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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.

According to analysts, global employers are unable to fill about 40 million IT vacancies due to a shortage of technical specialists in their region. And the situation is expected to worsen further. For example, in the USA, it is projected that by 2030, there will be a shortage of over 85 million programmers.

What to do? The solution is simple: consider an extended development team as an alternative to in-house developers.

What are these teams? What are the benefits of this approach to creating digital solutions? We will discuss this and much more in this article.

What Is an Extended Development Team?

An extended software development team consists of specialists who work together on a single project, but can be located anywhere, even in different time zones. This type of work organization is typically designed to combine the internal team with external specialists.

In other words, by involving an extended team in your project, you can gain all the advantages of outsourcing while saving budget and maintaining full control over the development process.

According to research, 92% of G2000 companies use tended teams and other forms of outsourcing. Now is the time to consider involving freelance specialists and experience all the benefits of this approach for your business.

Key Benefits of an Extended Team Model

Integrating external specialists into an internal team helps achieve even the boldest business goals through the following advantages:

Access to Global Talent Pool

Do you want to create a truly innovative product that no competitor offers? Be prepared for the fact that the local market may not have a programmer with the relevant experience. Alternatively, a lack of personnel may make it very difficult to attract the right specialist.

‍The number of developers varies from country to country. In fact, the difference can be significant even within the same country. For example, in the USA, the highest number of software engineers resides in California. There are over 628,000 in this state, while in Michigan and North Carolina, the number of specialists barely exceeds 100,000.

In such a situation, extended development teams can be a real lifesaver. With their help, you will be able to attract experts from any corner of the world to your project.

Cost-Effective Scaling

Hiring an employee in-house is always costly. According to numerous studies, it can cost a company anywhere from $28,000 to $35,000. And this is just the cost of provided benefits, insurance, vacation, and additional payments (such as overtime). In addition, you'll need to spend on training and onboarding staff.

Apart from the high costs, all of this takes a lot of time, and the need for scaling sometimes cannot be delayed.

However, if you consider involving external specialists, these problems can be easily avoided. A reliable provider will quickly find the right specialist, and your expenses will be limited to payment per hour or based on the actual volume of work completed.

‍Additionally, with the extended team model, you have another opportunity to save. This is possible due to the lack of regional constraints—as the cost of labor for developers is evaluated differently in various countries. For example, in North America, the average software development hourly rate is $77. In Eastern Europe, the same service can be obtained for $58 per hour, and in the Asia Pacific region, developers charge even less—around $28 per hour.

Faster Development and Delivery

An extended software development team helps accelerate development cycles. Here’s how this happens:

• Involving developers from different time zones allows such a team to work on the project almost around the clock. Is this possible with in-house specialists? Hardly.

• By expanding your team, you can save time on hiring and staff training. On average, the recruitment process in the IT field takes 35 days.

• You can hire several extended teams to handle different tasks. They will work on the project in parallel, thereby reducing development time.

Flexibility and Adaptability

The hiring model discussed in this article allows you to be as flexible as possible when it comes to changes in project needs. Let’s break it down further.

• Have the project goals changed, and you need to scale quickly? As you know, finding an external specialist is much faster than hiring an in-house employee. Additionally, you can adjust the requirements for candidates as the product evolves.

• Geographical flexibility also plays a role here. If you're experiencing a shortage of the right talent in your region, you can look for experts in the required technology stack overseas. With extended development teams, anything is possible.

Hassle-Free Management

When working with external specialists, you can fully control the process of developing a digital solution. At the same time, managing such a team is economically efficient and requires less time.

Why?

• When turning to trusted providers, they take on all the responsibilities for staff selection. You don’t spend time and money on searching, interviews, and hiring.

• The high qualification of extended teams frees you from the need for micromanagement. This allows you to focus on project management and achieving global business goals.

• Outsourcing service providers typically have established communication channels. They use specialized tools like Slack, Trello, or Jira to bridge geographical distances. Moreover, such companies often provide a project manager. It’s their responsibility to ensure smooth collaboration.

