The Importance of Ukrainian Developers for Global Success

With the never-ending evolution of digital technologies we can see in the last years, businesses can discover more opportunities and options to make a profit, reduce the cost of their operations, and attract/retain customers than ever before. Specifically, this thesis can be applied to the financial industry, one of the leading areas of which is lending.

For example, the independent source Global Data claims that the global digital lending services market will grow at a CAGR of 22.1% until 2030. At the same time, one of the most effective ways of providing such services is by using the Lending as a Service format. Below, we will explain what it is, how such solutions work, what their advantages are, and what impact they can have on the financial sector in the future.

What Is Lending as a Service (LaaS)?

In a nutshell, Lending as a Service is the delegation of all operations related to providing loans to its customers to a third-party provider.

The convenience of SaaS lending technology is obvious even at first glance: instead of independently learning how to manage risks and generally studying how the financial sector and related legislation work, businesses get the opportunity to remain focused on their key operations while expanding their range of services and making them more convenient and accessible to consumers. This approach also reduces the costs associated with the implementation of financial lending services without reducing their effectiveness.

How Lending as a Service Works

From a practical point of view, LaaS is usually presented in the form of cloud solutions that can be adapted to the individual needs of a particular business. Their functionality is available in real time, which means that businesses that have chosen this format of cooperation do not experience any inconveniences associated with delays and downtime.

At the same time, such solutions take on much more than just issuing loans according to the rules and restrictions pre-established by a particular company – they allow these companies to fully automate credit lending services and even speed them up compared to how it could be if they did it personally. 

Benefits of Lending as a Service

So, let's formalize the advantages of fintech lending solutions for businesses.

• Less time and overhead expenses. Companies that do not have the opportunity to pay attention to the implementation of lending can significantly benefit from outsourcing loan management. They will also not be forced to deal with tasks related to maintaining their credit platforms since all this will be the responsibility of their suppliers.
• More information about customers. Any average Lending as a Service platform provides businesses with analytical data about their consumers, which can give them valuable insights into their income level, preferences, and other characteristics important for attracting and retaining them. In this way, businesses can significantly boost their efficiency.
• Fewer risks associated with unreliable customers. Typically, the implementation of a lending service is accompanied by the development and deployment of internal Know-Your-Customer (KYC) policies, which may require businesses to become more familiar with how the lending industry works in general and what laws and rules govern it. Conversely, by entrusting all this to a third-party provider, businesses can gain time to solve more pressing problems.
• Wider coverage of the target audience. The rapid implementation of lending services, which LaaS platforms can provide, allows companies to enter new markets and do this much earlier than their competitors. Thus, they gain an increased competitive advantage without the need for a radical modernization of their internal digital infrastructures.
• Accelerated access to advanced IT achievements in the financial sector. Sometimes such a fintech innovation becomes a starting point for more serious updates in the IT infrastructure of a particular business. Indeed, by regularly interacting with LaaS solutions, companies learn more about new digital technologies and the possibilities of their application in a specific business niche, which, in the long term, can give them new ideas for automation and increase the efficiency of their other business operations.

Impact of LaaS on the Financial Industry

It is worth noting that LaaS has an impact not only on individual businesses that have decided to implement such solutions but also on the financial industry as a whole.

Since they actually connect financial institutions (banks), fintech companies, businesses engaged in sale of services/products, and consumers themselves, this interaction chain opens up colossal potential for benefit for each of its links. Indeed: banks get even more clients, companies involved in the development of fintech startups get useful knowledge that allows them to implement standard-compliant solutions, businesses get increased efficiency and adaptability, and consumers themselves get the opportunity to purchase previously unavailable goods and services.

In the long term, all this can lead to the obsolescence of traditional lending models and the opening of new opportunities for cooperation between all the above-described participants in the lending procedure.

Conclusion

If you are considering the digitalization of financial operations in your business and are looking for a reliable service provider providing fintech development services, feel free to contact us. We will create a custom digital banking solution adapted to the standards and generally accepted regulations of your business niche to ensure your company’s sustainability for many years to come.

In software development, selecting the proper version control and collaboration platform is crucial and can significantly influence a project's success. Today, numerous solutions are available on the market, but two of the most popular choices are GitHub and GitLab. Both platforms offer powerful features for managing code repositories, yet they possess unique characteristics tailored to different needs and project types. The choice of platform depends not only on the development team's convenience and efficiency but also on development speed, product quality, and scalability.

