Policy Document · 2026-08-01
2026 Electricity Statement of Opportunities for the National Electricity Market
Open the record to view the available source metadata.
Scientific Paper
A Study on Energy Consumption in AI-Driven Medical Image Segmentation
Prajwal et al. · 2025-05-26
What it covers
This study evaluates the energy consumption of AI models used in medical image segmentation and finds that Depthwise Convolution with Mixed Precision offers the most energy-efficient training configuration, highlighting the need for GPU-aware, sustainable AI practices.
Who it helps
Intended audience has not been documented.
Limitations
Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.
Show citation
Prajwal et al.. 2025-05-26. A Study on Energy Consumption in AI-Driven Medical Image Segmentation. https://www.mdpi.com/2313-433X/11/6/174 (AI & Environment Resource Hub; record paper-286; collection snapshot 2026-10-04).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
lifecycle
orientation
Available source metadata
- Author or creator
- Prajwal et al.
- Publisher
- MDPI
- Publication date
- 2025-05-26
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source access
- Reachable at last check
- Metadata confidence
- Metadata unconfirmed
- Last verified
- Not yet verified
- Snapshot import
- 2026-09-06
- Legacy domain
- Energy
- Legacy subdomain
- Green AI and Energy Costs in Health & Medical Imaging
- journal
- MDPI
Provenance and review
- Source access
- Reachable at last check
- Metadata confidence
- Metadata unconfirmed
- Editorial relevance
- Relevance has not been separately reviewed.
- Evidence quality
- Evidence quality has not been separately assessed.
Source sheet: Scientific Paper · Row 290 · Original ID: paper-286.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-28. Metadata not yet corroborated. The import date is not the original date added.
Suggest a correction for this record →Newsletter appearances
Publication, placement, and evidence are tracked separately. A legacy coverage flag alone does not prove an appearance.
No issue-specific evidence recorded.
Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Energy and electricity
Shared topics: Energy and electricity
Policy Document · 2026-05-01
24/7 Renewables: The Economics of Firm Solar and Wind
Open the record to view the available source metadata.
Shared topics: Energy and electricity
Scientific Paper · 2026-09-15
A Strategic Roadmap for Sustainable Data Center Deployment
This article proposes a five-step state policy roadmap for data center development: conduct environmental and community impact studies, use the findings in siting decisions, reassess incentives, require large facilities to pair with off-grid clean energy and storage, and mandate community-benefit sharing. It focuses on protecting grid reliability, electricity affordability, environmental integrity, and host communities.
Shared topics: Energy and electricity
Scientific Paper · 2023-05-23
A Systematic Review of Green AI
A systematic review of 98 studies shows that Green AI research has matured rapidly since 2020, focusing on energy-efficient training, model benchmarking, and footprint monitoring though real-world tool adoption remains limited.