AI Medical Diagnosis App Development in 2025: Features & Cost

As healthcare races toward AI-driven transformation, accurate and timely diagnosis is more indispensable than ever. However, traditional diagnostic methods usually fail to deliver rapid results, leaving doctors struggling with time-consuming processes and patients waiting for answers.

This is where AI medical diagnosis apps come into play. By leveraging artificial intelligence in medical diagnosis, these apps can help healthcare providers diagnose conditions more quickly and accurately, thereby improving patient outcomes.

AI medical diagnosis app development for healthcare businesses are designed to support doctors by delivering data-driven insights and predictions, making them a vital tool in modern medical practices. Whether you are interested in AI medical diagnosis app development or exploring the broader process of AI doctor diagnosis & treatment application development, understanding the features and costs involved is key.

In this guide, we’ll explore:

• The AI medical diagnosis app development cost and features
• The steps to create an AI-powered healthcare diagnosis app that meets healthcare standards
• The right tech stack to choose from, and more

By the end of this guide, healthcare providers, tech companies, and business owners will have the insights needed to build and implement AI-powered medical diagnosis applications successfully.

What is an AI Medical Diagnosis App?

An AI medical diagnosis app is an avant-garde tool that leverages the strength of artificial intelligence to help healthcare professionals diagnose medical conditions. Such apps utilize advanced algorithms and machine learning models to thoroughly analyze patient data, including lab results, medical histories, and imaging, providing insights and potential diagnoses that inform healthcare decisions.

The process of creating applications that utilize artificial intelligence to assist healthcare providers in diagnosing medical conditions is known as AI medical diagnosis application development.

How AI Medical Diagnosis Apps Work - A Quick Scan

1. Data Collection

AI apps collect data from various sources, including patient records, lab tests, and medical imaging.

2. Data Analysis

The app utilizes algorithms and machine learning to thoroughly analyze the data, seeking patterns or signs of possible health issues.

3. Diagnosis Generation

Based on the analysis, the app proffers possible diagnoses, flags abnormalities, or delivers insights to aid decision-making.

4. Continuous Learning

The more data the app processes, the more intelligent it becomes. Machine learning for medical diagnosis constantly improves its diagnostic precision.

These apps don’t replace doctors but rather help them make quicker and more accurate decisions, thereby improving patient care and reducing errors.

Why Should New Business Owners Consider Investing in AI Medical Diagnosis App Development?

The healthcare industry is undergoing rapid transformation, and AI-driven medical diagnosis apps are at the forefront of this evolution. For new business owners and entrepreneurs, investing in this space presents a significant opportunity to enter an expanding market and address critical healthcare challenges.

Here's why you should consider creating AI medical diagnosis app:

1. Growing Demand for Healthcare Innovation

• Healthcare providers are increasingly adopting AI medical diagnosis applications for faster, more accurate diagnoses.
• Entrepreneurs can capitalize on this increasing demand for digital healthcare solutions by providing Real-time AI health diagnosis apps.

The global AI medical diagnosis app market was valued at $0.9 billion in 2023 and is expected to hit $5.2 billion by 2033, growing at a compound annual growth rate (CAGR) of 19.1%.

2. Improved Accuracy and Cost Efficiency

• AI diagnosis apps minimize human errors by analyzing huge amounts of data with precision, resulting in improved patient outcomes.
• These apps can also assist healthcare providers in saving costs by simplifying diagnostic processes and diminishing mistakes.

A UCLA (University of California, Los Angeles) study found that the AI tool Unfold AI detected prostate cancer with 84% accuracy, exceeding the 67% accuracy rate of doctors.

3. Compliance and Trust

• AI apps must comply with stringent regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), to ensure trust and reliability.
• By developing AI medical diagnosis app that’s compliant and trustworthy, entrepreneurs can attract healthcare businesses seeking secure and dependable tools.
• AI in healthcare is primarily regulated under frameworks for Software as a Medical Device (SaMD), ensuring the safety and efficacy of these applications.

4. Scalability and Market Growth

• The AI healthcare market is experiencing growth, with increasing adoption across hospitals and clinics.
• Investing in an AI-based patient diagnosis app provides entrepreneurs with the opportunity to scale their solutions globally, capitalizing on the constantly growing demand.

