Why MVP Development Services Are Perfect for Startups

Hey, are you here to find a solution for a consolidated and easy-to-access “home” for your business information, which is usually transferred from different places, often unrelated to each other? Then, you probably need to think about creating a digital space where it will be integrated, cleaned, structured, and stored accordingly, for further use in your regular business operations. But what is this space? Let’s find it out right now.

What Is Data Warehousing?

In a nutshell, it is a repository where your business data comes from disparate sources, where it is brought to the proper quality, and where it is stored, providing individuals with the appropriate rights with easy and fast access to it. Typically, such repositories serve as one of the central components in automated analytical solutions, but, as practice shows, the range of their application can be much wider.

Key Components of a Data Warehouse

In general, its main components are considered to be the following:

• Sources, from which structured and unstructured information comes – these can be third-party databases, tables, systems, applications, etc.;
• Data Extraction, Transformation, and Loading (ETL) tools, which define scenarios for extracting, transforming, and loading the information;
• Data warehouse database, which is the main repository (usually in the form of a database management system, DBMS) with already prepared, i.e., cleaned and structured data, that can be used for analytics, reporting, and other business tasks without additional manipulation;
• Metadata repository, which includes the permissible data types and the rules according to which this data will be used;
• Query and reporting tools, which define algorithms for fast and unified access to data, as well as its analysis;
• Data mart, a subset of the data warehouse that is used for individual business tasks (this can imply, for example, marketing data warehouse design) which formation occurs either directly through the collection from disparate sources or pre-preparing within a consolidated space.

Core Principles of Data Warehouse Design

Now, let's talk about the main data warehouse design principles:

• A clear definition of business requirements and goals, as well as metrics that allow you to objectively assess the degree of their achievement;
• Understanding how information is integrated from multiple sources and what should be the unified format after its transformation;
• Choosing the right type of data modeling – these can be star schemas, snowflake schemas, fact tables, and so on;
• Choosing the right methods and data warehouse software to ensure the quality and consistency of information, in particular, the methods of cleansing, validation, and supplementing of data;
• Planning, scaling, and managing the performance through various methods of indexing, partitioning, compression, etc.;
• Choosing the right tools for metadata management to simplify and improve access to the information;
• Ensuring security and access based on policies and rules (usually, this is achieved through the implementation of advanced encryption algorithms, the introduction of access policies for different user groups, as well as providing reliable authentication and authorization methods);
• Implementing end-to-end monitoring for ensuring data quality and security, performance of processes occurring within the data warehouse, and so on.

Steps in Data Warehouse Design

In this section, we invite you to consider the key steps leading to a successful and agile data warehouse design.

Requirement Gathering

According to the principles to design a data warehouse we defined above, the key to effective data warehouse design is to gather business requirements and clear business goals that it should fulfill. It is also important to identify the requirements for security, scalability, and performance of the repository.

Data Modeling

This is not yet a practical stage, but it still requires the participation of specialists – in particular, it implies the identification of entities, their attributes, as well as possible relationships between them. After this, the most suitable type of DBMS implementation needs to be selected – for example, in the form of tables, columns, indexing, etc. After this, you have to make sure that the selected type performs all the tasks assigned to it according to the predetermined requirements.

ETL Process Design

We have already explained above what the ETL process means, and this stage actually involves choosing the right tools and scenarios for their use.

In particular, you will need to define methods for extracting information from disparate sources (these can be database queries, API calls, file transfers, etc.), methods for bringing data to a single format and ensuring its proper quality (since you will most likely have to deal with big data, there may be many inconsistencies), methods for aggregating data to create complex information structures, automation methods for bulk loading of data (this can be full or incremental loading, and so on) with the usage of temporary storage areas, methods for detecting and eliminating data errors, as well as methods for checking data for completeness and accuracy.

Database Schema Design

There are several well-known data warehouse design patterns, such as:

• Star, which has a fact table at its center and dimension tables associated with it around it;
• Snowflake, which is a more complex Star and also implies additional dimension tables that surround each base dimension table;
• Galaxy, which contains two fact tables and the dimension tables between them.