This article compares GitLab vs GitHub features, similarities, and differences to help you determine which option is best in 2024. We will examine both platforms' essential aspects, analyze their strengths and possible shortcomings, and provide recommendations for choosing a platform based on your project's specific requirements.

GitLab vs GitHub: The Basics

Before moving on to the details, it is essential to understand the fundamental aspects of both GitHub and GitLab. Both platforms are designed to simplify the software development process by offering a variety of tools for version control, continuous integration, and collaboration. Let's start with a closer look at each platform.

What Is GitLab?

GitLab is a comprehensive DevSecOps platform and software repository designed to facilitate the entire software development lifecycle, from planning and coding to testing, security, and deployment. 

Launched in 2011 as a simple Git-based version control system, today GitLab is more of an “all-in-one” platform that supports every stage of development. This includes not only DevOps practices such as continuous integration and continuous deployment (CI/CD), but also project management, security testing, and monitoring, making it a robust solution for all phases of software development.​

Here are the main functions of GitLab:

• Comprehensive DevSecOps platform: GitLab integrates development, security, and operations in a single platform, providing a complete toolkit for DevOps workflows.
• Compliance and audit management: GitLab helps manage compliance and audit requirements with automated compliance pipelines, audit logs, and policy management features​.
• Built-in security features: GitLab offers built-in security features like Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), container scanning, dependency scanning, and secrets detection that are integrated directly into CI/CD pipelines.
• Project management tools: GitLab offers extensive project management features such as issue tracking, milestones, task boards, and roadmaps to help organize and track projects.‍
• Remote development and Web IDE: GitLab supports remote development with features like the Web IDE, which allows developers to code, commit, and collaborate directly in the browser.

What Is GitHub?

Established in 2008, GitHub is a web platform that provides GitHub repository hosting, version control, and collaboration tools. Renowned for its user-friendly interface and thriving community, GitHub has emerged as a pivotal platform for open-source projects and individual developers.

The main functions of GitHub include:

• Strong community and emphasis on open source: GitHub is known for its active developer community and many open-source projects.
• GitHub Copilot: GitHub offers an AI-powered code completion tool called GitHub Copilot that provides code suggestions directly in the editor, significantly increasing developer productivity.
• Packages: GitHub integrates package management into the platform, allowing users to host and manage software packages, and use them as dependencies in other projects, all within GitHub.‍
• Security and code scanning: GitHub provides security tools such as Dependabot for automated dependency updates, code and secrets scanning.‍
• Integration with third-party tools: GitHub offers many integrations with popular development tools such as Slack, Trello, etc.

GitLab vs GitHub: Similarities 

Both platforms from our GitLab vs GitHub comparison have a lot in common, making them popular choices for developers:

• Git-based version control: GitHub and GitLab use Git for version control, allowing developers to track code changes and collaborate on projects.
• Collaboration tools: Both platforms provide collaboration features, such as pull requests, merge requests, code review tools, and issue tracking.
• Continuous integration and delivery: GitHub and GitLab support tools for continuous integration and delivery, which helps automate the development process.

Key Difference Between GitHub and GitLab

Despite the similarities, there is critical difference between GitHub and GitLab:

• Hosting: GitLab offers the option of self-hosting, allowing users complete control over their repositories, while GitHub mainly focuses on cloud hosting.
• Pricing: GitLab provides a free version with many features, while GitHub has a more limited free version with advanced features in paid plans.‍
• Open source: GitLab itself is open-source software, allowing anyone to study its code and participate in its development, while GitHub offers some open-source components but is not fully open-source.‍
• UI/UX: GitHub’s interface is known for being simple and user-friendly, making it easy for new users to navigate, especially for open-source projects, while GitLab’s interface is more complex due to its wide range of features, which can make it more difficult for beginners to learn initially.

GitLab vs GitHub: Which Is the Best Option?

The choice between GitHub and GitLab depends on your project's specific needs. GitLab is preferred for teams looking for a comprehensive DevOps platform with built-in CI/CD tools, self-hosting capabilities, and additional features for project management. For example, large enterprise projects that require an integrated approach to continuous integration and deployment will find robust deployment tools and extensive workflow customization options in GitLab. Essential aspects of choosing GitLab are the possibility of self-hosting and integration with other project management systems.