The global AI in diagnostics market was estimated to be worth $1.12 billion in 2023 and is anticipated to reach $12.65 billion by 2034, expanding at a CAGR of 24.64%.

5. Competitive Edge

• Offering innovative, AI-powered solutions gives businesses a competitive edge in a rapidly evolving market.
• Entrepreneurs can distinguish themselves by delivering cutting-edge technology that improves the accuracy and speed of medical diagnoses.

6. Expanding Healthcare Access

• AI diagnosis apps can reach underserved or remote areas, thereby increasing access to healthcare where it is most needed.
• This presents an opportunity to create a positive social impact while generating a profitable business.

7. Investment Opportunities

• The healthcare sector is driving significant investment, with venture capitalists seeking AI-driven innovations.
• Entrepreneurs in the AI medical diagnosis landscape may propose funding and partnership opportunities to expand their business.

Healthcare AI companies focused on patient diagnostics report 52% of the total AI investment in clinical solutions.

Also Read: How To Build An AI Mental Health and Wellness App Like Wysa?

Must-Have Features for AI Medical Diagnosis App Development

Certain features are essential to ensure a fully functional and user-friendly AI medical diagnosis app, which improves the accuracy of diagnoses, enhances the user experience, and maintains security.

Below are the key features of AI medical diagnosis applications that facilitate the automation of the diagnostic process, enhance patient care, and ensure compliance with healthcare regulations.

Feature	Explanation
Real-time Diagnosis	Provides immediate diagnostic results based on user inputs or uploaded medical data, enabling faster decision-making for healthcare providers.
Symptom Checker	Allows users to input symptoms and receive potential diagnoses, which can guide them in seeking further medical attention or managing their health.
Medical History Integration	Integrates patients’ previous medical records to provide a comprehensive view of their health history, ensuring accurate diagnostics.
Predictive Analytics	Uses historical data and AI models to predict potential health issues or outcomes, enabling early intervention and proactive healthcare.
Personalized Health Recommendations	Offers tailored advice based on the patient's health data, including lifestyle changes, medications, and preventive measures for better health management.
Image Recognition (for medical images)	Utilizes AI algorithms to analyze medical images (e.g., X-rays, MRIs) for detecting conditions such as tumors, fractures, or abnormalities.
Telemedicine Integration	Integrates video calls and messaging features to allow remote consultations with healthcare providers, improving access to care.
Integration with Wearable Devices	Syncs with wearables (e.g., fitness trackers, heart rate monitors) to collect real-time health data, which can inform diagnoses and treatment plans.
Natural Language Processing (NLP)	Allows the app to interpret and process text or speech inputs from patients, enabling more effective interaction and understanding of symptoms or health-related queries.
AI-Powered Decision Support System	Assists doctors in making informed decisions by analyzing medical data, providing possible diagnoses, and recommending treatment options based on AI analysis.
Prescription Management	Manages prescriptions by allowing users to track medications, receive reminders, and manage refills, ensuring continuous care.
Cloud Data Storage and Access	Stores medical data securely in the cloud, allowing easy access, sharing, and updating of patient records between authorized healthcare providers.
HIPAA-Compliant Security Features	Ensures the app meets HIPAA guidelines for data protection, maintaining confidentiality, and security for sensitive health information.
Multi-language Support	Allows users from different linguistic backgrounds to access the app in their preferred language, enhancing usability in global markets.
Secure User Authentication	Incorporates biometric or two-factor authentication to ensure that user data is protected from unauthorized access, maintaining privacy and security.

Advanced Features to Consider While Developing AI Medical Diagnosis App

While the standard features of AI medical diagnosis applications focus on basic functions, incorporating non-ordinary features can significantly enhance the app's functionality, usability, and overall user experience.

Below are the advanced features that provide unique value and hold the caliber to set your app apart in a competitive market.