Data Integration

Now, you can start integrating the data using the previously defined ETL tools and technologies. At this step, you need to make sure that everything works as intended, and the data is transformed into the required unified format.

Data Storage Solutions

At this stage, you need to choose specific data storage solutions according to your requirements for the expected data volume, performance, scalability, and cost. Usually, the choice is made between relational databases, columnar databases, data lakes, and cloud data warehouse solutions.

Data Presentation Layer

Finally, to design data warehouse, you will need to understand what your data presentation layer will be – the layer at which end users will be able to seamlessly access the data and use it to solve specific business problems. This includes developing interfaces, dashboards, reports, and various data visualization tools.

Conclusion

Now that you have a clear guide to data warehouse design, you can begin implementing it with a full understanding of the principles and stages on which it is based. If you would like to delegate this comprehensive task to seasoned data warehouse development specialists, just contact us.

The implementation of artificial intelligence (AI) and machine learning (ML) has the potential to turn the healthcare industry upside down. And this global transformation is already beginning. Innovative technologies are being used in diagnostics and treatment. They are influencing pharmaceuticals, research work, and other aspects of medical practice.

Analysts at Global Market Insights have calculated that the AI market in healthcare is valued at $18.7 billion. However, by 2032, the situation will change. According to forecasts, its size will exceed $317 billion.

Considering this trend, we find it appropriate to explore the prospects that AI and the technologies based on it offer to medical professionals worldwide. We will also focus on the near future of AI-based medical solutions and discuss examples of digital products that are already available to specialists.

What Are Machine Learning Healthcare Projects?

Machine learning projects in the medical field involve the use of ML algorithms and models to perform various tasks for healthcare workers. These include:

• data collection and analysis;
• predicting the condition of patients and treatment outcomes;
• making decisions about the appropriateness of certain prescriptions, and more.

The effectiveness of using AI and ML in healthcare is confirmed by numerous specialized studies. Below are some examples:

• The Houston Method Research Institute used an AI-based tool for diagnostic purposes. Specifically, it was used to detect malignant breast tumors. The results shocked scientists. The system demonstrated a disease detection accuracy approaching 99%. Moreover, AI was able to analyze mammograms 30 times faster than humans.

• The Nationwide Korean Cohort Study focused on researching the effectiveness of predicting infectious disease outcomes using machine learning. The results showed that the accuracy of the applied ML model was over 90%.

• Scientists from Indiana University and Purdue University in Indianapolis developed an ML-powered system to predict the outcome of acute myeloid leukemia. The results of the model's tests demonstrated that it could predict remission with 100% accuracy. The likelihood of recurrence was correctly identified in 90% of cases.

• Researchers at BMJ Health & Care Informatics developed and tested a machine learning model for detecting NASH (non-alcoholic steatohepatitis). This disease is difficult to diagnose. However, the use of ML contributes to more accurate screening of NASH and allows for the timely application of effective treatments.

All of these statistics confirm the vast potential of machine learning in healthcare projects. So, what is this technology most often used for?

Popular Use Cases of AI Projects in Healthcare

According to Morgan Stanley Research, 94% of respondents (medical companies) reported using AI or ML in their practice.

Let’s take a look at which aspects of healthcare these technologies can optimize.

1. Pathology Detection

Machine learning models can be used to analyze various types of images. The goal is to recognize pathological conditions.

For example, they can be used to diagnose cancer, tuberculosis, and other infections.

How does this work in practice? Here are several examples of using ML in the field of pathology detection:

• A machine learning-based program analyzes tissue samples and helps identify malignant growths.
• ML algorithms recognize tuberculosis by examining sputum samples from an infected person.
• ML-based systems help differentiate between benign and malignant tumors.

An example of such software is AIM-PD-L1: an AI and ML-based tool that detects PD-L1. This protein is formed as a result of cancer cell development. Research on this solution showed that its results closely align with human diagnostics. Thus, the tool can be useful in identifying pathology.

Another solution from the same developer, PathAI, is the PathExplore panel for histopathological features. It can analyze tissue images at the pixel level. This allows it to examine millions of cells and accurately identify malignant ones.

