This paper evaluates the privacy policies of AI-powered mHealth apps, focusing on their availability, readability, transparency, and scope. We replicate the methodology of Sunyaev et al. 2015, for iOS apps and compile a dataset of 2,231 AI-focused health apps. Our analysis reveals that only 68.04% of these apps have publicly accessible privacy policies. On average, a privacy policy contains 2,784.25 words, with a mean readability score of 13.48. Regarding transparency, aspects such as "type of information collected" and "sharing of information" are more frequently addressed, whereas "rationale for collection" is less commonly discussed. Additionally, only 11.2% of the privacy policies mention the use of user health data for training AI systems. In terms of scope, over 60% of app privacy policies cover the single app, and 25% cover no app-related scope., , # iOS AI Mobile Health Application Privacy Policies

This dataset comprises privacy policies collected from mobile health applications available on the iOS App Store that utilize Artificial Intelligence (AI).

Dataset Structure

The dataset is provided in a JSON format. Each entry in the JSON array represents an individual mobile health application and contains the following fields:

• title: The name of the mobile health application.
• privacy_policy: The full text of the application's privacy policy. In cases where a privacy policy could not be found, this field is explicitly marked as "None Found".

,

Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

US Healthcare NPI Data is a comprehensive resource offering detailed information on health providers registered in the United States.

Dataset Highlights:

• NPI Numbers: Unique identification numbers for health providers.
• Contact Details: Includes addresses and phone numbers.
• State License Numbers: State-specific licensing information.
• Additional Identifiers: Other identifiers related to the providers.
• Business Names: Names of the provider’s business entities.
• Taxonomies: Classification of provider types and specialties.

Taxonomy Data:

• Includes codes, groupings, and classifications.
• Facilitates detailed analysis and categorization of providers.

Data Updates:

• Weekly Delta Changes: Ensures the dataset is current with the latest changes.
• Monthly Full Refresh: Comprehensive update to maintain accuracy.

Use Cases:

• Market Analysis: Understand the distribution and types of healthcare providers across the US. Analyze market trends and identify potential gaps in healthcare services.
• Outreach: Create targeted marketing campaigns to reach specific types of healthcare providers. Use contact details for direct outreach and engagement with providers.
• Research: Conduct in-depth research on healthcare providers and their specialties. Analyze provider attributes to support academic or commercial research projects.
• Compliance and Verification: Verify provider credentials and compliance with state licensing requirements. Ensure accurate provider information for regulatory and compliance purposes.

Data Quality and Reliability:

• The dataset is meticulously curated to ensure high quality and reliability. Regular updates, both weekly and monthly, ensure that users have access to the most current information. The comprehensive nature of the data, combined with its regular updates, makes it a valuable tool for a wide range of applications in the healthcare sector.

Access and Integration: - CSV Format: The dataset is provided in CSV format, making it easy to integrate with various data analysis tools and platforms. - Ease of Use: The structured format of the data ensures that it can be easily imported, analyzed, and utilized for various applications without extensive preprocessing.

Ideal for:

• Healthcare Professionals: Physicians, nurses, and other healthcare providers who need to verify information about their peers.
• Analysts: Data analysts and business analysts who require detailed and accurate healthcare provider data for their projects.
• Businesses: Companies in the healthcare sector looking to understand market dynamics and reach out to providers.
• Researchers: Academic and commercial researchers conducting studies on healthcare providers and services.

Why Choose This Dataset?

• Comprehensive Coverage: Detailed information on millions of healthcare providers across the US.
• Regular Updates: Weekly and monthly updates ensure that the data remains current and reliable.
• Ease of Integration: Provided in a user-friendly CSV format for easy integration with your existing systems.
• Versatility: Suitable for a wide range of applications, from market analysis to compliance and research.

By leveraging the US Healthcare NPI & Taxonomy Data, users can gain valuable insights into the healthcare landscape, enhance their outreach efforts, and conduct detailed research with confidence in the accuracy and comprehensiveness of the data.

Summary:

• This dataset is an invaluable resource for anyone needing detailed and up-to-date information on US healthcare providers. Whether for market analysis, research, outreach, or compliance, the US Healthcare NPI & Taxonomy Data offers the detailed, reliable information needed to achieve your goals.