Feature	Explanation
Voice Recognition for Hands-Free Operation	Allows users to interact with the app using voice commands, enabling hands-free operation, especially useful in medical settings where hands-on interaction is limited.
AI-Powered Genetic Testing Integration	Integrates genetic testing data into the diagnostic process, allowing AI to analyze genetic markers and offer insights into hereditary conditions and predispositions.
Mental Health Screening Tools	Includes AI-driven tools for identifying signs of mental health conditions like depression or anxiety based on user input and behavioral patterns.
Chronic Disease Management Tools	Offers tools for managing chronic conditions (e.g., diabetes, hypertension) by tracking symptoms, medications, and lifestyle changes to improve long-term health management.
AI Chatbot for User Interaction	Provides a conversational interface that allows users to ask questions, input symptoms, and receive instant feedback, thereby enhancing user engagement and support.
Blockchain for Data Privacy	Uses blockchain technology to store and manage patient data securely, ensuring transparency, immutability, and enhanced privacy in handling sensitive health information.
Augmented Reality (AR) for Medical Education	Leverages AR to provide immersive medical training experiences for both healthcare professionals and patients, helping them better understand procedures and conditions.
3D Medical Imaging Integration	Integrates 3D medical imaging (e.g., CT scans, MRIs) into the app to provide users with a more interactive and detailed view of their health, enabling better diagnosis and education.
Blockchain-Based Health Records Management	Implements blockchain technology for decentralized and secure management of health records, allowing patients to own and control their data while ensuring secure sharing with healthcare providers.
AI-Driven Medication Reminders and Alerts	Uses AI to remind users to take their medications, ensuring adherence to prescribed treatment plans and improving health outcomes over time.

How to Build AI Medical Diagnosis App: 9 Key Steps to Follow

What is the process to build AI medical diagnosis app? Here, we will discuss every relevant detail.

Building AI medical diagnosis app needs a meticulous approach, from conception to launch. The process involves technical development and a comprehensive understanding of the healthcare industry's needs, ensuring regulatory compliance and delivering a user-friendly experience.

Let’s break down how to develop AI medical diagnosis app step by step:

1. Define the App’s Purpose and Target Audience

• Clearly define your AI Medical diagnosis application development goal (such as symptom checkers, diagnostic assistance, or chronic disease management).
• Identify the target audience, whether doctors, patients, or healthcare providers, to ensure the app meets their specific needs.

2. Choose the Right Technology Stack

• Select appropriate technologies (frameworks, programming languages, AI tools, and databases) that best fit the app’s functionality and scalability.
• Ensure that the chosen stack supports AI development, data handling, and an effortless user experience.

3. Build a Proof of Concept (PoC) or Minimum Viable Product (MVP)

• Develop a Minimum Viable Product (MVP) or Proof of Concept (PoC) to reveal the core functionality of the app.
• Utilize this to gather feedback, test feasibility, and refine the idea before investing more resources.

4. Develop AI Algorithms and Train Models with Medical Data

• Create and train machine learning models leveraging the power of high-quality, annotated medical data.
• These models enable the app to make accurate diagnoses and medical predictions.

5. Ensure Compliance with Regulations

• Ensure the app follows healthcare regulations, such as HIPAA for data privacy and FDA approval for medical devices.
• This ensures legal compliance and fosters trust with users.

6. Design a User-Friendly UI/UX for Better Accessibility

• Strive for a clean, intuitive UI/UX design that ensures ease of use, particularly for healthcare professionals and patients.
• Consider incorporating accessibility features to make the app accessible to people with various needs.

7. Develop the Full-Fledged App with Advanced Features and Scalability

• Once the MVP is validated, proceed to create the full AI app, including advanced features such as telemedicine integration, predictive analytics, and AI-powered decision support.
• Ensure the app is scalable to drive growth and future updates.

8. Test the AI Medical Diagnosis App Thoroughly

• Conduct rigorous testing across different areas (security, functionality, usability, and performance) to ensure the app works seamlessly.
• Verify that the app meets the necessary healthcare standards and regulatory needs.

9. Launch, Monitor, and Iterate the App

• After launch, monitor user feedback and app performance.
• Regularly update and iterate on the app to enhance functionality and manage emerging needs.

By following these steps to create an AI-powered healthcare diagnosis app, you can effectively develop one that delivers real value to users while meeting the standards and regulations of the healthcare industry.

Development Costs for AI Medical Diagnosis Apps in 2025

The cost to develop AI medical diagnosis app in 2025 depends on features, complexity, and AI depth. Knowing the cost breakdown of AI medical applications helps in setting a realistic budget.