2. Medical Imaging and Diagnostics

Healthcare machine-learning projects often use innovative technologies to analyze various images. These include CT scans, MRIs, mammograms, X-rays, and more.

Here’s how machine learning models can be used in diagnostics based on visual data:

• An ML-based system can analyze CT scan results and, with a high degree of accuracy, identify malignant tumors.
• Similarly, such platforms can diagnose ischemic strokes, such as hemorrhage, based on brain MRIs.
• Specialized ML-based tools allow for retina scans. This helps in the early detection of blindness due to diabetes, specifically diabetic retinopathy.

There are many similar solutions available on the market. Among them are Viz.ai and InnerEye.

The first, Viz.ai, is a care coordination system based on AI. It allows for automate detection of many diseases, including vascular pathologies.

The second, InnerEye,  is a solution from Microsoft that significantly speeds up work with patient images. For example, with this platform, doctors can accurately differentiate between healthy and cancerous cells. It also accelerates the start of treatment by 13 times.

3. Personalized Medicine

Each patient is unique due to the following characteristics:

• genetic predisposition;
• medical history;
• lifestyle;
• comorbid health issues, and more.

Machine learning-based platforms are able to take all these factors into account when selecting treatment methods.

Here’s how this can work in practice:

• ML models analyze genomic data and recommend individualized treatment methods for oncological diseases.
• Thanks to predictive analytics, AI-based tools can adjust insulin dosage. This is possible through constant monitoring of its level in the patient.
• Machine learning algorithms help choose the optimal antidepressant based on the patient's psychological state and genetic predispositions.

As an example, let's look at IBM Watson for Oncology. This is a machine learning-based system that considers many factors when recommending cancer treatments. These include the compatibility of prescribed medications, possible side effects, and the effectiveness of treatment in each specific case. As a result, the system provides healthcare professionals with several therapy options from which they can choose the most optimal.

4. Drug Discovery Process

The use of ML can accelerate the invention of new pharmaceuticals. Specialized systems show excellent results in discovering the medicinal properties of various substances. They can also analyze the interactions of drug components and optimize clinical trials.

AI in healthcare projects can be used for this purpose as follows:

• ML models accelerate the search for antibiotics and antiviral drugs.
• AI-based modeling helps reduce the number of failed clinical trials, thus saving time and resources.
• Machine learning algorithms can analyze the structure of substances, assisting in the development of new drugs.

A notable example of using AI and ML in creating new drugs is AlphaFold by DeepMind. This tool is designed to determine the structure of proteins, which are the foundation of biological processes in every living organism. Currently, the platform is successfully used for:

• fighting malaria;
• searching for a treatment for Parkinson's disease;
• counteracting bacteria resistant to existing drugs.

5. Epidemic Prediction

One of the capabilities of ML models is analyzing patterns and making predictions about outbreaks of various diseases.

Here are some examples of using ML-powered solutions:

• Such systems can predict global pandemics. To do this, they analyze data on population movement and disease statistics.
• ML algorithms track seasonal disease spikes, such as the flu.
• “Smart” platforms can predict outbreaks of specific diseases in high-risk areas. For example, they can forecast cases of malaria or dengue fever.

EPIWATCH is one of the leading examples of such platforms. This tool uses artificial intelligence to predict epidemics that could reach a global scale. The system collects and processes extensive infectious disease data in real time. It has proven effective in providing early warnings about potential pandemics.

6. Remote Patient Monitoring

In recent years, IoT (Internet of Things) devices have become widely used in medicine. These are objects equipped with sensors that can collect and transmit data wirelessly without human intervention.

According to Yahoo!Finance, the IoT market in healthcare, was valued at $58.8 billion in 2024, and by 2032, it is expected to exceed $305.5 billion.

This situation is explained by the fact that AI and ML enhance the efficiency of wearable devices and health monitoring systems in general.

The use of ML in remote monitoring includes the following cases:

• Smartwatches can track irregular heart rhythms and detect instances of sleep apnea.
• ML models can analyze ECG data in real time. Based on the obtained data, they can predict a heart attack.
• AI-based inhalers can track the patient’s readings and warn about an asthma attack.