IntroductionAnxiety and depression are major causes of disability in Arab countries, yet resources for mental health services are insufficient. Mobile devices may improve mental health care delivery (mental m-Health), but the Arab region's mental m-Health app landscape remains under-documented. This study aims to systematically assess the features, quality, and digital safety of mental m-Health apps available in the Arab marketplace. We also contrast a set of recommended Australian apps to benchmark current strategies and evidence-based practices and suggest areas for improvement in Arabic apps.MethodsFifteen Arab country-specific iOS Apple Stores and an Android Google Play Store were searched. Apps that met the inclusion criteria were downloaded and evaluated using the Mobile App Rating Scale (MARS) and the Mobile App Development and Assessment Guide (MAG).ResultsTwenty-two apps met the inclusion criteria. The majority of apps showed no evidence of mental health experts being involved in the app design processes. Most apps offered real-time communication with specialists through video, text, or audio calls rather than evidence-based self-help techniques. Standardized quality assessment showed low scores for design features related to engagement, information, safety, security, privacy, usability, transparency, and technical support. In comparison to apps available in Australia, Arabic apps did not include evidence-based interventions like CBT, self-help tools and crisis-specific resources, including a suicide support hotline and emergency numbers.DiscussionIn conclusion, dedicated frameworks and strategies are required to facilitate the effective development, validation, and uptake of Arabic mental mHealth apps. Involving end users and healthcare professionals in the design process may help improve app quality, dependability, and efficacy.

Context

Google Play stores top 500 app data based on their rankings on January 2022 for all the available categories. Link to scraping code: https://github.com/Shakthi-Dhar/AppPin Link to backup datafiles: github data files

Content

The dataset contains the top 500 android apps available on the google play store for the following categories: All Categories, Art & Design, Auto & Vehicles, Beauty, Books & Reference, Business, Comics, Communication, Education, Entertainment, Events, Finance, Food & Drink, Health & Fitness, House & Home, Libraries & Demo, Lifestyle, Maps & Navigation, Medical, Music & Audio, News & Magazines, Parenting, Personalization, Photography, Productivity, Shopping, Social, Sports, Tools, Travel & Local, and Video Players & Editors.

The app rankings are based on google play store app rankings for January 2022.

Abbreviations

In Review and Downloads, the alphabet T, L, Cr represents Thousands, Lakhs, Crores as per the google play store naming convention. They are similar to M, B which represent millions, billions. 1L (1 Lakh) = 100T (100 Thousand) 10L (10 Lakhs) = 1M (1 Million) 1Cr( 1 Crore) = 10M (10 Million)

Acknowledgements

This data is not provided directly by Google, so I used Appium an automation tool with python to scrape the data from the google play store app.

Inspiration

Inspired by Fortune500. Fortune500 provides data on top companies in the world, so why not have a data source for top apps in the world.

People with suicidal ideation and non-suicidal self-injury (NSSI) behavior face numerous barriers to help-seeking, which worsened during the COVID-19 pandemic. Mobile health applications (MHA) are discussed as one solution to improve healthcare. However, the commercial app markets are growing unregulated and rapidly, leading to an inscrutable market. This study evaluates the quality, features, functions, and prevention strategies of MHA for people with suicidal ideation and NSSI. An automatic search engine identified MHA for suicidal behavior and NSSI in the European commercial app stores. MHA quality and general characteristics were assessed using the Mobile Application Rating Scale (MARS). MHA of high quality (top 25%) were examined in detail and checked for consistency with established suicide prevention strategies. Of 10,274 identified apps, 179 MHA met the predefined inclusion criteria. Average MHA quality was moderate (M = 3.56, SD = 0.40). Most MHA provided emergency contact, but lacked security features. High-quality MHA were broadly consistent with the best-practice guidelines. The search revealed apps containing potentially harmful and triggering content, and no randomized controlled trial of any included MHA was found. Despite a large heterogeneity in the quality of MHA, high-quality MHA for suicidal behavior and NSSI are available in European commercial app stores. However, a lack of a scientific evidence base poses potential threats to users.

ANN sensitivity (excluding description text data).

Cloud Electronic Health Records Market Outlook

According to the latest research conducted in 2025, the global cloud electronic health records (EHR) market size stands at USD 7.3 billion in 2024. The market is exhibiting robust momentum, driven by the accelerating digital transformation in healthcare, with a compound annual growth rate (CAGR) of 13.2% projected through the forecast period. By 2033, the market is anticipated to reach approximately USD 22.3 billion, highlighting the increasing adoption of cloud-based solutions across healthcare organizations globally. The primary growth factor fueling this expansion is the urgent need for interoperable, scalable, and cost-effective health information management systems, as healthcare providers strive to enhance patient care, streamline clinical workflows, and comply with evolving regulatory mandates.

