and OneR).

It includes numerous visualization libraries such as Ggplot2, Lattice, and Shiny, which allow for the quick creation of graphs, charts, and other visual representations of data. Thanks to built-in functions like GLM and LM, specialists can perform statistical modeling.

The Caret library enables building, training, and testing machine learning models, as well as optimizing their performance through hyperparameter tuning.

R also provides tools for performance evaluation, including resampling methods and cross-validation, which help developers improve their AI models. Additionally, R supports integration with other programming languages, such as Java and Python, creating a powerful ecosystem for developing various applications.

Best suited for: Statistical analysis, data processing, and visualization in AI projects.

Tip: R is the best programming language for AI in the early stages of research and experimentation. However, its use for deploying machine learning models in real-world products may be limited, as it lacks built-in compilers and efficient memory management. Moreover, it may be challenging to learn for those who do not specialize in data science.

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. Thanks to its platform independence, Java programs can run on any operating system where the Java Virtual Machine (JVM) is installed. In addition to compatibility with popular tools for ML programming and AI programming, such as the Deep Java Library for deep learning, OpenNLP for natural language processing, and Neuroph for building neural networks, Java is easy to write and debug program code.

It is worth noting that this language is object-oriented and scalable. It also simplifies data visualization and supports Swing and SWT (Standard Widget Toolkit), which allow developers to create attractive and complex graphics and interfaces.

Java offers a wide range of built-in libraries and modules, such as JUnit and Apache Commons, which accelerate development and help create high-performance applications. For example, Java is actively used in simulations, robotics, and embedded artificial intelligence systems. Additionally, the RTSJ extension enables the development of real-time systems, particularly for robotic solutions.

Support for multithreading makes Java suitable for resource-intensive tasks that require parallel execution. The language scales well, allowing for the creation of both simple and complex applications capable of adapting to growing demands. A large user community provides access to numerous learning materials and valuable resources.

Optimal choice for: Developing scalable, platform-independent AI applications and embedded systems.

Tip: As an artificial intelligence programming language, Java is an excellent choice for large-scale business AI systems where algorithms need to be transformed into stable and reliable software. It ensures high performance, reliability, and compatibility with various devices, making it an efficient choice for building high-performance solutions.

Weakness: Verbose syntax, not specifically designed for AI.

C++

C++ is considered an extremely powerful language for AI programming and can greatly benefit developers when creating games and embedded systems. Like Python, C++ is a mature language, which does not detract from its advantages, such as flexibility and high performance. C++ has several libraries for machine learning and neural networks that help complex algorithms run faster (including MapReduce, mlpack, and MongoDB). In general, many software engineers prefer this language for building projects that require high speed, as it interacts with training modules in a production environment.

Actually, that’s why this low-level language used for AI is primarily a tool for creating complex banking software, cloud systems, and intelligent speech recognition solutions.

C++ plays a key role in developing complex software systems with a high level of abstraction. Its popularity in the field of artificial intelligence is due to its flexibility, performance, and ability to efficiently interact with hardware components.

Although machine learning libraries such as TensorFlow and PyTorch are primarily designed for Python, C++ allows working with them through APIs or bindings. This makes it possible to integrate powerful tools into developed applications without the need to build all algorithms from scratch. Thanks to support for multithreading and parallel computing, C++ is suitable for creating high-performance AI solutions and stands out among other machine learning programming languages.

Developers can manually manage memory allocation, for example, by allocating more resources for computationally intensive tasks, which reduces latency and improves performance. Additionally, using OpenCL and CUDA enables hardware acceleration, enhancing algorithm efficiency by leveraging the computing power of graphics processors.

Best suited for: Developing high-performance AI applications with hardware resource control and memory management.

Weakness: A complex language designed for system programming.

JavaScript

JavaScript is a scripting language used to add interactivity to web pages. Even though it is not as popular as the AI programming languages ​​described above, it can be extremely helpful in implementing solutions for Data Science, one of the most promising areas for using JS.

In addition, JavaScript is the most popular programming language in the world, so you won’t have any problems finding the right AI development tools and frameworks that are compatible with it. The popularity of this coding language for AI can be explained by several factors: reliability, stability, abundance of high-level functions, libraries, and tools, as well as fairly low requirements for other technical skills of developers (it is likely that you will not have any difficulty finding dozens of AI tools that support JS, which will cover the whole scope of work related to your AI project).