Below are the details of AI healthcare app development cost and features for your better understanding:

App Type	Cost Estimate	Key Features	Description
MVP for AI Medical Diagnosis App	$20,000 - $50,000	Basic functionality: symptom checkers, simple diagnostics powered by AI, and minimal features.	A basic version of the app includes core functionalities such as a symptom checker and simple diagnostics using AI.
Simple AI Medical Diagnosis App	$50,000 - $100,000	Core functionality, along with additional features such as integration with medical records and basic diagnostic AI.	Includes additional features like a basic symptom checker, integration with medical records, and AI-driven diagnosis.
Advanced AI Medical Diagnosis App	$100,000 - $200,000+	Full-fledged features: predictive analytics, telemedicine integration, real-time diagnostics, and more complex AI.	Includes advanced AI algorithms, integration with wearables, telemedicine features, predictive analytics, and more.

Factors Affecting the cost to develop AI medical diagnosis app in 2025

Factor	Impact	Explanation
AI Integration Complexity	High	More advanced AI algorithms require significant development and testing time, raising the cost.
App Features	Variable	Adding features such as telemedicine, wearables, and predictive analytics in medical diagnosis apps increases the development cost.
Regulatory Compliance	High	Ensuring the app meets HIPAA and FDA standards demands additional legal consultations and technical work.
Design and UX	Medium	Creating an intuitive, user-friendly design that prioritizes accessibility will require specialized resources and expertise.
Data Security	High	Implementing robust data security, including encryption and secure data storage, is a key factor in increasing costs.

Whether you're looking for a simple MVP or a fully advanced, AI-driven healthcare solution, it's essential to align your budget with the key features and functionalities that will drive the ultimate success of your app in the healthcare industry.

Also Read: AI Fitness App Development Cost in 2025: Factors & Examples

AI Tools and Tech Stack Required to Build AI Medical Diagnosis App

Building AI medical diagnosis app demands choosing the right tools and technologies. Here are the best AI technologies for medical diagnosis app development, including AI tools and frameworks, that you can consider when developing key features to make your solution effective and scalable.

Category	Tools/Technologies	Purpose
AI Frameworks & Libraries	TensorFlow, PyTorch, Keras, Scikit-learn	Used for developing and training machine learning and deep learning models that can diagnose medical conditions based on data inputs (e.g., symptoms, images).
Programming Languages	Python, R, Java, C++	Python is the go-to language for AI/ML due to its robust libraries. R is used for statistical analysis, while Java and C++ are utilized for performance-sensitive tasks.
Data Processing & Analysis	Pandas, Numpy, Matplotlib, Dask	Libraries for data manipulation, analysis, and visualization are essential for handling large datasets, such as medical records.
Cloud Platforms	AWS, Google Cloud Platform (GCP), Microsoft Azure	Cloud services for scalable storage, compute power, and model deployment. Supports real-time diagnostics and large-scale data analysis.
Medical Imaging Processing	OpenCV, SimpleITK, Pydicom	Libraries for processing medical images (X-rays, MRI scans, CT scans) to support image-based diagnostics like cancer detection.
Natural Language Processing	SpaCy, NLTK, Hugging Face	NLP tools for processing and understanding unstructured medical text (e.g., clinical notes, patient queries).
Databases	MongoDB, PostgreSQL, MySQL, SQLite, Firebase	Databases for storing medical data, patient history, diagnostic results, and logs. NoSQL (MongoDB) for flexibility, SQL (PostgreSQL, MySQL) for structured data.
Mobile App Development	Flutter, React Native, Swift (iOS), Kotlin (Android)	Tools for developing cross-platform mobile applications that can interface with AI models, display results, and interact with users.
Security and Compliance	OpenSSL, OAuth, JWT, Firebase Authentication, HIPAA-compliant tools	Encryption, authentication, and security tools to ensure patient data privacy and HIPAA compliance.
APIs for Integration	Redox, FHIR, HL7	APIs that allow integration with electronic health records (EHR), health management systems, and other third-party tools.
Testing & Deployment Tools	Selenium, JUnit, Appium, Docker, Kubernetes, Jenkins	Tools for testing and automating the deployment pipeline, ensuring continuous integration and efficient app maintenance.
Visualization Tools	Tableau, Power BI, Plotly	Used to create visual representations of the data or diagnostic results for both users and healthcare providers.

Challenges in AI Medical Diagnosis App Development

AI diagnosis app development encounters a unique set of challenges that can hinder the development, deployment, and long-term success of AI medical apps for doctors and hospitals.