Operational Process of an Extended Development Team

To ensure the seamless integration of external specialists into your internal team, several stages of organizing updated workflows must be followed. Remember, your team is the foundation on which the creation of the software product will be based. Therefore, no detail should be overlooked.

Initial Planning Stage

This stage is crucial for the success of the project. Clear planning is the key to knowing the direction you need to move in.

It includes the following steps:

1. Formulating business goals. Start by defining the product you want to create and how it will benefit your business. It is also recommended to immediately outline the required features to be developed. This will help clearly define expectations for the final product and avoid misunderstandings between stakeholders and the team.

2. Assessing internal resources. Identify what knowledge and skills are needed to achieve the set goals. After that, assess your internal team. Are there enough specialists? Do they have the necessary expertise?

3. Project timeline and budget planning. Set realistic deadlines and determine the expenses you can afford. Consider development costs, project management, and potential unforeseen issues.

4. Choosing the collaboration model. If the project requires external developers, you have several options. Among them are outsourcing, dedicated teams, and the extended team model discussed in this article. We will discuss the differences between these three approaches later.

5. Risk assessment. Suppose you have chosen the extended team model. Think in advance about the risks you may face, such as communication gaps and potential time zone differences. In addition to the risks associated with engaging external specialists, consider other possible problems, including technical ones.

Finding the Right Tech Partner

Opt for a proven company with a good reputation. This guarantees that highly qualified professionals will be selected for you, whose expertise fully matches the project requirements. 

Here are a few tips to help you choose a reliable provider:

• Study the company’s website, focusing on its portfolio.
• Read reviews about the provider online.
• Compare the specialization of the potential partner with the plan for your project.
• Assess the cost-to-quality ratio of the services offered.
• Ensure that the company’s values align with yours.

Building the Development Team

The main task at this stage is to ensure smooth integration of the new specialists into the existing team. To do this, it is important to clearly define the roles they will perform on the project.

And a few more recommendations:

• Work on staff selection together with your technical partner. This does not mean total control or direct participation in the interviews. Just stay in the loop to ensure the candidate matches your requirements as closely as possible.

• Balance the size of the extended team. Make sure the number of employees corresponds to the goals of the project and its budget. A team that is too large may lead to unnecessary costs, while one that is too small can increase time-to-market.

Onboarding and Integration

This step involves introducing external specialists to the nuances of your internal processes:

1. Familiarization with the project specifics. Before starting work, make sure the hired developers fully understand the project’s goals and its specific requirements.

2. Setting up communication channels. Inform the extended development team about the communication channels used in your team. Set up tools like Slack, Trello, or Jira for project management and daily communication.

3. Providing access to technical infrastructure. Ensure that all team members have access to the tools necessary for efficient work. These may include version control systems, code repositories, etc.

4. Discussing internal procedures and rules. Communicate to the team how you plan to measure project progress, which metrics you will use to evaluate performance developers, and how often you expect feedback from them. This will help avoid misunderstandings and conflicts.

Establishing Clear Objectives

What do you think is the most important thing at this stage? The answer is simple. You need to ensure that both in-house and external specialists are working towards the same goal. To do this, you should:

• Clearly define the project goals. This may involve creating a product from scratch, implementing new functionality, or maintaining an existing system.

• Set milestones. Break the project into smaller segments. Each segment should have specific deadlines and deliverables. This will help track progress.

• Define KPIs. Predefine metrics to measure the team’s performance. These should relate to both the technical characteristics of the product (e.g., code quality or delivery speed) and the achievement of business goals (e.g., return on investment).

• Discuss communication issues: Frequency of meetings, reporting formats, communication channels—all of these should be discussed with the extended software development team at the very beginning of the collaboration. This will ensure you always know what stage of goal achievement you are at.

Project Kickoff and Execution

When all the details are discussed, it's time to start working on the project.

Project kickoff is the official start of collaboration. Now, the team needs to gather together (in whatever format possible), discuss the final details, and set the tone for the subsequent work.

The further work on the project includes:

• Prioritizing tasks. If the team uses an agile development methodology (which is most commonly used these days), this step involves planning the upcoming sprint. Specifically, it means defining the scope of work and aligning the team’s focus.

• Moving toward set goals. The team starts coding, testing, and building according to the project’s defined requirements.

