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TRUVACE RECORD VERSION
record: TRV-2026-0972
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-03T06:03:19.101375Z
status: published
lens: p_space
sector: health
headline: Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold
dek: Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks. When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide pre…
gain_title: (none)
problem_title: Results Yet, challenges persist, including algorithmic bias, data inequity and variable regulatory standards across regions.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: Results Yet, challenges persist, including algorithmic bias, data inequity and variable regulatory standards across regions.
problem_evidence: (none)
quick_read: Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks.

When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies. Nevertheless, the rapid diffusion of AI in health care also raises profound ethical, regulatory and social challenges, since only a small fraction of AI tools have achieved routine clinical use, often due to limited generalizability, opaque algorithms and workflow incompatibility.
limitation: 
tag: Evidence-backed problem
key_points: Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. | Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks. | When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies.
rundown: Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks.

When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies. Nevertheless, the rapid diffusion of AI in health care also raises profound ethical, regulatory and social challenges, since only a small fraction of AI tools have achieved routine clinical use, often due to limited generalizability, opaque algorithms and workflow incompatibility.
sources:
- peer_reviewed | European Journal of Clinical Investigation | https://doi.org/10.1111/eci.70254 | 2026-09-01
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