The problem EZMED solves

Accessibility: Our website is accessible 24/7, enabling users to seek medical advice at any time, day or night, without waiting for appointments or visiting hospitals or clinics.

Cost-Efficiency: Virtual doctors offer cost-effective healthcare solutions, reducing medical expenses associated with in-person consultations.

Timely Responses: Users receive instant feedback and medication recommendations, speeding up the process of diagnosis and treatment.

Reducing Healthcare Burden: By handling routine and minor health issues, EZMED alleviates the burden on healthcare facilities, allowing doctors to focus on more critical cases.

Data-Driven Insights: Our platform gathers vast amounts of healthcare data, which can be used to identify health trends and enhance medical research.

Challenges and Concerns

While EZMED recommendation holds great promise, it also faces certain challenges and concerns:

Accuracy: Ensuring the accuracy of diagnoses and medication recommendations remains a critical concern. Incorrect recommendations could lead to serious health risks.

Privacy and Data Security: Collecting and storing user health data must be done securely to protect user privacy and comply with healthcare regulations.

Liability Issues: Determining responsibility in case of adverse effects from AI-recommended medications can be legally complex.

Overreliance on AI: There's a risk that users may rely too heavily on virtual doctors, potentially neglecting the importance of physical check-ups and expert medical opinions.

Lack of Personalized Care: EZMED may not fully capture the nuances of individual health conditions and may provide generic recommendations that don't consider unique patient circumstances.

Conclusion

AI medication recommendation websites represent a significant advancement in healthcare accessibility and efficiency. They have the potential to make healthcare more accessible, cost-effective, and timely.

Challenges we ran into

Using Fastn was a real bummer. Although we had the knowledge of using Fastn framework earlier it was a really difficult job to deploy Fastn in this big-scale product. We ran into many errors but at the end we w ere able to make a big picture out of the mess we created earlier.