There are many such solutions on the market. These include popular devices like the Apple Watch and Fitbit. These smart bracelets can monitor heart rhythms and detect uncontrolled breathing stops.

There are also more complex systems available to healthcare professionals. For example, SafeBeing from Somatix. Thanks to wearable devices, this platform responds to patient gestures and draws conclusions about their health condition.

7. Virtual Assistants

Not everyone can visit a medical facility in a timely manner. Moreover, many people need assistance and support after being discharged from the hospital. AI handles these challenges effectively. Developers are actively creating digital assistants with integrated ML algorithms. These assistants are capable of providing first-aid and consulting patients after treatment.

What can such tools do?

• These platforms help assess health status independently and suggest possible diseases.
• Smart chatbots help schedule doctor visits, remind patients to take medications, and more.
• Virtual assistants help monitor the progress of chronic diseases.

There are many such solutions available to patients. Let’s look at the features of Ada Health as an example. This platform helps assess symptoms and provide preliminary diagnoses using machine learning algorithms. It can:

• track changes in the body over time;
• provide recommendations regarding treatment and a healthy lifestyle.

8. Psychiatric Assistance

ML algorithms help diagnose and treat various psychiatric disorders, including those used in the fight against depression and anxiety.

Practical examples of ML usage in psychiatry:

• Intelligent chatbots can mimic real-world conversations. They can answer patients' questions and provide recommendations.
• ML models can analyze user behavior on social media and detect suicidal tendencies.
• These digital solutions can analyze human speech and identify early signs of Alzheimer’s disease.

Woebot is a smart chatbot that helps people with mental health disorders. It can assess a user’s mood and emotions. Among its functions are conducting conversations with patients and providing personalized strategies for coping with difficulties.

9. Robotic Surgery

Just imagine. Surgeons no longer need to be in the operating room. All procedures are performed by a robot. Sounds like a scene from a sci-fi movie? Nevertheless, this is our reality. Developers have already created robotic systems based on AI that enhance the accuracy of surgical interventions and reduce rehabilitation time.

What can such software do?

• Smart surgical systems can perform minimally invasive surgeries.
• AI-based software shows excellent results in neurosurgery and orthopedics.
• ML models predict potential surgical risks and possible complications.

Here, we cannot fail to mention the famous Da Vinci surgical robot created by Intuitive Surgical. This robotic system offers medical professionals incredible possibilities in the field of minimally invasive surgery. Its features include fluorescence visualization, integrated table movement, a vision cart, and more. This allows for precise and effective surgical interventions.

10. Automation of Administrative Tasks

Routine tasks such as organizing document flow or billing require a lot of time, diverting medical staff from their primary duties — treating patients. The implementation of machine learning-based software allows automation of these processes, reducing the time spent by medical personnel.

AI-powered systems are handling numerous administrative tasks:

• Platforms based on NLP can extract data from medical records and update patient electronic charts.
• ML algorithms analyze insurance cases, minimizing fraudulent claims.
• Smart scheduling systems efficiently manage hospital resources.

An example of such software is the Optum system. It offers numerous solutions for the healthcare business, including a platform for Revenue Cycle Management. Its capabilities include billing, increasing revenue efficiency, medical coding, and documentation.

Benefits of Machine Learning for Healthcare Analytics Projects

The use of ML models in healthcare allows for the analysis of large amounts of medical data. Furthermore, such models are continuously trained and improved. The implementation of tools based on these models brings a number of benefits to both medical professionals and patients:

Early Disease Diagnosis

Identifying serious diseases at early stages reduces mortality, improves the quality of life for patients, and increases the likelihood of full recovery or sustained remission. For medical institutions, this helps reduce their expenses and saves time.

ML models excel at early diagnosis. They analyze medical images, test results, and patient medical histories. They then track patterns and detect even the slightest deviations from the norm.

Improvement in Patient Care Quality

This is facilitated by AI-based digital solutions for personalized care, virtual assistants, and chatbots. They help maintain a person's health outside of the hospital, for instance, after discharge or before visiting a specialist.

At the same time, such platforms help reduce the workload on medical institutions. They can provide the patient with an initial consultation and allow medical professionals to remotely monitor the patient's condition.