On the other hand, GitHub is ideal for open-source projects, individual developers, service providers and teams who want to take advantage of a large community, a broad ecosystem of integrations with popular third-party tools and ease of use. This makes GitHub an excellent choice for open-source projects where visibility and community engagement are essential and for small or medium-sized teams working on collaborative projects. The main advantages of GitHub include:

• A large community of developers.
• Access to many third-party integrations and plugins.
• A user-friendly interface for collaboration.

When choosing a platform, it's not just about the features; it's about your project. It's essential to carefully consider the specifics of your project, the number of participants, the need for integration with other tools and the requirements for continuous integration and deployment. This thoughtful approach will ensure you make the right choice for your project's success.

GitLab and GitHub Alternatives

In the world of software development, there are many tools to help effectively manage repositories. In addition to the popular GitHub and GitLab, other platforms can be useful for your projects. Consider a few of them:

• Bitbucket: Offers integration with Jira, making it convenient for teams that use this tool for project management. In addition, Bitbucket supports Mercurial, which can be helpful for teams using this version control system.‍
• SourceForge: This site is famous for open-source projects and has an extensive software archive. It allows users to host projects and provides tools for version management and code collaboration, which makes it attractive for open-source developers.‍
• Azure DevOps: This is a comprehensive platform for DevOps from Microsoft that provides a complete set of tools to manage application development, testing and deployment. Azure DevOps integrates with other Azure services, making it an excellent choice for organizations already using Azure infrastructure.

Conclusion

In the world of software development, you are choosing the platform that best meets the needs of your project. GitHub and GitLab offer powerful tools for managing repositories and collaboration, but their unique advantages make them more suitable for different use cases. In addition to these two popular options, it is worth considering other platforms such as Bitbucket, SourceForge and Azure DevOps, each of which has its characteristics and advantages. Consider the specifics of your project, integration requirements, number of participants and other important factors to make the best choice for your project. The deliberate choice of platform will help to increase the team's efficiency, ensure the final product's quality and contribute to the success of your project as a whole.

In 2024 alone, the medical imaging software market size reached $8.11B. By 2029, it is projected to grow to $11.83B and up to 7.84% at a CAGR. This is a fairly predictable trend due to the development of AI. Especially since big data, cloud technologies, and other advancements are already significantly speeding up the accuracy of diagnostics. 

If you are considering custom development of medical image analysis software, now is the most favorable time. Below, we will reveal the specifics of creating such solutions and describe the requirements and the challenges you may face.

What is the definition of medical imaging software?

Medical imaging software — it's the digital tool doctors use to examine medical images. Think X-rays, MRI and CT scans, ultrasounds, PET, and other radiology scans. Basically, it helps to see the details of every complex illness and make informed decisions about patient care.

To maximize efficiency, medical imaging software integrates a range of advanced technologies. These include AI for anomaly detection, ML for image segmentation, and methods for filtering, contrast enhancement, and noise reduction to improve image quality. 

Also, 3D reconstruction technologies create volumetric models of organs and tissues. Developers also rely on the DICOM standard for medical images as it allows seamless transfer. They also use cloud tech to access data, integrated medical records, and VR and AR to visualize data and create interactive interfaces.

As a result, with medical image analysis software, healthcare organizations reduce the workload of their doctors and researchers and minimize the likelihood of misdiagnosis.

Examples of medical imaging software

To better grasp how these solutions work, we suggest you look at several medical imaging software examples that have gained worldwide recognition.

RadiAnt DICOM viewer

It is a high-performance medical imaging software that processes DICOM images. Due to its rich functionality, both doctors and researchers use it in their work. It has smart multimodality tools for 3D and 2D visualization and MPR (multiplanar reconstruction). Moreover, developers made the interface very user-friendly, so this software is also a great choice for users with low technical skills.

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OsiriX MD

Specifically designed for macOS, OsiriX MD is a powerful DICOM platform that meets the needs of radiologists. Its advanced capabilities support 3D and 4D image analysis, hybrid imaging with PET-CT and PET-MRI, and integration with PACS servers. Crucially, it is FDA- and CE-certified for clinical use.