• Monitoring progress and making adjustments. As the team completes tasks, it is important to track the status of the project. This allows for timely identification and resolution of problems.

The success of this stage depends on how accurately the previous steps for organizing the work with the extended team were followed.

Dedicated vs Outsourcing vs. Extended Teams: A Comparative Analysis

All three terms—dedicated team, outsourcing, and extended development team—refer to outsourcing work on a project to external developers. However, there are significant differences between them. Let's explore them further.

When to Consider an Extended Development Team?

There are several situations where it makes sense to consider the extended team model.

1. Need for rapid scaling. Is your business growing quickly? Do you need additional staff but don’t have time for long-term hiring? An extended team can help.

2. Need for specific expertise. If your internal team lacks certain skills, it’s not always necessary to hire a full-time employee. You can find an external specialist who will integrate into your team and contribute to the project.

3. Risk of missing deadlines. Is it time to release the product, but your internal resources are exhausted? This is another reason to turn to external developers. Extra hands and minds can easily save the situation.

4. Limited budget. Hiring external specialists is cheaper than increasing your full-time team. This is why extended teams are optimal for startups.

5. A responsible project requiring personal control. Not every project can be fully outsourced. Often, the client wants to retain the right to control the development process themselves. This opportunity is exactly what extended teams provide.

Want to get the best global talent at your disposal? Need to relieve your internal team? Want to speed up time-to-market and beat your competitors? Create an extended software development team on your project, and all of this will become a reality.

Challenges of Managing an Extended Development Team

If you’ve decided to integrate external developers into your internal team, you should consider not only the benefits of this approach, but also the certain challenges that come with it. It’s better to know about these challenges in advance.

• Communication Barriers. If you’re bringing in foreign specialists, be prepared for language, cultural, and time zone barriers. However, some of these can easily be turned into advantages. For example, thanks to different time zones, work on the project can continue around the clock.

• Integration difficulties. External developers may use work methods different from yours. Therefore, it’s important to discuss all expectations for collaboration in advance.

• Limited visibility. It can be challenging to monitor the work of remote employees as closely as you would like. To avoid this, it’s important to use reliable communication channels, reporting tools, analytics, and project management systems.

• Risks related to data security. This is especially relevant for projects involving sensitive user data. To minimize risks, work only with a trusted provider who ensures the reliability of the provided staff.

• Team engagement. It’s logical that full-time employees are more invested in the company’s development and the project’s success. Therefore, when bringing in external specialists, make sure to focus on their adaptation and motivation.

As you can see, with a thoughtful approach to forming extended development teams, all potential challenges can be easily overcome, leaving you in a winning position.

Future Trends in Extended Development Teams

The popularity of the extended team model in the field of software product development is gaining momentum. What transformations can we expect for this approach to organizing workflows in the near future?

• Focus on the use of AI and automation. Innovative technologies will allow companies to optimize project management and minimize the risks associated with working with remote employees.

• Preference for hybrid work formats. Companies will become increasingly interested in combining internal teams with external specialists. This will allow them to tap into the global talent pool while still maintaining control over processes.

• Emphasis on Agile and DevOps. Recent practices show that more and more teams are abandoning classical development methodologies in favor of flexible approaches. This allows extended teams to integrate more smoothly into internal processes and stay on the same wavelength as the in-house team.

• Expanded integration of specialized tools. Collaboration platforms are an essential attribute of extended development teams. As this model of cooperation spreads, new tools will emerge, and existing services will be improved.

• Maximum focus on data security. This issue is critical for all companies that engage external specialists. As a result, improved security protocols will emerge, and decentralized development and blockchain will become more relevant.

In summary, the future of extended teams is closely tied to the implementation of progressive technologies, process automation, and the increasing spread of this work format.

Create Your Extended Development Team with Darly Solutions

Darly Solutions is your reliable partner in creating unique digital solutions that will become a competitive advantage and the hallmark of your business.

We are ready to strengthen your internal team with specialists in Web and mobile development, UI/UX design, and testing. A qualified project manager will seamlessly integrate our experts into your processes and ensure smooth operations.

