Using AI to Monitor Mobile App Health: A Revolutionary Approach to Well-Being
AI's Influence on Mobile Health Apps
Artificial Intelligence (AI), in particular machine learning (ML), endows mobile apps with smart features that improve user experience and health outcomes. How to do it is as follows:
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Personalized Care: A tremendous quantity of user data, such as activity levels, sleep habits, dietary preferences, and medical histories, are analyzed by AI algorithms. Targeted interventions, recommendations, and tailored health insights are produced using this data. Consider a fitness app that utilizes AI to customize training schedules based on your goals, level of fitness, and real-time performance data. This app would be created by a top fitness app development firm in India.
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Early Detection and Intervention: AI is highly skilled at seeing trends and patterns in medical data. Early identification of possible health hazards is made possible by this, opening the door to proactive interventions and preventative actions. For example, an AI-driven app may examine heart rate and sleep patterns to spot symptoms of sleep apnea and alert the user to see a doctor.
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Better Diagnosis and Treatment: By analyzing vital signs or using image recognition in medical scans (MRIs, X-rays), AI systems can help medical personnel make diagnoses. This may result in quicker and more precise diagnosis, opening the door for prompt treatment regimens.
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Medication Adherence: One of the biggest obstacles to managing chronic diseases is medication adherence. Through the use of smartphone cameras and computer vision, AI-powered apps can monitor medicine intake and make sure patients are following their prescription regimens. To enhance treatment outcomes, top medical app development businesses in India are implementing this technology.
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Enhanced Patient Engagement: AI-powered chatbots may respond to user inquiries, give out basic medical information, and provide encouragement and support over time. Better patient participation and the ability for people to take control of their health are fostered by this.
AI-driven Functionalities for Mobile Health Apps
Mobile health apps can use a number of AI-powered elements to provide a thorough and interesting user experience, including:
- Activity Tracking and Analysis: Artificial Intelligence is not limited to step counting. It can evaluate workout intensity, spot possible injuries, and identify movement patterns by analyzing activity data.
- Sleep Monitoring and Optimization: AI is able to monitor and optimize sleep by analyzing several aspects of sleep, such as the length, phases, and interruptions of sleep. The app can provide tailored suggestions for better sleep hygiene based on this data.
- Nutritional Guidance: AI-enabled applications are able to assess eating patterns and recommend customized meal plans that take into account users' requirements, preferences, and health objectives.
- Mental Health Support: AI-driven chatbots can direct users to pertinent resources, conduct mindfulness exercises, and offer basic mental health support.
- Symptom Checker and Risk Assessment: Artificial intelligence algorithms are capable of evaluating symptoms reported by users and provide initial risk evaluations for a range of medical disorders. This gives people the ability to get medical help as soon as they need it.
Advantages of Mobile Health Apps with AI Power
There are several advantages to using AI in mobile health apps for both patients and healthcare providers:
- Better Health Outcomes: AI can greatly enhance people's health outcomes through early detection, tailored therapies, and drug adherence support.
- Enhanced Patient Engagement: Chatbots and tailored recommendations, two AI-powered services, keep users motivated and involved in their health journeys.
- Lower Healthcare Costs: By reducing the need for costly treatments in later stages, early detection and preventive actions can lessen the strain on healthcare systems.
- Remote Patient Monitoring: AI-powered apps facilitate remote patient monitoring, which enables medical professionals to keep tabs on patient health information and take appropriate action as needed. This is very helpful for treating patients who live far away and controlling chronic illnesses.
- Streamlined Workflow for Providers: Artificial intelligence (AI) may automate processes such as data analysis and report preparation, giving medical practitioners more time to concentrate on patient engagement and intricate medical decisions.
Obstacles and Things to Think About
Although AI has a lot of potential for mobile health apps, there are certain things to keep in mind:
- Data Security and Privacy: Since AI uses a lot of user data, it is critical to protect user data. To gain user trust, strong security protocols and open data policies are essential.
- Regulatory Environment: Regulations pertaining to healthcare apps with AI capabilities are currently being developed. It is imperative for app developers to remain informed about pertinent rules and guarantee that their applications adhere to data privacy and security standards.
