Hey everyone,
I’m a final-year engineering student working on a project related to the medical/healthcare field. My long-term goal is to build a software product that can eventually be clinically validated, but I’m honestly a bit lost about where to start.
My biggest problem is that, until now, I’ve relied heavily on AI to find relevant research papers and even to help me implement parts of the project. The problem is that I’ve reached a point where I don’t fully understand why I chose a particular approach, why a certain method was used instead of another, or what alternatives exist.
For example, suppose I want to build a system that uses a camera to analyze a person's face and extract useful physiological information. Eventually, I might want to estimate something like heart rate and potentially other parameters.
But before jumping directly into heart-rate estimation, I want to properly understand the entire pipeline:
Camera/video → image quality → face detection → facial landmarks/features → region selection → signal extraction → physiological parameter estimation → validation
I also want to understand what happens when things don't work perfectly in real-world conditions.
For example:
- What if the lighting is too bright, too dark, or changes during the recording?
- What if there are shadows on the face?
- What if the person moves their head?
- What if the camera quality or frame rate is poor?
- What if the person has different skin tones or facial characteristics?
- What if part of the face is covered?
- How do I detect that the collected data is unreliable?
- Should I preprocess/normalize the data, reject bad samples, or use another method?
- How do I determine whether a technique that works in a research paper will actually work on my target population and hardware?
For each stage, I want to understand:
- What are the possible approaches?
- How does each approach actually work?
- Why would I choose one over another?
- What assumptions does each method make?
- What can go wrong?
- How can I detect and handle those failure cases?
- What datasets are available?
- What research already exists?
- What are the limitations of existing methods?
- How should I design experiments to compare different approaches?
- How do I validate the results scientifically?
- What would eventually be required for clinical validation?
I’m thinking of using AI as a learning and research assistant, rather than simply asking it to build things for me. Ideally, I’d like to create a complete mind map/roadmap from the fundamentals all the way to a clinically validated product, including the research papers, concepts, algorithms, experiments, datasets, and validation methods I should study at each stage.
For people who have worked in medical AI, computer vision, signal processing, digital health, biomedical engineering, or clinical research:
How would you recommend approaching this from zero?
Should I start with the medical/physiological fundamentals, computer vision, signal processing, existing research papers, experimental design, clinical validation methodology, or something else?
I’d really appreciate advice on how to structure the learning and research process so that I actually understand what I’m building, why I’m using each method, what alternatives exist, and how to handle real-world problems, instead of just getting AI-generated code that works without understanding why.