Among AI programming languages, JavaScript is particularly useful for developing real-time AI applications, including virtual assistants, chatbots, and recommendation systems. By using APIs, developers can easily integrate artificial intelligence into web applications, expanding their functionality.

Since code execution occurs on the client side, this reduces server load and increases speed, which is especially important for applications requiring instant interaction.

With the emergence of JavaScript frameworks such as TensorFlow.js, Brain.js, and ConvNetJS, it has become possible to directly implement machine learning in web development. These tools allow training and deploying machine learning models directly in the browser, expanding AI capabilities in the web environment.

Best suited for: Developing interactive AI applications in the browser and integrating artificial intelligence into web projects.

Weakness: Not well-suited for mathematical computations and machine learning.

Scala

This machine learning language is concise and expressive, and is often used in big data solutions. Scala is a multi-paradigm language specifically designed to express common programming concepts in a simple, convenient, and type-safe manner.

Scala is commonly used in the development of machine learning algorithms and pattern detection in large data sets, and all this – thanks to the built-in Smile data processing library, extensive compatibility with AI frameworks and libraries (in particular, BigDL, Breeze, DeepLearning.scala, and so on), as well as its object-oriented and scalable nature.

It allows writing clear and efficient code, facilitating the creation of high-performance applications.

One of Scala’s key advantages is its default support for immutability. This means that once declared, objects do not change their state, making them easier to track and manage throughout the entire development lifecycle. Similar to C++, Scala supports multithreading and parallel computing, enabling the creation of AI applications capable of performing multiple tasks simultaneously.

Thanks to its compatibility with Java, developers can leverage tools and libraries built for Java, including Apache Hadoop and Spark, which are essential for processing large volumes of data. This helps analyze information, identify hidden patterns, and make informed decisions.

Scala also features a powerful pattern matching system, making it convenient for natural language processing (NLP). This is particularly important in areas where AI needs to recognize and analyze user input to generate appropriate responses. Scala’s object-oriented approach supports the development of specialized tools, such as for prototyping and research.

Best suited for: Developing scalable AI applications with big data support and real-time information processing.

However, Scala is not the best choice for projects requiring rapid prototyping, such as NLP or machine learning model development in tight timeframes. This is due to a limited selection of AI-focused libraries and frameworks, as well as a longer compilation process, which complicates quick testing and model adjustments.

Julia

Julia is the best programming language for AI powered scientific solutions and technical projects that require statistical data processing. This is a relatively new language with dynamic typing, parallel programming, and JIT compilation that has proven itself well due to its high speed (Julia-based projects are rarely inferior in performance to their C++-based counterparts), flexibility, and a mature ecosystem.

Many of the tools built into this language use polymorphic dispatch, which makes switching between them much faster than in other languages. Julia has gained popularity among AI programming languages for AI developers due to its high performance and rapid prototyping capabilities. It ensures fast data processing, which is critical in the field of artificial intelligence due to the vast amounts of data that need to be analyzed. Moreover, its syntax is clear and similar to Python, making the learning process easier.

Julia supports interaction with other programming languages, such as R, C, and Python. This allows developers to use powerful libraries, including TensorFlow and PyTorch, integrating them into their projects without the need to rewrite code.

With built-in support for parallel computing and distributed computational processes, Julia is capable of effectively handling large datasets and significant computational loads without a noticeable drop in performance.

As an open-source language, Julia provides access to libraries developed by the community, saving time and effort during development. Notable tools include Plots.jl and Gadfly.jl for data visualization, MLJ.jl used for machine and deep learning, as well as Scikitlearn.jl, which is an interface to the Python library scikit-learn.

Best suited for: High-performance AI applications that require fast data processing and easy integration with other languages.

Weakness: Newer language, smaller ecosystem.

Haskell

Haskell is based on the functional programming paradigm and uses key concepts such as pattern matching, higher-order functions, and immutability. This makes it suitable for handling complex data structures in the field of artificial intelligence.

The language has a strong mathematical foundation, which contributes to the development of complex algorithms and even deep learning models. Thanks to lazy evaluation, expressions are only evaluated when needed, allowing for more efficient resource usage.

Haskell's strong type system and type safety control help avoid errors related to incompatible values in variables, improving the reliability and stability of AI applications developed in this language.

Best suited for: Creating AI systems with complex algorithms and a focus on functional programming.

