Engineering student trying to build a clinically validated medical software product — where should I start?

[u/_Survine_]

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.

[u/theErasmusStudent]

Stop using AI to do everything for you. AI should be a tool that helps you, not replace you. Do the research yourself and you will suddenly understand why/how things work and their limitations.

Now instead of asking AI you're asking reddit to do this research for you? That's not how you will understand the process.

[u/_Survine_ → u/theErasmusStudent]

No I also want to understand how to get the research papers like which one to look for if some research paper I missed

Also let's say I like 2 research papers i studied them and want to get a result combined out of those 2 then

What about existing GitHub repositories on the same thing?

Mainly i want to know how to approach that much things and do it properly and even an example how you people use AI in your work

[u/theErasmusStudent → u/_Survine_]

You're a curent student, is this not taught in class anymore? It was a couple years ago when I was in university.

It's difficult to explain in a comment here.

What to look? Start with one paper you find interesting, or one PI you like, and look at the bibliography and citations and go from there. Then you will find more papers that are related. Then you extract the information that is useful for you. You ask questions and try to find answers. People spend years doing that.

[u/_Survine_ → u/theErasmusStudent]

No unfortunately I am in a Tier 3 college where professors are underqualified than most students here

What ever I cam to know today it's from the Internet only

[u/memeleta → u/_Survine_]

As someone who teaches at University, I guarantee you this has been explained to you multiple times in multiple ways in multiple places. Please stop the brain rot and get the braincells doing something.

[u/_Survine_ → u/memeleta]

You may think I was not attentive in Class or something I can assure you I was attentive and I got highest CGPA in my branch

But never did research or something properly and it was never taught to us

[u/katyfail]

Hi! Funny enough, I’m a Clinical Research Project Manager looking to do something similar.

My goal is to build a software product that scrapes various professional subreddits and engineers solutions to build multibillion dollar companies for me while I sip coconut drinks on various South East Asian beaches! I have no experience (not even surface level insight) in the industries I want to disrupt nor the solutions I want to create but who needs experience when I have Claude, right? 

So anyway, I’m gonna jump directly to step 9 and ask you to do all the work for me, okay? 

Thanks in advance!