Development of the Pharmaceutical Industry

Innovative technologies allow for faster development of new medications. This is a step toward effectively combating various diseases. This is particularly relevant during global epidemics. 

Cost Reduction

ML automates many processes in healthcare, from documentation management in clinics to diagnostics and treatment prescriptions. Additionally, the use of smart technologies helps reduce human errors and allows for some patients to switch to home care. As a result, medical institutions can increase the number of patients served and their profits.

Improvement in Treatment Effectiveness

In medicine, predictive models are used, which are trained on large sets of historical patient data. Such models can predict:

• the likelihood of recurrence;
• the need for hospitalization;
• the tendency for the disease to progress.

Medical professionals can use all this data to adjust the prescribed treatment to improve its effectiveness.

Key Challenges in Healthcare Machine Learning Projects

The implementation of machine learning in healthcare projects opens up many opportunities for the industry. We have already seen this today. However, alongside numerous advantages, some challenges may arise for innovators.

Security of Confidential Data

The digitalization of healthcare has led to a particularly sharp issue. We mean the preservation of medical confidentiality and the privacy of patient data. The use of virtual tools implies entering personal information into the system, which increases the risk of data leakage.

To mitigate these risks, maximum anonymity should be maintained. For example, financial and insurance data, as well as the patient's address, should not be included. It is also important to implement reliable data encryption methods and threat notification mechanisms on the platforms.

Quality of Data for Model Training

The effectiveness of ML models depends on the data input into the system. Inaccurate input data can lead to incorrect diagnoses, wrong treatments, and even death.

To prevent this problem, machine learning software should be used under the supervision of highly qualified medical staff.

Skepticism of Healthcare Professionals and Patients

Since machine learning projects in the medical field are still in the early stages of gaining ground, many doctors and patients are not fully confident in its capabilities. The main concerns include inaccurate diagnostics and the complete replacement of human involvement in medical practice.

To build trust, continuous training for healthcare professionals and patients is necessary. It is also important to notify people about the chosen treatment methods.

As for the complete replacement of human labor, this is highly questionable. Scientists believe that innovative technology will, in fact, expand the role of healthcare professionals. Their duties will include overseeing the operation of ML models and interpreting their results.

Difficulty of Integration with Existing Software

To ensure the smooth operation of medical institutions, new software products need to be seamlessly integrated into the existing infrastructure. The task becomes more complex because clinics may be using outdated systems that are not compatible with new technologies.

To solve this problem, investment in qualified IT personnel and gradual replacement of old software with new ones is necessary.

Technologies Used in Machine Learning Projects in Healthcare

Such high efficiency in AI projects in healthcare is achieved thanks to the wide range of technologies used in specialized software. Let's look at the main ones.

Computer Vision

This is the ability of a machine to understand and interpret visual information from the external world. In healthcare, it is used for analyzing medical images for subsequent diagnosis and treatment.

Examples of use:

• detection of tumors in X-rays, CT scans, or MRIs;
• analysis of biopsy results to identify malignant formations.

Robotic Process Automation (RPA)

This involves using bots (software robots) to perform repetitive tasks. In medicine, this technology is applied to automate administrative processes.

Examples of use:

• processing insurance claims;
• billing;
• inputting data into electronic patient records.

Logistic Regression

This is a statistical machine learning algorithm that is effective for predicting the probability of disease and classifying ailments.

Examples of use:

• assessing the risk of developing diabetes;
• determining the likelihood of cardiovascular diseases.

Support Vector Machines (SVM)

This is a supervised machine learning algorithm that is best suited for classification, i.e., separating data into different categories. It is used in medicine for diagnostic purposes.

Examples of use:

• differentiating between benign and malignant tumor cells based on biopsy data;
• classifying genes to identify genetic disorders.

Natural Language Processing (NLP)

This is a field of artificial intelligence that enables computers to understand, interpret, and generate human language. It is used to process medical text, such as doctor’s notes or specialized literature.

Examples of use:

• analyzing electronic medical records to extract patient data;
• virtual assistants and chatbots to assist patients outside medical facilities.