Weakness: Abstract, rarely used in the field of artificial intelligence.

Lisp

Lisp is a high-level functional programming language that is well-suited for artificial intelligence development due to its flexibility and ability to process symbolic expressions. These expressions can contain both simple data types (such as strings or numbers) and complex structures like lists and arrays. This makes Lisp effective in working with large data sets, performing complex computations and manipulations.

The recursive programming mechanism allows for the creation of powerful algorithms for analyzing and processing significant volumes of information. Additionally, automatic garbage collection enables programmers to delegate memory management to the system, focusing on developing core functionality.

Lisp's dynamic typing allows the interpreter to determine variable types during program execution. This provides high flexibility and makes it easy to work with complex data structures, which is important for artificial intelligence tasks.

Lisp is one of the first AI programming languages to support functional programming. Its simple yet powerful syntax structure encourages experimentation and research in AI, especially in areas that require symbolic expression processing.

Best suited for: Scientific research and experimentation in the field of artificial intelligence, particularly for working with symbolic expressions.

Although Lisp was once an innovative language in artificial intelligence, over time it has given way to statistical machine learning and neural networks, which currently dominate the field. However, it remains indispensable for developing expert systems and logical decision-making models based on symbolic reasoning rather than data analysis.

Prolog

Prolog is known for its capabilities in logical programming and symbolic reasoning, making it an effective tool for creating AI solutions, particularly for knowledge representation and solving complex problems.

Thanks to its declarative approach, developers can describe the logic of computations without focusing on the details of execution flow management. This allows for easy specification of rules, relationships, and facts, which is especially useful when creating knowledge bases in artificial intelligence.

The powerful pattern-matching mechanism enables quick analysis of large amounts of information and finding matches based on logical relationships. This makes Prolog a valuable tool in areas such as natural language processing (NLP), helping systems interpret and respond to queries. Additionally, this feature supports the development of various algorithmic solutions.

Best suited for: Symbolic reasoning, knowledge representation, and solving logical problems in AI.

Weakness: Limited usage and a small number of libraries.

What Is the Most Popular Programming Language Used in AI in 2025?

Considering the top programming languages for artificial intelligence, it is worth noting that in 2025, Python remains the most popular. Its popularity is driven by its easy-to-learn syntax and the wide range of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which significantly simplify the AI development process.

In addition to Python, other languages are also actively used in AI. These include R, known for its powerful statistical analysis and data visualization tools; Java, which offers high performance and scalability; C++, which provides low-level control over hardware; and Julia, which is quickly gaining popularity for its efficiency in numerical computations.

The choice of programming language for AI depends on the specific requirements of the project, available tools, and the personal preferences of the developer.

The Programming Languages That Are Not Recommended for AI

Along with the extensive list of languages for AI development, there are also those that are better used for solving other problems. These are usually niche and low-level languages, such as PHP and Ruby (scripting languages that do not have the needed flexibility and speed to simplify the development process), C (a low-level language that lacks almost all the functions needed in AI development), and Fortran (despite its use for mostly scientific purposes, it has poor functionality).

Ultimately, a seasoned software engineer will be able to cope with any AI development task using any language; another thing is that, given the expected scope of work, the languages that we described in the previous paragraph are much better suited for these tasks.

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Choosing the Best Programming Language for AI Development

From Python to Scala, different programming languages can be used for AI projects. While they share some similarities, each one has unique characteristics that make it optimal for specific tasks.

Here are a few steps that will help you choose the best programming language for your next AI project:

For Your Project

Start by assessing the requirements and needs of your AI project. Pay attention to the structure of the application and the main features it needs to function properly.

Determine whether you are building a web application, a deep learning model, or a robotics project. Different types of projects may require different programming languages.

For example, JavaScript may be suitable for building web applications, such as those related to trading, as it supports many browser features by default. In contrast, C++ would be the optimal choice for robotics development, as it provides low-level hardware integration.

To Learn

Consider your skill level, as programming languages have different learning curves. For instance, Python has a gentle learning curve thanks to its simple and understandable syntax. On the other hand, C++ has a steeper learning curve, meaning it requires more effort to master.

By taking the time to carefully assess these factors, rather than rushing, you will lay a solid foundation for your AI project. Proper planning at the start will help avoid costly problems in the future. Additionally, it will increase your chances of choosing a programming language that best suits both your current needs and long-term goals.