- Equity and Accessibility: Developing AI takes a lot of resources. It is essential to guarantee that AI-powered applications are accessible and affordable for every socioeconomic group in society.
- Algorithmic Bias: The quality of AI algorithms depends on the quality of the training data. Biases in the training set of data may cause the AI model to produce biased results. Developers must be aware of such biases and put mitigation measures in place.
Developing a Robust Mobile Health App with AI
Careful planning, execution, and adherence to best practices are necessary for the successful development of a mobile health app driven by AI. Here's a guide to think about:
- Determine a definite Need: Concentrate on a particular health issue or user group. Who stands to gain the most from the solution you are attempting to implement?
- Put Together a Powerful Team: User experience (UX) designers, software engineers, data scientists, and medical specialists should all be on the development team. In this process, the knowledge of a top medical app development business in India can be quite helpful.
- Data Management and Gathering: Create a safe, moral, and ethical data gathering plan that prioritizes user permission. Put into place reliable data management and storage procedures.
- Choose the Correct AI Model: Take into account the features and data type of the app while choosing a suitable AI model.
- Put the User Experience First: Create an interface that is simple to use and straightforward to encourage user participation and make data collecting easier.
- Iteration and Pilot Testing: Before launching the app widely, get user input through extensive pilot testing.
- Regulatory Compliance: Verify that the application conforms with all applicable laws and guidelines on data privacy.
AI's Role in Mobile Health Apps
AI in mobile health apps has a bright future ahead of it. Observe these fascinating trends:
- Integration with Wearables and Sensors: Wearables and sensors will be easily integrated with AI-powered apps to enable the real-time collection of a greater variety of health data. Even more individualized health insights and therapies will be made possible by this.
- Advanced AI Techniques: Natural language processing (NLP) advances will improve chatbots' capacity to assist users with mental health issues and respond to intricate user inquiries.
- AI-powered Diagnostics and Treatment: Through sophisticated picture analysis and real-time health data interpretation, AI will become more important in diagnostics, enabling quicker and more precise diagnosis.
- Preventative and Predictive Healthcare: AI will play a key role in anticipating possible health hazards based on individual data and historical trends, enabling proactive preventive interventions.
In Summary
AI is transforming the field of mobile health by enabling patients to take control of their health and enabling medical professionals to give more effective, individualized care. Mobile health applications have the potential to significantly contribute to a healthier future for all people by ethically and responsibly utilizing AI.
Making Use of Indian Medical App Development Companies' Experience
The medical app development company India is booming and has access to a plethora of talent and knowledge. A successful AI-powered mobile health app can be created by collaborating with a top medical app development firm in India. What they can contribute is as follows:
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Understanding of the Healthcare Industry: Companies that design medical apps are aware of the special opportunities and difficulties that exist in this field.
- Experience with AI Integration: A lot of businesses have integrated AI features into mobile health apps in the past, guaranteeing the best possible user experience and performance.
- Expertise in Security and Compliance: They can guarantee that the application complies with strict privacy and security laws.
- Agile Development Methodologies: They frequently make use of them, which promote quick development cycles and ongoing enhancements based on input from users.
The Function of Indian Fitness App Development Companies
Although medical app development businesses have received much of the attention, fitness app development firms in India are just as important in utilizing AI for mobile health monitoring. How to do it is as follows:
- Customized Training Plans: AI may examine user information such as performance, goals, and fitness levels to design demanding yet doable customized training plans. Better fitness results and higher user engagement may result from this.
- Injury Prevention and Detection: AI systems are capable of analyzing movement patterns during exercise to spot any injury hazards. This helps in both injury prevention and detection. This makes it possible to modify training plans and encourages safer exercise habits.
- AI-Powered Nutritional Coaching: Fitness applications can incorporate AI-driven dietary recommendations that take into account users' activity levels, food choices, and personal goals.
- Motivation and Support: Throughout the fitness journey, AI-powered chatbots can send out inspirational messages, monitor progress, and give assistance.