Large Language Models (LLM)

These are a specific type of machine learning models trained on large datasets. Their capabilities include understanding and generating human-like language.

Examples of use:

• answering complex medical questions;
• automating clinical documentation.

Deep Learning (DL)

A subset of machine learning that uses neural networks with multiple layers to perform tasks. It is applicable for image recognition and speech processing. It is widely used in medical imaging, as well as in fields such as genomics and pharmaceuticals.

Examples of use:

• developing new medications;
• diagnosing diseases based on visual data.

Reinforcement Learning (RL)

A type of machine learning where an agent learns by interacting with its environment, in other words, through trial and error. This technology helps optimize the effectiveness of treatment strategies.

Examples of use:

• robotic surgical systems;
• personalized treatment for various diseases.

The powerful potential of AI-based technologies suggests that the future of healthcare lies in them.

Future Trends in AI and Machine Learning in Healthcare Projects

It is predicted that in the coming years, AI and machine learning will continue to strengthen their position in healthcare. This is facilitated by:

• better awareness of these technologies among doctors and patients;
• an increase in the number of developers capable of creating and integrating innovative systems;
• a growing demand for skilled healthcare services and medications.

What can we expect in the near future?

Transformation of Primary Healthcare

AI and machine learning systems will take on more responsibility during a patient's initial visit to the doctor. They will classify patients based on their risk level, using examination results. Patients with a low probability of illness will be notified immediately. Those with a high risk will be referred to specialists.

This will reduce the waiting time for referrals and ease the burden on specialists.

Improvement of Healthcare Accessibility

According to the International Labour Organization, 56% of people living in rural areas have no access to healthcare. In cities, the situation is better but still concerning, with 22% of the population unable to access medical services.

AI and machine learning have the potential to fundamentally change this situation. This will be possible through telemedicine and mobile health services.

Focus on Data Security

AI system developers understand the importance of ensuring data security in healthcare. The HIPAA Journal states that in 2023, over 133 million medical records were exposed due to data breaches. This means the problem is becoming global in scale.

As a result, IT specialists will work on enhancing the reliability of the security measures being implemented.

Progress in Early Disease Detection

Modern intelligent software is capable of recognizing symptoms at the very early stages of disease development. However, in the near future, AI capabilities will go even further. The first steps in this direction are already being taken. An example is the Sybil tool developed by scientists from the Massachusetts Institute of Technology. It can predict the development of lung cancer within a year with 94% accuracy.

This is a huge breakthrough in healthcare. It has the potential to save millions of lives.

Emphasis on Personalized Treatment Plans

Doctors will increasingly move away from general prescriptions that do not account for the specific characteristics of individual patients. AI systems will be used to analyze medical histories, genetics, and other factors. Based on this analysis, personalized treatment methods will be selected.

This approach is particularly relevant for treating complex diseases such as cancer.

Darly Solutions Case Studies

Finally, we would like to share the experience of the Darly Solutions team. Our custom healthcare software development company has implemented many successful AI-powered projects for healthcare.

A company approached us with concerns about patient safety during the diagnosis of infectious diseases. They decided to rely on AI and machine learning for the accuracy and safety of their research.

The Darly Solutions development team created a platform for the client that can achieve 99.9% accuracy in clinical diagnostics. To achieve such impressive results, we used the following technology stack:

• Python for machine learning and data analytics;
• JavaScript and React for creating an intuitive user interface;
• WordPress for backend development.

We successfully implemented predictive tools, high-quality data visualization, and a user-centered UI/UX design in the diagnostic solution. As a result of our collaboration, the client’s goals were met. The company received a flexible, scalable, and highly efficient system.

Conclusion

It can be confidently stated that machine learning projects in the medical field are the future of global healthcare. Intelligent solutions have the potential to revolutionize disease diagnosis and treatment, patient care, and drug development.

Do you want to follow the latest trends and provide the highest quality services? Now is the perfect time to find an expert team to develop an AI and machine learning-based medical application.

The Darly Solutions team is ready to assist you with this! Contact our manager to discuss the project details and receive a digital solution that will exceed all your expectations.

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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