Below are some of the most noteworthy challenges, along with potential solutions for each:

1. Data Privacy and Security Concerns

Solution:

• Implement encryption (AES-256) for every sensitive patient data, guaranteeing that data is secure both in transit and at rest.
• Utilize HIPAA-compliant cloud services, such as AWS, Google Cloud, or Azure, to securely store and process health data.
• Integrate two-factor authentication (2FA) to add an extra layer of security.
• Conduct regular vulnerability assessments and security audits to safeguard data continually.

2. Lack of High-Quality Medical Data for Training AI

Solution:

• Partner with medical institutions to get anonymized, high-quality datasets for training purposes.
• Utilize synthetic data generation techniques to create additional training data that simulates real-world medical data while maintaining privacy.
• Select data augmentation methods tailored explicitly for medical images to enhance the training datasets and improve model performance.

3. Regulatory Compliance and Approval (FDA, HIPAA)

Solution:

• Consult with legal professionals and compliance experts early in the development process to ensure tcomplies withulfills healthcare regulasuch ass, like HIPAA and FDA guidelines.
• Look for FDA pre-certification programs to speed up the approval process for medical software.
• Ensure HIPAA compliance in AI healthcare app development, especially in terms of data handling, security, and storage.
• Maintain clear and detailed documentation of the AI models and their processes, which is crucial for regulatory approval.

4. AI Model Bias and Accuracy Issues

Solution:

• Ensure that AI models are trained on diverse and balanced datasets to reduce bias and enhance diagnostic accuracy across various demographics.
• Utilize bias detection tools during the training process to recognize and eliminate any biases in the models.
• Regularly monitor and audit the AI models post-launch to detect and fix any accuracy problems or emerging biases.
• Integrate explainable AI (XAI) techniques to deliver transparency in how the model makes its diagnostic decisions.

5. Integration with Existing Healthcare Systems

Solution:

• Utilize FHIR (Fast Healthcare Interoperability Resources) and HL7 standards to lead to smooth integration with existing EHR (Electronic Health Records) and EMR (Electronic Medical Records) systems.
• Ensure the app can interact with hospital databases and other healthcare management tools via secure APIs, enabling seamless data exchange.
• Collaborate with healthcare IT teams to ensure compatibility and seamless interoperability with existing systems at healthcare facilities.

Also Read: Top 20+ AI Development Companies In USA: Pioneering Innovation in the Market

The Future of AI in Medical Diagnosis (2025 and Beyond)

In 2025 and beyond, AI for healthcare diagnosis will enable early detection, provide predictive insights, and facilitate personalized treatment across all levels of care. With AI in clinical decision support systems, medical professionals can make faster, data-informed decisions that improve outcomes and streamline workflows.

Here are some key trends that will catalyze the AI landscape in medical diagnosis over the coming years:

1. Increased Accuracy in Diagnostics through Advanced AI Algorithms

AI algorithms will become more refined, improving diagnostic precision. With deeper learning and access to larger datasets, AI will enable the detection of diseases at an earlier stage, resulting in improved treatment outcomes.

2. Widespread Adoption of AI in Telemedicine

AI integration in telemedicine will permit healthcare providers to make more accurate remote diagnoses. This will enhance access to quality healthcare, particularly in underserved areas.

3. Integration of AI with Genomic and Personalized Medicine

AI will play a vital role in personalized medicine by examining genetic data to create individualized treatment plans. It will enable the prediction of drug responses and the identification of genetic risks associated with diseases.

4. Real-Time Diagnosis via Wearables and Mobile Apps

AI-powered wearables and mobile apps will offer real-time health monitoring. They will detect irregularities in health metrics and provide immediate diagnoses, facilitating quicker intervention.

5. AI-Driven Predictive Analytics for Preventive Healthcare

AI will utilize patient data and lifestyle factors to anticipate future health risks. This will aid in early prevention strategies, downsizing healthcare costs, and enhancing long-term health outcomes.

How PixelBrainy Can Help You Develop AI Medical Diagnosis App?

PixelBrainy is recognized as a leading AI development company that offers a skilled team of AI developers with expertise in AI medical diagnosis app development tailored to the healthcare industry. Our team excels in creating AI-powered solutions that leverage advanced algorithms and machine learning to offer real-time, accurate diagnostic tools.

