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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.

There are about 8.93 million mobile apps worldwide, with new ones popping up daily, but not each of them has a good mobile interface design. At the same time, those who have it, win the hearts of customers and earn millions of dollars. So, how can you achieve this goal? Let’s find it out right now.

What Is Mobile App Design?

Mobile app design is the visual identity of an app tailored to the user's behavior. It has two components: UI and UX. UI (user interface) is how the application looks, and what colors, fonts, icons, buttons, or other elements it contains. UX is how an app works, responds to user actions, and helps fulfill people’s needs.

Interface design for a mobile application calls for consideration of the specifics of mobile platforms, such as screen size, data input methods, battery level, and internet connection speed. You should bear in mind that design can have a big impact on the success and popularity of the product, its usability, and its attraction to users.

Key Principles of Mobile App Design

Creating the interface of mobile applications is a truly complex and multi-layered process. It can vary dramatically depending on what project the app is created for, who the target audience is, and what functionality its owner is looking for. However, there are a few fundamentals to keep in mind when designing.

Here are top basic mobile design principles:

1. Prioritize the user’s needs 
2. Focus on visual design
3. Make the app interface convenient
4. Make the content easy to read
5. Add some interaction
6. Optimize the interface for different mobile devices

User Interface (UI) Design Principles

Let’s define the most important user interface design principles.

Simplicity

An eye-catching and straightforward user interface is ideal. There shouldn't be too many interface elements that can confuse a user. Nobody wants to explore a new interface once an application is downloaded—instead, people just want to get what they need or have fun.  All they seek is simple products that “just work”, that’s why, you have to prioritize the main parts for design and remove the unnecessary ones.

There is a simple rule of thumb: one main action per screen. Each screen you design in your application should support a single, genuinely important action for the user. This makes it easier to learn and use. One hundred clear screens are better than a single cluttered one.

Consistency 

The interface has to be intuitive and easy to navigate. You can add a great feature or content, but if people can't find it, it doesn't matter. Mobile navigation should be consistent. To ensure navigation doesn’t require any explanation, you should use the right visual metaphors, and check that each navigation item leads to the appropriate place.

Feedback and Response Time

Feedback informs users whether they are doing the proper action or not. It can be audible (the ding of a new message notification), tactile (the vibrating signal for a new email or call while your phone is set to "silent"), or just visual. Feedback should be provided after each operation to show if it was successful. 

Feedback can be useful in responding to problems in four areas:

1. Locating: Where are you?
2. Current Situation: What's happening? Is it still going on?
3. Prospects: What comes next?
4. Effects: What did you get?

Quick, insightful, and observable feedback is essential to let people know their actions were noticed. The link between the activity and its effect should be clear so that users understand what happened, and what to expect next. Feedback is supposed to optimize and complement the user's experience rather than complicate it.

User Experience (UX) Design Principles

Now, we propose to list the key UX design principles.

Usability

Usability measures how easy and convenient it is for the user to interact with the interface of an application. It depends on the ease of navigation, structure, content quality, text layout, mobile app design, and a bunch of technical features. This aspect affects users' time on an app, conversion rates, and customer loyalty. 

Here are five components of usability:

1. Learnability: how easy is it for people to perform basic tasks the first time?
2. Productivity: how quickly can people complete tasks?
3. Memorability: when people come back to an application after a time out, how easily can they recover their skills?
4. Error recovery: how many errors do users make, how serious are they, and how easily can people fix them?
5. Enjoyment: How engaging is it to use the app? Does it help people accomplish their tasks? 

User-Centric Design

User-centered design is an approach that consistently identifies user needs and translates them into a product. The core value is to focus on what people are used to, on what is “easy” for them and meets their basic needs.

Figure out what users want from your mobile application. On a food delivery app, people want to see available propositions, and discounts, place an order, track a delivery, and that’s it. They don’t want to read the history of a local family restaurant and its owners—this information may be kept in subsections. 

Accessibility 

If you want people to use your app, make it easy to reach. First, make sure it’s available on all mobile platforms and devices. Secondly, remember about inclusivity while designing for mobile devices. Partially blind or colorblind people may also use your product. So, you should help them effortlessly use it by adding voice interaction, and alternative text for images and employing color-blind oriented templates. Additionally, let customers change the text size in the app to suit their visual requirements.

Best Practices in Mobile App Design

Creating an interface that is both attractive and user-friendly is a challenge and an art. To achieve a delicate balance between aesthetics and functionality, designers need to utilize best practices. 

User Interface and Navigation 

Navigation in a mobile application must be intuitive. Each button or link has to state its purpose clearly. All icons must be recognizable and standardized. 

Mobile UX design's best practice for icons is to tag them with text. Tagged icons ensure that the purpose is conveyed quickly and easily.  You can see this in the most popular apps like YouTube, Spotify, and TikTok. 

