Approximately, 28% of Australians, or about 7 million people, live in rural and remote areas with limited access to healthcare. In the Asia-Pacific region, this number rises to half of the population, facing similar challenges, which often results in delayed diagnoses, untreated conditions, or even higher mortality rates. Many individuals in these regions either cannot afford medical consultations or lack awareness of available treatments.
To bridge this gap, I introduce MediBridge which is a remote diagnosis and treatment platform driven by Artificial Intelligence (AI), where users can choose symptoms, upload photos, and get real-time medical diagnosis for common health problems. The system can identify severe health conditions and, when needed, connect users with medical professionals to ensure they receive the appropriate care. MediBridge bridges the gap for people living in underserved and rural areas by making sure that healthcare is timely, accessible, and inclusive for everyone, no matter where they are.
Name : Shaivika Anand
Student Number : 48239192
User Profile Setup: First-time users are required to create a profile, which includes selecting preferred language for communication, adding personal details, any relevant medical history and granting location access to the app. Users can either manually add this or opt for the voice recognition feature in the app.
Symptom Entry: Users are prompted to enter their current symptoms through a series of interactive selections or they can opt to speak their symptoms, and the app will recognise them through voice recognition and add it to the system.
Image Uploading and Analysis: For some conditions, users can be asked to upload images. These images are processed by the AI model for a proper diagnosis. The system takes both textual symptom description and uploaded image into consideration while providing with a consultation.
Medical Advice and Recommendations: On the basis of the symptoms provided by the user, their medical history, and uploaded images, MediBridge will offer a diagnosis with possible medicine recommendation, or treatments that the user can take.
Remote Consultations with Medical Professionals: In the case of severe conditions, the app will connect the user to a medical professional for an in-app video consultation. This would make sure that user receive professional advice instead of relying only on AI-generated recommendation to avoid any potential misdiagnoses.
Location-Based Pharmacy Search: Once the user receives medical advice or a prescription, MediBridge connects the user to its nearest pharmacy up to 25 km distance ) where they can purchase the medication at a discounted price.
Voice Recognition: Users can choose to enable voice recognition at any time while using the app, allowing them to navigate easily and access all features simply by speaking.
The scope for the MVP will include the features:
Reliability – The platform is providing medical advice and consultation, hence, it is important that the system is able to deliver correct and up-to-date medical diagnosis. Any delay and failure to provide a correct diagnosis or to connect users with a medical professional in the case of severe conditions could lead to major problems for the user’s health. Therefore, the system must ensure reliability in its diagnostic capabilities.
Availability - It is important for system to be available 24/7 so that the users can access healthcare services anytime. It should operate consistently, handle user traffic and network conditions, to provide an on-demand medical advice.
Interoperability – As MediBridge integrates with pharmacies, medical professionals, and potentially other healthcare systems, it is important to have easy access of medical data between different components.
Security – The system handles sensitive medical data, including user profiles, health records, and image uploads, which would require security to protect user privacy and compliance with regulations.
Scalability – The platform must be capable of handling a high volume of concurrent requests and should also support technical updates or new feature integration to help with a better diagnosis.
Reliability: To test the reliability, the system would run test cases on multiple inputs to check if the model provides correct diagnosis. Medical professionals will also cross-validate generated diagnosis against real-world cases. For this, we can use Amazon SageMaker to train and refine AI models. We will also use AWS CloudWatch to monitor system logs and errors, which would allow us to track misdiagnoses and improve the system’s reliability by feedback from medical professionals.
Availability: To evaluate this, we will host the application on AWS Elastic Beanstalk, which would enable automatic scaling to accommodate high demand. AWS Route 53 will also be used to route users to the nearest and most responsive server, minimising latency. We can also use AWS CloudWatch to monitor uptime and detect any service disruptions.
Interoperability: To facilitate an easy share of information between different components of the system, we will use AWS API Gateway, allowing communication between different services. AWS HealthLake will store and process medical data in a standardised format, making it easy for external healthcare providers to access patient information when needed.
Security: All the personal data for a user, medical history, personal details will be encrypted using AWS Key Management Service. We will implement AWS Identity and Access Management (IAM) to restrict access to authorised persons only, and AWS Shield will help to provide protection against cyberattacks.
Scalability: In order for the system to handle increased demand, AWS Auto Scaling will be used which would help to compute the resources dynamically. Medical records can be managed through the use of Amazon Relational Database Service (RDS) and Amazon S3 can be used to handle image uploads. AWS Load Balancer can also be used to distribute traffic evenly, preventing server overload.