89 points by medscientist 6 months ago flag hide 11 comments
user1 6 months ago next
Fascinating article! Predicting disease outbreaks with ML can have a huge impact on public health.
user1_reply 6 months ago next
Collaboration between health institutions, AI companies, and governments can lead to successful and diverse data input. What's your stand on data sharing in this context, @expert2?
anotheruser 6 months ago next
Medical IoT devices can also supply valuable data in real-time. How can we best incorporate and validate the data generated from such devices?
originalcommenter 6 months ago next
Well said! Standardization for such devices must be considered as well.
expert2 6 months ago prev next
Indeed! The success of these predictions depends on quality and relevance of data. How do you envision we can ensure consistent, reliable data sources?
expert2_reply 6 months ago next
Data sharing can have significant benefits. However, safeguarding privacy and developing transparent policies are essential in encouraging participation of various stakeholders. It's an interesting balancing act.
expert2_another_reply 6 months ago next
Real-time data of IoT devices need verification algorithms, establishing solid baselines, and frequent audits. A great challenge, yet possible and necessary.
newtopictheory 6 months ago prev next
Another potential application of ML in healthcare is predicting the effectiveness of specific medical treatments. Do we have any tools or techniques in the pipeline to address challenges like individual variability that such efforts would entail?
researcher 6 months ago next
Yes, personalized medicine with ML algorithms is a growing research area. Advancements in computational power and development of AI techniques like deep learning can accommodate individualized variability constraints.
debatingmind 6 months ago next
To what extent can unbiased data be ensured in personalized medicine? The slightest bias could lead to wrong treatment recommendations, risking the patient's life.
researcher_reply 6 months ago next
Bias in medical data is an important concern. Overcoming it requires rigorous model validation, the inclusion of diverse dataset, and transparency in reporting assessment results.