Content Organization and Data Entry

When directing user attention across the application interface, use visual hierarchy. Distinguish important elements from less significant ones by varying size and spacing. Features that stand out attract attention, and clustering related items makes them easier to understand. For better readability and differentiation between titles, subheadings, and body content, designers should use the proper font sizes and styles. 

As for good examples of great content organization, we’d like to pay attention to Airbnb, an illustration of minimalism and simplicity. People love it for its aesthetic graphic design, balanced font sizes and colors, and the absence of flashy elements or bright banners. Airbnb has also made booking real estate clear: users understand what they are paying for.

Input fields and forms are the basic parts of mobile apps and are also crucial in mobile design principles. Improving the total user experience requires making these elements efficient and usable. Make a user type as little as possible by utilizing default values and input masks. Give consumers immediate feedback on validation so they can fix mistakes quickly. To help users fill out forms correctly, provide them with informative error warnings and visual clues.

Usability and Accessibility 

If you choose the right color palette, you can greatly improve the user experience. Remember about color blindness and test how your app looks to users with different types of color perception. Use contrasting colors for text and background to make content easy to read for anyone and anywhere. Add some extra explanations if needed. 

For example, the choice of clothing color in an online store should have a text explanation to be understood by people with a violation of color perception. Also, consider how the interface will look on small screens.

Performance and Responsiveness 

Take into account a diverse range of screen sizes and resolutions. Your design should be adaptive to ensure perfect display on any device. To check adaptability, test your design on a tablet, smartphone, and computer: this way you can adjust the display of elements in the interface and understand what and where to improve. Make sure the elements are not overlapping and are scaled correctly.

Platform Conformance

Research iOS and Android operating system guidelines. A guideline is a set of rules that determine how mobile apps are designed within an operating system. The iOS and Android systems have recognizable elements that users expect to see, so the guidelines help to create optimal UX and UI. Also, it will speed up the development process and reduce bugs.

Tools for Designing for Mobile Devices

As for the most popular tools designers use to implement solutions for mobile devices, we can consider the next four.

Figma 

Figma is an application for all kinds of graphical works: from creating website layouts to developing mobile interfaces and prototyping. Its biggest advantage is an opportunity to work directly in a browser, allowing access to projects from any computer or platform. You don’t have to buy multiple licenses or install the software. Another plus of the application is the collaboration feature that allows simultaneous changes to the project design by several users without the need to upload files locally.

Adobe XD

XD is designed from scratch to meet the requirements of modern UX/UI design with many features not available in other graphic applications. It solves the main problem that competitors can't cope with: it provides interaction with non-static elements and allows you to implement thoughtful dynamics on the page. Adobe XD is ideal for vector UI design, wireframe modeling, implementing engagement with interactive elements, and prototyping.

Sketch

Sketch is a simple vector tool. Designers and front-end developers use it to create UX/UI components. Sketch is simple and easy to learn, has an intuitive interface, and offers cross-platform design tools. It has a preview feature to see how the project will look on different devices. It has many good plugins and resources, plus some useful functions for working in the cloud.

InVision

InVision is a versatile design tool that focuses on maximizing UX for apps. It can be used to create automatically customized interactive prototypes for different devices. InVision Studio has tools for working with vector drawing, interactive design, and built-in animation. The application can be installed on macOS and Windows.

Mobile Application Design Process

UI UX mobile app design is a complex process that requires a thorough approach to ensure it is visually appealing and compatible with various devices and operating systems. You also need to make sure it’s handy for both experienced users and newcomers and meets modern trends in app design. So, what are the stages of mobile design?

1. Gathering data and design requirements. Research is a crucial part of the design effort, guiding the development of a product concept that accurately defines the market niche, functionality, monetization, and usage scenarios. 
2. Planning the architecture and navigation of the application. UX specialists create architecture based on application usage scenarios, ensuring navigation and consistency. This design stage is crucial for the end-user, as it directly affects the app's navigation and overall user experience.
3. Development of prototypes. Mockups are created for prototyping a mobile application, allowing for easy modification and identification of best solutions. This process involves discussing the architecture with the team and client, and later involving a team of developers to evaluate the technical design.
4. Testing the prototype. Interactive prototypes allow testing the app design to ensure it is suitable for the target audience, allowing for detailed feedback and understanding of user behavior patterns. This stage of development helps identify non-obvious patterns and improves the product's overall functionality.‍
5. Final refinements and development. Next, the agreed prototype is handed over to designers to make the product more attractive, refine the styling, and improve small user interactions, taking into account the final UI of a product. The final design is passed to developers who do the technical part.

Get Some Inspiration with Our Case Studies

Below, we invite you to check out some of our projects where we have successfully implemented all the ideas described above:

• Booking beauty appointment platform
• Takeout food delivery app
• Social network
• Advertising placement system
• Vehicle rental mobile app
• Smart city mobile app

Conclusion

Creating an aesthetically pleasing design is not enough. You also need to make it user-friendly and inclusive, bearing in mind mobile device demands.  Your target audience will be captivated and delighted by the appealing, highly engaging, and intuitive mobile applications if you carefully adhere to mobile app design principles and never stop learning from real-world examples of great mobile applications.