AI Ethics Concerns for Mobile Health Apps
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)
Several ethical considerations must be addressed for the responsible use of AI in mobile health apps:
- Explainability and Transparency: Users should be able to easily comprehend how AI is applied within the app and how it impacts their personal health information. Users can comprehend the rationale behind the app's recommendations and outputs with the use of explainable AI approaches.
- User Control over Data: Users ought to have total authority over the data they own, including the freedom to view, edit, and remove it whenever they like.
- Steer Clear of Algorithmic Bias: As previously indicated, ensuring fair and equitable health results for all users requires reducing bias in AI algorithms. In order to overcome potential biases, diverse datasets and continuous model monitoring are crucial for AI systems.
In Conclusion, a Cooperative Strategy
A collaborative approach is necessary for the successful development and implementation of mobile health apps driven by AI. The major players are as follows:
- Medical Professionals: Their knowledge is essential for directing the creation of apps, guaranteeing that their features comply with accepted medical procedures and offer correct health information.
- Data Scientists and AI Developers: The technical expertise of data scientists and AI developers is essential for creating reliable AI models and smoothly incorporating them into mobile applications.
- Designers of User Experiences (UX): They make sure the application is accessible, easy to use, and encourages user participation.
- Regulatory Bodies: Their job is to set forth precise rules and regulations that govern the creation and application of AI in healthcare applications, guaranteeing patient security and privacy of data.
Possible Effects on Public Health
Mobile health apps with AI capabilities could have a big impact on Indian public health, especially when it comes to addressing:
- Chronic Disease Management: With the use of tools for symptom tracking, individualized food advice, and medication adherence reminders, artificial intelligence (AI) can help people with long-term disorders like diabetes or heart disease manage their health more successfully. Both better health outcomes and lower healthcare expenditures may result from this.
- Mental Health Awareness: AI-driven chatbots can give self-care resources, basic mental health support, and referrals to professionals when necessary. This can be especially helpful in areas where mental health providers are scarce.
- Early Disease Detection: Wearable technology and sensor data can be analyzed by AI algorithms to find early warning indicators of possible health problems. This makes it possible for treatment and intervention to begin early, which may improve prognoses.
- Accessibility and Affordability: Particularly in rural locations with little healthcare infrastructure, mobile health apps can provide people with a more affordable and accessible means to keep an eye on their health.
Obstacles and Things to Take Into Account for Widespread Adoption
Even though AI has a lot of potential, there are obstacles that must be removed before AI-powered health apps are widely used in India:
- Infrastructure and Digital Literacy: Many Indians do not have access to cellphones or dependable internet service. To guarantee that everyone has equal access to these technologies, this obstacle must be removed.
- Language Barriers: Inclusivity depends on the development of AI models that can comprehend and react to users in a variety of Indian languages.
- Cultural Considerations: Distinct parts of India may have quite distinct healthcare customs and beliefs. To encourage user adoption, apps' functionality and design must take cultural sensitivity into account.
- Cost of Development and Implementation: It can be costly to create and maintain AI-powered applications. To guarantee affordability and accessibility for patients and healthcare providers, creative funding approaches are needed.
The Function of Public Policy and the Government
The appropriate development and uptake of AI in mobile health apps can be greatly aided by the Indian government in the following ways:
- Investment in Digital Infrastructure: It's critical to increase nationwide availability of reasonably priced cellphones and dependable internet connectivity.
- Creating Regulatory Frameworks: It's critical to establish precise rules that cover data security, privacy, and moral issues for healthcare apps that use AI.
- Fostering Innovation and Research: Research on artificial intelligence (AI) in healthcare can advance more quickly if it is funded and encouraged.
- Increasing Public Awareness: Creating a better understanding of the advantages and possible dangers of AI-powered mobile health apps helps foster acceptance and foster a sense of trust.
In Summary: An Initiator for a Healthier Future
AI in mobile health apps offers a game-changing chance to empower people, enhance healthcare results, and build a healthier future for everybody. India can use AI to transform healthcare and guarantee equal access to healthcare for all of its residents by resolving the obstacles, encouraging cooperation among stakeholders, and putting responsible practices into place.