Horos

Horos is a free OsiriX-based DICOM viewer available on macOS. It has rich customization options for analyzing volumetric data, such as 3D reconstruction, and is especially useful for students and researchers. 

GE Healthcare Centricity PACS

GE Healthcare Centricity PACS is a proprietary enterprise medical image analysis software that analyzes medical images. It has EHR and EMR integration, real-time collaboration, advanced AI analysis, DICOM standards, and format support. It can be a full-fledged assistant for doctors and researchers. 

Philips IntelliSpace Portal

Tailored for large clinical institutions, Philips IntelliSpace Portal excels in medical image analysis and visualization. It integrates AI-driven automation and tools for multiparametric imaging in cardiology, neurology, and oncology; this medical imaging software supports multi-user collaboration.

Key features of medical image processing software

This section explores the key functionalities typically found in standard medical imaging software.

Tools for viewing and processing medical images

Ensure your medical imaging software works with various input data (CT scans, MRI scans, X-rays, ultrasounds, and hybrid studies like PET-CT and PET-MRI). Usually, this is done by supporting the DICOM format. In addition, you will need tools to scale, rotate, and adjust image contrast. So, optionally, develop a panel for 3D and 4D visualization, including multiplanar reconstruction.

AI-driven image analysis 

AI is key in automating the detection of anomalies in medical scans. It can identify cancerous tumors, blood clots, and fractures early, with a high degree of independence. Also, AI in your medical imaging software can classify pathologies using trained models. It can segment organs and tissues on scans and analyze multiparametric data.

Diagnostic and treatment planning tools

This includes tools for creating 3D models, surgical planning, and evaluating the effectiveness of treatment. You should also consider integrating your medical imaging software with robotic surgical systems.

Medical data management tools

To implement effective medical data management, you will probably need to integrate your medical imaging software with PACS (for storing and transmitting data), EHRs (for centralized access to personal patient information), and cloud solutions (for unimpeded access to images from anywhere in the world where there is an Internet connection).

Collaboration tools

It's mainly for remote access so doctors and specialists can chat and comment on each other's actions. It also involves integrating telemedicine platforms to discuss complex cases and hold educational seminars.

What types of organizations need medical image analysis software development?

A wide range of organizations can benefit from medical image analysis software development. Now, let's find out which areas of healthcare benefit from medical imaging software the most.

• Cardiology.

In this field, medical imaging software is mostly used to analyze CT and MRI of the heart and angiography. In addition, it monitors treatment effectiveness, plans operations, and predicts cardiovascular disease risks.

• Dentistry.

Inevitable for 3D scanning when planning dental implants, diagnosing jaw diseases, visualizing root canals, etc.

• Oncology.
  Here, medical imaging software detects and classifies tumors, tracks their growth, and assesses treatment effectiveness.

• Neurology.

In this sector, medical image analysis software analyzes brain MRIs and CTs and provides 3D visualizations to assess the spine and nerves.

• Orthopedics.

Orthopedics studies thrive on precise X-ray analysis, which includes 3D joint modeling and spinal disease diagnostics.

• Mammology.

Medical imaging software can detect microcalcifications and early breast cancer through comparative analysis of changes in mammary gland tissue.

• Urology.

In this industry, medical imaging software helps diagnose kidney and bladder diseases. It does this by analyzing CT and ultrasound images. Additionally, the software can help plan operations and monitor patients with chronic diseases.

• Pulmonology.

Industry specialists can use such software to diagnose lung diseases, analyze chest CT data, and assess COVID-19 damage.

• Gynecology.

In most cases, medical image analysis software is used to perform pregnancy ultrasounds. It helps monitor the fetus, find pelvic tumors, and analyze the endometrium and other tissues.

• Traumatology and emergency medicine.

In traumatology, 3D medical imaging software can quickly diagnose fractures and internal injuries. It can also visualize organs for urgent decisions.

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Still, deciding on the right healthcare sector for your medical imaging project? Contact us and discuss the possibilities of its practical implementation with Darly Solutions' experienced developers.

Medical imaging software development: Steps to follow

Custom development must follow clearly defined stages that most teams use. But, it can still be approached in various ways. Below, we outline how healthcare software development services are delivered in our company.