The first question that you should ask yourself before contacting the IT outsourcing company is “what billing model is the best fit for my project?”. If you are confused about choosing an outsourcing model from the Dedicated team vs. Fixed price vs. Time and Materials for your business idea, then welcome aboard!

In this article, we will compare these business models for your project. Let’s analyze each in detail and describe the advantages and disadvantages of 3 main IT outsourcing models.

Fixed price

The fixed-price agreement is a single-sum contract where the software development team must deliver the project within a predefined sum which is agreed by both parties. A client should be able to share his clear vision of the product with developers to ensure appropriate final results. This model requires the client to provide a detailed list of requirements for the project and all required developer documentation.

Pros of fixed price model:

• Clarity. Project requirements are agreed upon before signing the software development contract, as well as all the required developer documentation is specified beforehand.
• Accuracy. The project scope and time frames are known and discussed in advance, so the team can set the deadlines, and it is convenient to monitor the project progress.
• Autonomy. The development process is coordinated by a project manager, so the client’s participation isn’t necessary.

Cons of fixed price model:

• Preparation. The period is needed as all requirements for implementation and deadlines must be documented.
• Inflexibility. Any change in the scope of work would cause a change in the total cost of the project and this change would be compensated by the service provider’s end.
• Risks. If you don’t provide your requirements correctly, then the delivered product won’t be the same as expected.

When is recommended to use a fixed price model:

• Clear requirements and determined deadlines.
• Limited or fixed budget.
• MVP.
• Small and medium projects with limited project scope of work.

Time and Materials

Time and materials contract is absolutely different. This model is great suited for long-term projects with changing requirements, where the final requirements are not known in advance and can be changed on the go. This is a flexible approach to IT outsourcing, as the customer pays in smaller chunks for work accomplished during the billable hours. To say more, this pay-as-you-go model allows you to pay only for the work actually done. For example, for features delivered, milestones, etc.

Pros of Time and Materials model:

• A flexible approach. The customer can change the scope of work, requirements or add/drop features on the go to meet his business aims without thinking about the budget. This model perfectly combines with modern agile methodologies, for example with Scrum.
• Transparency. This model allows the customer to control the cost and concentrate on important elements of the project. The client pays only for the results actually delivered.
• Involvement. The client can monitor the process of the development of a certain work schedule in accordance with the expected results.

Cons of Time and Materials model:

• Budget and time. It is not always possible to estimate the budget in advance. It’s hard to predict when the product will be delivered or how much it will cost. This depends on the accuracy of the specifications or its changes in the process of work;
• Communications. To achieve a high-quality product, continuous communication with the team is necessary. It requires full-fledged involvement from your side, which includes spending much time in communication with the development team.

When is recommended to use Time and Materials model:

• Long and middle term projects with dynamic requirements;
• Project scope is not fully known;
• The customer wants the flexibility to modify the project scope.

Dedicated team

The Dedicated Team model is used to compensate for the lack of in-house IT expertise on mid-to-long-term projects. The billing is based on hourly rates of the developers, and they become a part of a team that is only concerned with the project and not engaged in any other tasks. The developers report directly to the client. However, the client is responsible for team management and control.

As an example, let’s say the client wants to develop software that is not in his team’s expertise, he can hire a developer or team who can do this. In this case, the client can outsource to an experienced team that can help him fulfill his customer’s needs by creating rich quality projects. This will enable the client to maintain stability between your team and the dedicated team.

Pros of Dedicated Team model:

• Control. The customer is provided full control and management of the team. Cooperation between the client and the team is very important because it allows for effective planning and evaluating the strengths and weaknesses of the work process.
• Development. When a team is focused on one project, it ensures the best results. The team has a full understanding of all project features and is dedicated to running the project smoothly.
• Progress. The ability to optimize the product is important for product growth. With the dedicated team, it is much easier.
• Reduced costs. The remote team solves many problems with the recruitment and hiring of staff. Plus, you always know the next month’s payments and taxes.

Cons of Dedicated Team model:

• Thoroughness. The client must consider process planning and task distribution for every member of the team.
• Engagement. You might have to invest more time in communication, discussion and software development.
• Graduality. Looking for the perfect candidate takes a lot of time and the client has to be involved in hiring team members.

When is recommended to use Dedicated Team model:

• For long-term IT projects with rapidly changing scopes.
• Adding more workforce to set up a strong development team.
• When you want to access cross-border expertise and knowledge sharing.

Conclusion

To sum up, every project provides the best results when it is managed according to the most suitable IT outsourcing model. Each software product development pricing model has its pros and cons. Choosing the best possible approach fully depends on the specifics of your project and expected results.

According to this, the first step is understanding your business model’s requirements. After determining your project’s needs, you can select the right outsourcing model and start with it.