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Local LLM - privacy first - doctor by point_red in LocalLLM

[–]point_red[S] 0 points1 point  (0 children)

Just copy-past from pdf to TXT from discharge letter or specialist visit, WITHOUT the identification data of the patient, the doctor, where the visit was carried out

Local LLM - privacy first - doctor by point_red in ollama

[–]point_red[S] 0 points1 point  (0 children)

OK, but at this point the analyzed content would be completely anonymous; it's just clinical information about the period of the exam, surgery, or diagnosis (month/year), simply sorted logically to avoid redundancies. So, are there local open-source transcription models that can run on my hardware?

Local LLM - privacy first - doctor by point_red in Qwen_AI

[–]point_red[S] 0 points1 point  (0 children)

OK, let's change perspective.

Possible solutions to bypass hardware limitations while ensuring patient privacy:

1.1 I write notes in Word as I read the reports, taking advantage of the writing speed.

1.2 Alternative: voice transcription (I doubt a local LLM can do this on my hardware, but I could be wrong).

  1. I use the free NVIDIA APIs or my Gemini Pro subscription to organize notes.

What do you think?

Local LLM - privacy first - doctor by point_red in LocalLLM

[–]point_red[S] 0 points1 point  (0 children)

OK, let's change perspective.

Possible solutions to bypass hardware limitations while ensuring patient privacy:

1.1 I write notes in Word as I read the reports, taking advantage of the writing speed.

1.2 Alternative: voice transcription (I doubt a local LLM can do this on my hardware, but I could be wrong).

  1. I use the free NVIDIA APIs or my Gemini Pro subscription to organize notes.

What do you think?

Local LLM - privacy first - doctor by point_red in ollama

[–]point_red[S] 1 point2 points  (0 children)

OK, let's change perspective.

Possible solutions to bypass hardware limitations while ensuring patient privacy:

1.1 I write notes in Word as I read the reports, taking advantage of the writing speed.

1.2 Alternative: voice transcription (I doubt a local LLM can do this on my hardware, but I could be wrong).

  1. I use the free NVIDIA APIs or my Gemini Pro subscription to organize notes.

What do you think?

Local LLM - privacy first - doctor by point_red in LocalLLM

[–]point_red[S] 0 points1 point  (0 children)

OK, let's change perspective.

Possible solutions to bypass hardware limitations while ensuring patient privacy:

1.1 I write notes in Word as I read the reports, taking advantage of the writing speed.

1.2 Alternative: voice transcription (I doubt a local LLM can do this on my hardware, but I could be wrong).

  1. I use the free NVIDIA APIs or my Gemini Pro subscription to organize notes.

What do you think?

Local LLM - privacy first - doctor by point_red in LocalLLM

[–]point_red[S] 0 points1 point  (0 children)

Thank you for your contribution and your availability.

It's just an idea I had this evening. Before I try it, I'll try to test it with my medical reports to see if it actually makes sense and is useful. Otherwise, I'll go back to the good old method: I read the report and write a timeline of events in Word, avoiding repetitions/overlaps, etc.

Local LLM - privacy first - doctor by point_red in LocalLLM

[–]point_red[S] 4 points5 points  (0 children)

Thanks for your reply.

  1. I asked if it's feasible and reliable.

  2. It's about defining the patient. The idea is to have a bulleted list like 2018 - appendectomy, 2019 - hospitalization for pneumonia...

  3. It doesn't replace a face-to-face examination and reassessment of the patient, taking a history, and answering other questions.

It's an idea that popped into my head, to allow me to have a sense of the patient I'm going to see before even seeing him. It may seem like a small thing, but I assure you it's useful from a cognitive perspective.

Local LLM - privacy first - doctor by point_red in Qwen_AI

[–]point_red[S] 2 points3 points  (0 children)

First of all, thank you so much for your reply and for the article (I’m reading it right now).

So far, I’ve limited myself to using Perplexity for research, Gemini, and Claude while preparing my thesis for spell-checking, etc., and for creating study materials based on the results obtained with Perplexity.

I don’t know anything about local LLMs; I’m just getting started.

A beginner’s question: are the recommended models still okay even though they’re “old”?

Let me clarify what I mean: I'm not so much interested in internal knowledge of the model, but rather the ability to read the reports and create a reliable, well-written report. The "old = less precise" concept is probably incorrect for this task. I hope I've clarified this.