Concept formation 

Start your medical imaging software project with market analysis. Define the target audience, prioritize tasks the software should solve, and research competitors (to identify their strengths and weaknesses). Based on the insights, our medical imaging software development team assesses the functional requirements and evaluates the need for specific technologies to handle image processing. This ensures that the chosen solutions align with the project's technical needs and optimize the processing of healthcare-related images.

Planning

Once we agree on the conditions with all stakeholders, we will write a technical specification for your medical imaging software. This document will describe its functionality, interface, API, security, and integration requirements. We will also approve the tech stack and necessary integrations. Finally, we create a roadmap that defines the milestones and deliverables for each medical imaging software development project stage.

Prototyping

Now that everything is ready, we can begin creating user stories. They include handling DICOM file uploads and 3D models, among other key tasks. For UX/UI best practices of safe data, we follow the WCAG 2.1 guidelines. They ensure accessibility for users with varying technical skills. We also test prototypes with focus groups to see feedback on complex features, which is helpful for future design improvement. Finally, after the edits are done, we develop a full-fledged design.

Coding

The frontend has algorithms to process and analyze medical images. The backend ensures secure data transfer between the medical imaging software and storage. It also encrypts data and protects against vulnerabilities like SQL injections. These involve writing database queries for smooth software interactions and data storage interactions. And last but not least — we also integrate with your healthcare org's existing systems and services (if any).

Testing

Once the code for your medical imaging software is ready and all components have passed unit tests, we run complete test cases. We check for load, functional, non-functional, security, and usability issues.

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Deployment

At this stage, we are choosing hosting for your medical imaging software (usually either cloud or local servers), setting up CI/CD, and training end users, for example, by providing them with documentation, training materials, or live courses. Once we've done it, we deploy the solution (first in the test environment and then — in the actual usage environment).

Support and updates

Finally, after the medical imaging software is deployed, we set up monitoring systems to track its performance and detect errors, fix post-release bugs, optimize it according to user feedback, and add new features if required.

Key tech specifications for medical imaging software development

Such software development can be complex, especially in its early stages. Basically, there is often no clear way to turn an abstract idea into actual requirements. 

So, let's examine all  the key tech specifications that are usually implemented in medical imaging software apps:

• Support for common medical image formats such as DICOM (including DICOM tags for metadata) and standards for storing, transmitting, and processing medical images (such as C-STORE, C-FIND, and C-MOVE).
• Compatibility with various devices (CT, MRI, ultrasound, etc.).
• Image processing can improve images by adjusting contrast brightness and applying filters. It can also segment them to highlight organs and tissues. Lastly, it can register them to compare scans over time.
• 2D and 3D visualization, including volume rendering (CT/MRI), support for iso-sections and reconstructions, and interactivity (e.g., rotation, zoom, and pan).
• Data security, including HIPAA and GDPR compliance, support for TLS (for data transfer) and AES-256 (for image and metadata storage) encryption standards, as well as access control with role-based authorization and two-factor authentication.
• PACS and EHR/EMR integration (e.g., via HL7/FHIR).
• Annotation (adding labels, arrows, and text comments) and providing real-time collaboration tools.
• PDF report generation and image export.
• Scalability (including horizontal scaling via the cloud), multi-threading, and hardware acceleration.
• WCAG 2.1 compliance and user interface customization.
• Logging and monitoring events (including loading, processing, and exporting scans), auditing user access, tracking system performance, and setting up failure notifications.
• Local deployment of software on physical servers (most likely, this will require ensuring compatibility with Linux and Windows OS).
• Setting up regular data backups and automatic recovery after system failures.

Of course, this is just a basic list of specifications. In practice, your project team will expand and refine the list of features while specifying the tools and technologies for the project's unique needs.

Medical imaging software development cost

When it comes to the development cost of medical image analysis software it depends on its complexity and the technologies used. Without data and business needs — it's hard to define the precise price, but on average, basic DICOM (Digital Imaging and Communications in Medicine) typically ranges from $30K to $300K. A customized version of Basic DICOM may cost $30K to $50K. Advanced customizations could cost $70K to $150K.

Implementation costs differ based on the size of the practice:

• Small practices typically cost $5K to $10K and take 1 to 2 weeks.
• Medium facilities cost $20K to $50K and take 1 to 3 months.
• Large enterprises may cost $100K to $200K and take 3 to 6 months.