The surge in demand for cloud EHR solutions is fundamentally underpinned by the global shift toward value-based healthcare and the growing emphasis on patient-centric care models. Healthcare organizations are increasingly recognizing the necessity of real-time access to patient data, not only for improving clinical decision-making but also for enhancing care coordination among multidisciplinary teams. The cloud-based architecture offers unparalleled advantages in terms of data accessibility, scalability, and integration capabilities, which are crucial for supporting telemedicine, population health management, and remote patient monitoring initiatives. Furthermore, the increasing prevalence of chronic diseases and the aging global population necessitate robust data management platforms, further fueling the adoption of cloud EHR systems.

Another significant growth driver is the rapid advancement in cloud computing technologies and the proliferation of health IT infrastructure. The integration of artificial intelligence (AI), machine learning, and advanced analytics into cloud EHR platforms is transforming the way healthcare data is captured, analyzed, and utilized. These technological innovations enable healthcare providers to derive actionable insights from vast datasets, optimize resource allocation, and personalize treatment plans. Additionally, the growing adoption of mobile health applications and wearable devices is generating a wealth of patient-generated health data, which can be seamlessly integrated into cloud EHR systems for holistic patient management. The flexibility and cost-efficiency offered by cloud deployment models are compelling even small and medium healthcare organizations to transition from legacy on-premises systems to cloud-based EHR solutions.

On the regulatory front, governments and healthcare authorities worldwide are implementing stringent data protection and interoperability standards, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States and the General Data Protection Regulation (GDPR) in Europe. These regulatory frameworks are compelling healthcare providers to adopt secure, compliant, and interoperable EHR solutions, further propelling market growth. However, the market's expansion is not uniform across all regions. North America continues to dominate the global landscape, owing to its advanced healthcare IT ecosystem and favorable reimbursement policies, while Asia Pacific is emerging as a high-growth market driven by healthcare digitization initiatives and rising investments in health infrastructure.

Product Type Analysis

The cloud electronic health records market is segmented by product type into standalone EHR and integrated EHR solutions. Standalone EHR systems are designed to function independently, offering core functionalities such as patient record management, appointment scheduling, and basic reporting. These solutions are particularly attractive to smaller healthcare facilities and clinics that require a cost-effective and easy-to-deploy platform without the complexities of broader system integration. However, standalone systems often face limitations in terms of scalability and interoperability, which can hinder their long-term viability as healthcare organizations grow or seek to connect with external partners and health information exchanges.

Integrated EHR solutions, on the other hand, are rapidly gaining traction due to their ability to seamlessly connect with other healthcare information systems, including laboratory information systems (LIS), radiology information systems (RIS), billing platforms, a

Introduction: It has been 4 months since the discovery of COVID-19, and there have been many measures introduced to curb movements of individuals to stem the spread. There has been an increase in the utilization of web-based technologies for counseling, and for supervision and training, and this has been carefully described in China. Several telehealth initiatives have been highlighted for Australian residents. Smartphone applications have previously been shown to be helpful in times of a crisis. Whilst there have been some examples of how web-based technologies have been used to support individuals who are concerned about or living with COVID-19, we know of no studies or review that have specifically looked at how M-Health technologies have been utilized for COVID-19.Objectives: There might be existing commercially available applications on the commercial stores, or in the published literature. There remains a lack of understanding of the resources that are available, the functionality of these applications, and the evidence base of these applications. Given this, the objective of this content analytical review is in identifying the commercial applications that are available currently for COVID-19, and in exploring their functionalities.Methods: A mobile application search application was used. The search terminologies used were “COVID” and “COVID-19.” Keyword search was performed based on the titles of the commercial applications. The search through the database was conducted from the 27th March through to the 18th of April 2020 by two independent authors.Results: A total of 103 applications were identified from the Apple iTunes and Google Play store, respectively; 32 were available on both Apple and Google Play stores. The majority appeared on the commercial stores between March and April 2020, more than 2 months after the first discovery of COVID-19. Some of the common functionalities include the provision of news and information, contact tracking, and self-assessment or diagnosis.Conclusions: This is the first review that has characterized the smartphone applications 4 months after the first discovery of COVID-19.