Contact Darly Solutions for a personalized consultation and experience the full potential of an extended development team from a trusted provider.

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If you have an idea for implementing a digital solution, but its business model is not yet clear, you may need a lot of resources to find the right one. But how can you test the effectiveness of this idea in practice when each of the potential target audience’s representatives may perceive it differently? The answer is simple: you should consider minimum viable product or MVP development for startups. Below, we will explain its peculiarity and analyze the advantages of this approach.

What Is an MVP for a Startup?

An MVP for startups is a software solution with a basic set of functions and a simplified visual that clearly demonstrates its unique selling proposition (USP) and does not have any additional features (that is, if you remove something from its functionality, it will lose this USP).

The Importance of MVP Development for Startups

The main purpose of MVP development for startup is simple: since early-stage startups do not have clear, verified business models, it is important to make sure that they will be accepted by their target audiences. Otherwise, their owners risk losing a lot of financial and time resources on something that “does not work” for the end consumers. So, to avoid this, it is better to implement such projects gradually, optimizing and expanding them, taking into account feedback obtained from real users.

MVP Development Process

Here are the main stages of the MVP development startups process:

• Defining the problem or goal of the target audience that you are going to help them solve/achieve with your product;
• Conducting market demand analysis, which consists of a detailed study of the target audience, market trends, similar software solutions, etc.;
• Rapid prototyping of the future solution and defining its key features that form the USP;
• Agile development of an MVP;
• Collecting feedback from real users;
• Continuous product iteration after the MVP launch aimed at tailoring the product to the needs of its target audience.

Top 9 Benefits of Building an MVP for Startups

In this paragraph, we would like to introduce MVP benefits that are valuable for each startup owner.

Cost Efficiency

Creating a minimum valuable solution is, first of all, much cheaper than in the case of a full-fledged one. This means that the initial budget for your project will be moderate, and you will not have to sacrifice anything important – for example, the budget for marketing, the salaries of your full-time specialists, and so on.

Faster Time to Market

No matter how unique your idea is, there is always a risk that someone will follow your example before you. In turn, the implementation of the MVP will allow you to reduce the time-to-market and, thus, gain an additional competitive advantage.

Market Validation

A quick launch of the MVP will help you validate it in real market conditions as fast as possible. In the future, you will be able to optimize it based on real feedback from its users and then improve it with each iteration and make it more and more valuable to them.

Risk Mitigation

Getting a working solution up and running quickly will help you identify its hidden issues early on, all thanks to the fact that it will be accessible to users from all over the world. In fact, no other type of testing, no matter how many resources are involved, can provide the same thoroughness in identifying errors and inconsistencies.

Investor Attraction

By launching a “raw” but functioning product to market early, it will be available to potential investors, too. Agree that this approach to demonstrating your idea looks much more viable and capable of achieving your goals than presenting a business plan only.

Customer-Centric Development

Generally speaking, creating a minimum viable solution is one of the key startup success factors since its entire development cycle is tailored to “please” its target audience. Indeed, since user feedback has to be taken into account long before the solution is released (first, it is tested by the project team and then – by representatives of focus groups), it turns out to be truly focused on the needs of the end users.

Enhanced Learning

MVP in startups helps to conduct an end-to-end assessment of market needs and make decisions based on the data obtained. This way, you can use the obtained insights in the further evolution of your product, turning it into a full-fledged software solution step by step.

Flexibility and Scalability

MVP development for tech startup involves creating an initially scalable, flexible foundation that is easy to modify and to which you can quickly add new/remove unnecessary functionality without much risk (primarily, associated with the appearance of hard-to-fix bugs).

Building a User Base

Finally, when you launch a full-fledged solution, it will already have some audience interested in using it. Thus, you will be able to reduce your budget for early adopter engagement and, in particular, the time and money needed to research and create a portrait of a potential client.

Conclusion

Now, knowing all the advantages and goals of startup MVP development, you can understand whether this option is right for you. In particular, if you are inclined in its favor, you can contact us, a service provider with many years of experience creating MVP solutions for startups in different industries. We will accompany you throughout the entire lifecycle of your project – from the ideation stage to the release and support of its full-fledged version.