Please complete this form to calculate the precise budget for your medical imaging software development idea. We will contact you shortly.

Challenges in medical imaging software development

Let's examine the main challenges encountered when developing medical imaging software.

• Regulatory compliance.
  Software handling sensitive data, like patient information, must comply with HIPAA, GDPR, FDA 21 CFR Part 11, and CE Marking regulations. Key security measures include code audits, RBAC, 2FA, and strong encryption (e.g., AES-256, TLS).
  To avoid fines, consult a local lawyer on medical standards.

• Integration with existing systems.
  Integrating PACS, EHRs, and other systems requires DICOM, HL7, and FHIR support. Also, medical organizations have very different established IT infrastructures, which makes it hard to unify their software. If you create a universal solution, you must provide some middleware. It will help users adapt to various services and systems.

• High performance and scalability.

Medical images, especially CT and MRI, are large. This can slow their processing and increase resource needs.
In this regard, you may need to implement lossless compression mechanisms for images and multithreading and parallel data processing algorithms. By the way, a common fix is to move your software to a cloud solution designed for healthcare businesses.

• The complexity of big data management.

Storing and processing massive data, like images and metadata, require a careful choice of databases and storage. In particular, this implies a preference for distributed databases and object storage.
For even greater reliability, do not forget to provide backup and auto-recovery.

• Risks associated with cyber attacks.

Cyber attacks that leak medical data are a serious problem for healthcare software. To solve it, you must implement constant monitoring. Also, set up regular security updates, including patches and OS updates.
Finally, train your staff on social engineering. It can reduce the risks of phishing attacks.
Providing a user-friendly interface.
Interfaces for doctors and medical personnel should be user-friendly and intuitive, requiring minimal technical training to operate efficiently. To achieve this goal, you must test hi-fi prototypes on the real target audience and perform subsequent optimizations. Also, do not forget to ensure your interface is created under the WCAG 2.1 guidelines.

The future of medical imaging software

Medical imaging software development will advance by adopting the newest technologies, process optimization, and increased integration with other medical systems. 

So, here are the core areas in which medical imaging software can be optimized:

• Speeding up diagnostic.
• Increasing image recognition accuracy.
• Costs reduction.
• Improving user experience.

This can be achieved through the implementation and development of the following technologies:

• Artificial intelligence and machine learning.

For highly accurate and automatic analysis of medical images and accelerated diagnostics.

• Cloud computing.

To provide quick access to medical images from anywhere in the world, process large amounts of data without the need to upgrade local infrastructure, and implement remote collaboration between healthcare specialists.

• VR/AR.

Medical imaging software development allows anatomy and pathologies to be studied using interactive 3D models and visualize the patient's anatomy before surgery.

• Quantum computing.

While most quantum computers are not yet available for widespread use, they will speed up processing large datasets and training neural networks for image recognition in a few years.

• Blockchain.

To guarantee the immutability and protection of data from medical imaging software while providing patients with comprehensive control over their medical information.

Our experience in medical imaging software development

This section covers the development of the PrismaORM brain scanner. This platform was crafted for chiropractors, neurologists, and neurosurgeons to monitor brain activity and brainwaves before, during, and after chiropractic treatments.

First, we assembled a team of eight experts to bring this vision to life. They worked closely with two external teams of medical imaging software engineers. We've pointed out a tech stack based on PostgreSQL, Typescript, React Native, Nest.js, Expo, Three.js, and SQLite. This tech of choice lets us build a platform that processes real-time data from brain activity helmets. The BLE protocol transmits this data. A tablet interface visualizes it. A key to the project's success was optimizing the user experience. This included better platform performance and integrating 3D models. 

As a result — we've made a powerful tool that empowers medical professionals to conduct more precise diagnostics and offer more effective treatment recommendations. 

For a detailed look at this project, visit our portfolio.

Wrapping up

Now that you understand the specifics of medical image analysis software development, you can begin searching for a team to bring your project to life. We are a reliable provider of custom healthtech solutions, ensuring a smooth, transparent, and predictable collaboration. Simply fill out the form, and we'll get in touch as soon as possible to discuss your medical imaging software project in detail.

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