Building MedReviewAI.com: My Journey and Challenges in Creating an OpenAI-based Medical Review Platform

Creating MedReviewAI.com has been a labor of both innovation and grit, as I’ve tackled one technical challenge after another to build a platform that can analyze complex medical records using AI. My goal has been to simplify the process for legal professionals, insurance adjusters, and even individuals to get concise, clear summaries from extensive medical records. But building something this ambitious hasn’t come without its hurdles. Here’s a glimpse into my journey, the choices I made, and the obstacles I’ve had to overcome.

Switching from Python to C++ for a More Stable Backend

Initially, I chose Python as the primary programming language for the backend. Python is widely used in machine learning and data processing due to its extensive libraries and ease of integration with OpenAI’s API. However, as I started implementing more complex features, I quickly ran into performance bottlenecks. Python was fast enough for testing but couldn’t handle the scale or stability required for a live platform that processes large volumes of data in real time.

Several issues became apparent:

• Unreliable Libraries: Many Python libraries, though powerful, were not stable enough for the demanding workload MedReviewAI required. The libraries would occasionally fail or produce inconsistent results, which was unacceptable for a platform that has to be highly accurate and dependable.
• Performance Issues: Python’s nature as an interpreted language made it slower in processing data-heavy backend operations. This led to delays and frustrations during testing, as some processes would take longer than expected, which isn’t ideal when dealing with time-sensitive data like medical records.
• Memory and Processing Constraints: Python struggled to efficiently manage memory for large files and datasets, especially under continuous use with multiple concurrent users.

After assessing the limitations, I made a significant decision: I switched the backend to C++. The transition wasn’t easy, as it required rewriting large portions of the code and learning new optimizations specific to C++. However, the benefits were clear:

• Speed and Efficiency: C++ provided the performance boost that was necessary for real-time data processing. Its compiled nature meant fewer delays, and it could handle the heavy lifting required to process large files.
• Stability: C++ libraries, while sometimes more challenging to implement, were far more stable for the requirements of MedReviewAI. I could now rely on more consistent performance, which improved the overall reliability of the platform.

This switch was a critical turning point, and it gave me the backend strength I needed to build a robust platform. Though it was a time-consuming adjustment, the stability and performance gains made the extra effort worthwhile.

Reducing OpenAI Token Usage with In-House AI-based OCR in C++

One of the biggest challenges in developing MedReviewAI was the cost associated with OpenAI’s token usage. Submitting entire PDFs to OpenAI for analysis quickly became a token drain, making the project financially unsustainable. I needed a way to reduce the amount of data being processed without sacrificing the detail and accuracy of the analysis.

At first, I explored ways to preprocess the PDFs in Python by selectively extracting text sections that I wanted to submit to OpenAI. However, this approach wasn’t as effective as I’d hoped. The pre-built OCR solutions I tried were either inaccurate or couldn’t consistently format the extracted data, and Python’s speed limitations only made the problem worse.

Developing a Custom OCR Solution in C++

After much trial and error, I decided to build my own AI-powered OCR software in C++. This OCR system had to:

1. Accurately Extract Relevant Data: I designed it to recognize and extract key information like patient names, dates, and specific medical findings. This way, I wouldn’t need to submit entire PDFs but could instead send only relevant data points.
2. Save Data in JSON Format: By storing the extracted data in a structured JSON format, I could optimize OpenAI’s token usage by submitting only the necessary sections. This not only streamlined the analysis process but also significantly reduced costs.
3. Improve Accuracy and Flexibility: Building the OCR from scratch allowed me to tailor it specifically for medical documents, improving accuracy by focusing on the terminology and formatting typically found in these records.

This custom OCR solution became a game-changer for MedReviewAI. By selectively parsing PDFs and converting them to JSON, I was able to cut down on the data volume sent to OpenAI, lowering token usage without compromising the quality of the analysis.

Reflections and Lessons Learned

Developing MedReviewAI has been a journey filled with challenges and learning opportunities. Transitioning from Python to C++ was a difficult decision but one that paid off by making the platform faster and more stable. Building an in-house OCR was equally daunting, yet it became essential to making the platform financially viable.

Here are a few lessons I’ve learned along the way:

• Be Willing to Pivot: Technology decisions sometimes need to change mid-project, even if it means backtracking. My initial choice of Python was suitable for prototyping, but C++ ultimately proved to be the right tool for the job.
• Optimizing Costs Matters: Building a financially sustainable platform requires thinking beyond functionality. Developing a custom OCR minimized the token usage, proving that sometimes in-house solutions are the best approach.
• Precision is Key in Healthcare Data: Working with medical records means that accuracy is paramount. Custom solutions allowed me to tailor the platform specifically to the needs of legal and medical users, ensuring they receive reliable and consistent results.

I’m excited about what lies ahead for MedReviewAI. The journey hasn’t been easy, but each challenge has taught me valuable lessons that I’ll carry forward. By focusing on performance, cost efficiency, and accuracy, I believe MedReviewAI can become a trusted tool for analyzing medical records, helping users make informed decisions with greater speed and ease.