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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.

If you are looking for a suitable platform to implement your mobile solution in the most cost-efficient way and, thus, make it cross-platform, it can be a rather challenging task given the variety of different frameworks, libraries, and SDKs.

For example, according to Statista, Flutter and React Native (RN) were the world's two most popular frameworks for cross-platform development by the end of 2023. However, they cannot be called universal, and each is better suited to solving specific problems. Below, we will cover the difference between them in more detail and also conduct a comparative analysis of these two solutions.

What Is Flutter?

Flutter is an open-source framework from Google that first saw the light of day in 2018. It is designed for both front-end and full-stack development of cross-platform solutions with a single code base for iOS, Android, Web, Windows, MacOS, and Linux. Flutter uses the open-source Dart language, which is also a brainchild of Google. Now, it has 162k stars on GitHub.

One of the most significant benefits of Flutter is the ability to accelerate time-to-market: this is ensured by hot reload (which provides the ability to change the program code and check the results in real time), the built-in increased performance provided by direct compilation to native code, as well as the presence of a C++ engine for rendering elements and optimization. In general, according to the unspoken opinion of developers, about 95% of all development tasks can be covered by Flutter libraries and tools.

Benefits of Flutter

Flutter popularity is absolutely justified – it is loved by developers around the world for the following advantages:

• Native-like performance. Due to the use of the Dart programming language, which is immediately compiled into machine code, applications created with this framework are unlikely to be inferior in performance to native analogs.
• Uniformity of visual components. Flutter does not use platform-dependent visualization tools – instead, this framework refers to the Google Skia graphics library, which allows developers to provide a uniform look for UI components regardless of the platform.
• Low entry threshold and a wide variety of available tools and libraries. Flutter is considered an easy-to-learn framework, and such built-in features as hot reloading, widget inspector, and a rich arsenal of tools for solving graphic problems (now, this framework already has graphics rendering at 120 fps) make the process of working on the frontend even more enjoyable and fast.

What Is React Native?

React Native is a cross-platform JavaScript-based framework created by the Facebook team in 2015 and introduced to the public as a solution for reusing the same code. Today, this platform can boast 116k stars on GitHub and the fact that it is at the heart of such world-famous services as Instagram, Facebook, and Skype. 

An interesting feature of this framework is its basis on the Flux architecture, which is based on unidirectional flows. This approach allows data to be passed from one parent component to its child components, making it easier for developers to manage data flows in the application. Also, like in Flutter, there is the Hot Reload feature that simplifies the testing and debugging processes, as well as a rich set of built-in components to boost the development speed of high-performance and visually attractive mobile solutions.

Benefits of React Native

The main benefits of React Native are:

• Ability to reuse code by up to 90% and reduced time to market. The reusability of code written in this framework can be up to 90%. In addition, the created assembly, for example, for Android, will require only minimal configurations in order to function properly on iOS. All these features of this framework speed up the launch of projects.
• Fast update in app stores. Thanks to the CodePush function, applications can be updated within the repeated deployment process without an intermediate step involving uploading updates to the app store. In particular, when the update is ready, its version that has already been presented on the store uploads it itself, without the need for re-communication with Google Play or the App Store.
• A wide variety of libraries and access to native components. While it provides access to native platform components, there are also internal libraries with well-developed documentation and, in some cases, even entire application templates, including Native Base, React Native Elements, and React Native Starter Kit.

Key Differences Between Flutter and React Native

Now it's time to start a more detailed comparison of React vs Flutter.

Entry threshold

Even though both mobile app frameworks are considered to be quite easy to learn, Flutter, due to its abundance of internal development tools, practically eliminates the need for developers to use third-party tools and libraries (unlike RN). This means that its entry threshold can formally be considered lower.

Performance

As for React Native vs Flutter performance, the second one has a lot of widgets based on its own high-performance rendering engine, which makes it suitable for developing applications with complex graphics. At the same time, despite the claims of RN creators, this solution can impose certain problems with the speed of work of resource-intensive projects.

Time ​​to market

In our Flutter vs React Native comparison, RN gives Flutter a head start when it comes to developing applications with different designs for each of the platforms since the latter is aimed at building uniform interfaces.

Specifics for creating user interfaces

While Flutter offers developers a variety of ready-to-use widgets based on the Material (Android) and Cupertino (iOS) designs, React Native refers to the native components of the platforms.

Full list of supported platforms

Flutter supports development for Android, iOS, Windows, macOS, Linux, Google Fuchsia, Web, Tizen for Samsung Smart TV, LG webOS, as well as for embedded devices. As for RN, this framework is compatible with Android, iOS, Windows, MacOS, as well as Android and iOS TV platforms.

Conclusion

We hope that we have answered the essential question: “Flutter or React Native?” and it will be easier for you to choose the best of these two options. At the same time, we would like to note that the future of Flutter is uncertain, as Google has suspended its active development and significantly reduced its team. Therefore, we will keep our finger on the pulse and inform you about all upcoming changes.

Anyway, whichever framework you settled on, an equally important aspect will be the choice of an experienced team of developers. In particular, you can contact us for a consultation and also to find out the cost of development of your project.

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.

What Is Artificial Intelligence (AI)?

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.

Programming Languages ​​Most Often Used for AI in 2024

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 seven languages that are most often used in AI programming.

Python

This programming language appeared long before the popularization of AI development. However, thanks to its low entry threshold and extensive compatibility, its community quickly grew, and today, Python is considered one of the three most relevant languages worldwide.

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, and many others. Using them, developers can create high-performance, reliable, and scalable solutions that can solve even the most complex research problems.

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).

Java

This portable and scalable programming language is actively used in the development of highly intelligent corporate applications, especially for mobile platforms. In addition to compatibility with popular tools for ML 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.

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.

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) .

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.

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. It is also worth noting that Julia projects can be integrated with TensorFlow.jl, MLBase.jl, and MXNet.jl.

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.

If you are looking for an experienced team that will launch the digital transformation of your business processes through custom-made AI and ML solutions, feel free to contact us.