Page 44 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
information, empowering individuals to make informed  Mobile health applications: Kumar et al. (2019)
decisions about their health. developed a mobile health application for diagnosing
cardiovascular diseases using ML algorithms
 Behavioral Change Advocacy: By promoting
awareness about the importance of routine screenings, While these studies demonstrate the potential of AI and ML
preventive measures, and healthy lifestyles, DigiVision in healthcare diagnostics, they are limited to specific disease
seeks to drive long-term behavioral changes within diagnosis or require specialized equipment. DigiVision builds
communities. upon this research by developing a multi-disease diagnostic
platform that leverages AI and ML for early disease detection
 Localized Content Delivery: The platform ensures that and public awareness.
health education materials are culturally sensitive and
available in multiple languages to reach diverse 4. Proposed Work
populations. The proposed work aims to further develop DigiVision into a
comprehensive platform capable of addressing diverse
C. Community-Centric Deployment healthcare needs. Key objectives include:
 Partnerships with Local Stakeholders: DigiVision
emphasizes collaboration with governments, NGOs, and A. Expand Diagnostic Capabilities
local healthcare providers to tailor its deployment  Develop and enhance AI algorithms to enable accurate
strategies to the specific needs of each community. multi-disease detection, including non-communicable
diseases (e.g., diabetes, hypertension) and infectious
 Capacity Building: The platform incorporates a “Train-
diseases (e.g., tuberculosis, malaria).
the-Trainer” model to ensure that local healthcare
workers are equipped with the skills needed to operate  Incorporate diagnostic modules for region-specific and
and maintain the system effectively. rare diseases to address the unique needs of
underserved populations.
 Affordability and Accessibility: By minimizing costs
and leveraging mobile technology, DigiVision reduces B. Integrate Telemedicine Services
financial and logistical barriers to healthcare access.  Add teleconsultation features to connect users with
healthcare professionals for follow-up care and
The integration of these pillars ensures that DigiVision not
only addresses the immediate needs of disease diagnosis but treatment recommendations.
also contributes to the long-term sustainability of health  Enable remote monitoring by integrating wearable
systems and the empowerment of individuals and health devices to track and share real-time health data.
communities. This framework highlights the platform’s
potential to bridge critical gaps in healthcare delivery and C. Optimize User Experience and Accessibility
awareness.  Design a user-friendly interface with support for
multiple languages, cultural localization, and
3. Related Work accessibility features like text-to-speech and voice
Existing research demonstrates the potential of AI-driven commands.
tools in healthcare. Platforms like Ada Health and Babylon
Health have shown promising results in symptom-checking  Include educational resources to increase health literacy
and diagnostic support. However, these tools often focus on and encourage preventive health behaviors.
single-disease diagnostics and lack integration with D. Conduct Pilot Programs for Validation
community-specific needs. DigiVision differentiates itself by  Implement pilot deployments in both urban and rural
offering multi-disease diagnostic capabilities and a strong settings to assess the platform’s usability, effectiveness,
emphasis on public health awareness, addressing the and adaptability.
broader health disparities faced by underserved populations.
Numerous studies have explored the application of artificial  Gather feedback from end-users and stakeholders to
intelligence (AI) and machine learning (ML) in healthcare refine the platform and address challenges encountered
diagnostics: during testing.
 Deep learning-based diagnosis: Esteva et al. (2017) E. Ensure Scalability and Sustainability
demonstrated the effectiveness of deep learning  Develop cost-effective strategies to deploy DigiVision in
algorithms in diagnosing skin cancer . low-resource settings, leveraging partnerships with
governments and NGOs.
 Computer-aided detection: Rajpurkar et al. (2017)
developed a computer-aided detection system for  Build a robust infrastructure using cloud-based systems
diagnosing breast cancer from mammography images . and offline capabilities to support large-scale adopti
 Multi-disease diagnosis: Kermany et al. (2018) proposed
a deep learning-based approach for multi-disease
diagnosis from retinal fundus images

IJTSRD | Special Issue on Emerging Trends and Innovations in Web-Based Applications and Technologies Page 34