Overview: This is a lab-based dataset with videos recording volunteers (medical students) washing their hands as part of a hand-washing monitoring and feedback experiment. The dataset is collected in the Medical Education Technology Center (METC) of Riga Stradins University, Riga, Latvia. In total, 72 participants took part in the experiments, each washing their hands three times, in a randomized order, going through three different hand-washing feedback approaches (user interfaces of a mobile app). The data was annotated in real time by a human operator, in order to give the experiment participants real-time feedback on their performance. There are 212 hand washing episodes in total, each of which is annotated by a single person. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code.

This dataset is part on three dataset series all following the same format:

https://zenodo.org/record/4537209 - data collected in Pauls Stradins Clinical University Hospital

https://zenodo.org/record/5808764 - data collected in Jurmala Hospital

https://zenodo.org/record/5808789 - data collected in the Medical Education Technology Center (METC) of Riga Stradins University

Note #1: we recommend that when using this dataset for machine learning, allowances are made for the reaction speed of the human operator labeling the data. For example, the annotations can be expected to be incorrect a short while after the person in the video switches their washing movements.

Application: The intention of this dataset is to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control.

Statistics:

Frame rate: ~16 FPS (slightly variable, as the video are reconstructed from a sequence of jpg images taken with max framerate supported by the capturing devices).

Resolution: 640x480

Number of videos: 212

Number of annotation files: 212

Movement codes (in JSON files):

1: Hand washing movement — Palm to palm

2: Hand washing movement — Palm over dorsum, fingers interlaced

3: Hand washing movement — Palm to palm, fingers interlaced

4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked

5: Hand washing movement — Rotational rubbing of the thumb

6: Hand washing movement — Fingertips to palm

0: Other hand washing movement

Note #2: The original dataset of JPG images is available upon request. There are 13 annotation classes in the original dataset: for each of the six washing movements defined by the WHO, "correct" and "incorrect" execution is market with two different labels. In this published dataset, all incorrect executions are marked with code 0, as "other" washing movement.

Acknowledgments: The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309.

References: For more detailed information, see this article, describing a similar dataset collected in a different project:

M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038

Contact information: atis.elsts@edi.lv

Global cloud mobile backend as a service (BaaS) market was valued at USD 6.52 billion in 2024 and is predicted to reach USD 27.08 billion by 2034, with a CAGR of 15.3% between 2025 and 2034.

Inventory Time Series for Compugroup Medical SE & Co. KGaA. CompuGroup Medical SE & Co. KGaA provides e-health services in Germany, Western and Eastern Europe, North America, and internationally. It operates through Ambulatory Information Systems (AIS), Hospital Information Systems (HIS), and Pharmacy Information Systems (PCS) segments. The Ambulatory Information Systems segment develops and sells management software for registered physicians, medical care centers, and physician networks. This segment also offers solutions that cover all essential clinical, administrative, and billing functions, as well as internet and intranet solutions for the secure exchange of doctor and patient information; and data-driven products, software interfaces for information exchange, medical decision support tools, pharmaceutical and therapy databases, solutions for the insurance industry, and digital applications and mobile apps. Its Hospital Information Systems segment offers clinical and administrative solutions for the inpatient healthcare sector, including the facilitation of patient administration, resource and personnel management, medical-care documentation, billing, and financial and medical controlling; and clinical applications to support specialist departments, medical laboratories and radiology networks. This segment serves acute care hospitals, rehabilitation centers, welfare institutions, hospital networks, medical laboratories, and radiologists. The Pharmacy Information Systems segment provides administrative and billing-related software applications for pharmacies, which supports aspects of the supply chain for medication from procuring and shipping the medication, managing and controlling inventory, through to planning, and performing and monitoring retail activities. CompuGroup Medical SE & Co. KGaA was founded in 1987 and is headquartered in Koblenz, Germany.