Whether you need an AI-powered symptom checker app or a fully integrated diagnostic platform, we provide scalable, innovative, and regulation-compliant solutions for your business.

Client Success Stories - A Quick Glimpse

"Working with PixelBrainy was a pivotal decision for us. The team’s expertise in AI and healthcare technology enabled us to develop an intuitive and highly accurate AI-driven diagnostic app. The platform’s impact on our diagnostic speed and patient satisfaction has been nothing short of transformative."

— Healthcare Provider X (NDA in place)

"Thanks to PixelBrainy’s innovative approach, our telemedicine platform now offers AI-assisted diagnostics that help our healthcare providers make informed decisions faster. Their professional team ensured that the app was both user-friendly and fully compliant with healthcare regulations."

— Telemedicine Company X (NDA in place)

Here's a real-time case study to illustrate how we can support your project:

Case Study: AI Medical Diagnosis App for Telemedicine

Client: A telemedicine company seeking to improve its service offering with AI-driven diagnostic tools.

Challenge:

The client required an AI-powered medical diagnosis app that could support remote consultations, aid healthcare professionals in diagnosing conditions, and enhance patient outcomes while complying with stringent regulatory standards, including HIPAA and FDA guidelines.

Solution by PixelBrainy:

PixelBrainy collaborated with the client to develop a comprehensive AI-powered medical diagnosis app that integrated seamlessly with telemedicine platforms.

The app features:

• Real-Time Symptom Analysis
• Data Privacy & Compliance
• AI-Powered Diagnostic Assistance
• Telemedicine Integration

Outcome:

• 40% improvement in diagnostic speed
• 30% increase in diagnostic accuracy
• 50% rise in telemedicine adoption
• 35% boost in patient satisfaction

Partner with us to design and build a scalable, AI-powered medical diagnosis platform from scratch or seamlessly integrate intelligent diagnostic solutions into your existing architecture.

Conclusion

AI medical diagnosis apps are disrupting traditional healthcare models by providing faster, more precise, and personalized care. Whether you are set to build from scratch or integrate AI solutions, now is the time to invest in AI medical diagnosis app development.

Curious about how to make AI medical diagnosis app from scratch? Whether you're targeting hospitals or individual users, the journey begins with defining your app’s purpose and selecting the right AI tools.

At PixelBrainy, we help healthcare businesses effortlessly create and integrate custom AI medical diagnosis apps that meet relevant industry standards and drive innovation. Let’s connect and build the future of AI-powered healthcare together.

Frequently Asked Questions

What is the difference between a medical diagnosis app and a healthcare app?

A medical diagnosis app is specifically designed to diagnose health conditions based on a patient's medical history, symptoms, and the use of AI algorithms. It offers diagnostic suggestions or insights to both patients and healthcare professionals. On the contrary, a healthcare app encloses a broader range of functionalities, including health tracking, appointment scheduling, fitness monitoring, and telemedicine features, which may or may not possess diagnostic capabilities.

How accurate are AI diagnosis apps?

AI diagnosis apps can be highly accurate, especially when trained on large datasets of high-quality medical data. However, the precision depends on the quality of the AI model, the data used, and the exact condition being diagnosed.

Can AI replace doctors in the diagnosis process?

AI is a powerful tool that can improve the diagnostic process, but it can’t fully replace doctors. While AI can process vast amounts of data fast and deliver diagnostic suggestions, human experience, determination, and patient context are essential for accurate diagnosis and treatment planning. AI should be utilized as a complement to healthcare professionals, not as a replacement.

Is it safe to use AI medical diagnosis apps for health-related decisions?

AI medical diagnosis apps are designed to facilitate informed decision-making. Safety depends on the app's accuracy, data security, and compliance with healthcare regulations, such as HIPAA. Still, it is essential to consult a healthcare professional before making any notable health-related decisions based entirely on app suggestions.

How long does it take to develop an AI medical diagnosis app?

The development timeline for an AI medical diagnosis app varies depending on its complexity and the specific features it requires. On average, an MVP (Minimum Viable Product) typically takes 4 to 6 months, while a fully developed and feature-rich AI medical diagnosis app can take 9 to 12 months or longer. Factors like AI model training, compliance with regulations, and integration with existing systems can impact the timeline.