Other-Stockholder-Equity Time Series for Compugroup Medical SE & Co. KGaA. CompuGroup Medical SE & Co. KGaA provides e-health services in Germany, Western and Eastern Europe, North America, and internationally. It operates through Ambulatory Information Systems (AIS), Hospital Information Systems (HIS), and Pharmacy Information Systems (PCS) segments. The Ambulatory Information Systems segment develops and sells management software for registered physicians, medical care centers, and physician networks. This segment also offers solutions that cover all essential clinical, administrative, and billing functions, as well as internet and intranet solutions for the secure exchange of doctor and patient information; and data-driven products, software interfaces for information exchange, medical decision support tools, pharmaceutical and therapy databases, solutions for the insurance industry, and digital applications and mobile apps. Its Hospital Information Systems segment offers clinical and administrative solutions for the inpatient healthcare sector, including the facilitation of patient administration, resource and personnel management, medical-care documentation, billing, and financial and medical controlling; and clinical applications to support specialist departments, medical laboratories and radiology networks. This segment serves acute care hospitals, rehabilitation centers, welfare institutions, hospital networks, medical laboratories, and radiologists. The Pharmacy Information Systems segment provides administrative and billing-related software applications for pharmacies, which supports aspects of the supply chain for medication from procuring and shipping the medication, managing and controlling inventory, through to planning, and performing and monitoring retail activities. CompuGroup Medical SE & Co. KGaA was founded in 1987 and is headquartered in Koblenz, Germany.

The Crop Disease Detection Dataset is a high-quality collection of plant images sourced from the well-known PlantVillage dataset. This dataset is designed for the detection and classification of crop diseases, enabling researchers and developers to build robust machine-learning models for precision agriculture. It consists of images of various plant species, each categorized into healthy and diseased classes, covering a wide range of crop conditions.

The dataset is referenced from the research paper PlantVillage Dataset for Visual Classification of Plant Diseases >(Hughes & Salathé, 2015), which has been widely used for training deep learning models such as CNNs, Vision Transformers, and Vision Mamba to identify plant diseases effectively.

With the increasing impact of plant diseases on agricultural productivity, this dataset serves as an essential resource for automated disease detection, early intervention systems, and AI-powered precision farming solutions.

References: 1. Hughes, David, and Marcel Salathé. "An open access repository of images on plant health to enable the development of mobile disease diagnostics." arXiv preprint arXiv:1511.08060 (2015).

How to Use This Dataset? 1. Train deep learning models (CNNs, ResNet, Vi1. Ts, Vision Mamba) for disease classification. 2. Use data augmentation and transfer learning to enhance model accuracy. 3. Deploy AI models for real-time crop monitoring using edge devices or mobile applications. 4. Perform comparative studies on different AI architectures for agriculture-based disease prediction. 5. Utilize this dataset in academic research and industry applications for sustainable farming.

Purpose and Significance: The Crop Disease Detection Dataset plays a crucial role in addressing global food security challenges by enabling AI-driven disease diagnosis in crops. Early detection of diseases can help farmers take preventive measures, reduce pesticide usage, and enhance crop yield. By leveraging computer vision and AI, this dataset contributes to the development of cost-effective, scalable, and automated solutions for precision agriculture, helping bridge the gap between technology and farming.

Dataset for a novel study which examines technologically-assisted interventions in South Africa for psychological and neurological treatments from 1996 to 2021. Using a PRISMA-ScR search across 14 databases, 13 studies were selected, revealing trends: concentration in populous or economically influential provinces, increasing accessibility, diverse participant considerations, and a notable gap in South African literature, primarily focusing on telemedicine. The prevalence of telemedicine underscores its role in mobile health (mHealth) interventions, addressing healthcare delivery challenges in rural areas. This study provides a concise overview of technologically-assisted interventions in South Africa, highlighting current trends and suggesting avenues for further research.

Internal consistency of the MARS.

Here are a few use cases for this project:

1. Diet App: Utilize MealSynth to analyze food images for meal planning, portion control, and dietary restriction applications. Users could simply take photos of their meals, and the app could provide nutritional information based on the identified ingredients.

2. Grocery Shopping: Implement the model in a mobile app that suggests needed ingredients for certain meals. Users can input a picture of their desired meal and the app gives them a list of ingredients to buy at the grocery store.

3. Cooking Tutorials: Create an interactive cooking tutorial application that uses MealSynth to guess the ingredients in photos of different steps of cooking meals. Users could compare their work-in-progress to the reference picture and even get real-time advice.

4. Restaurants and Cafes: Use the model to develop a digital interactive menu in restaurants. When customers take a photo of the displayed food image, the model identifies the ingredients and explains them to the customers. It could also provide suggestions for similar dishes based on the identified ingredients.

5. Health and Fitness Apps: Integrate the model into fitness apps aimed at tracking the user's calorie and/or ingredient intake. With a food photo, the model could provide a fairly accurate estimate of consumed calories and nutrients.