Bibliographic record
Abstract
Abstract Background: The maturation of imaging modalities, radiology, pathology, cardiology, genomics-integrated diagnostics, and nuclear medicine, into standardized, data-rich domains has uniquely positioned them as the foundational layer for precision medicine. Their digital transformation, coupled with harmonized data standards such as Digital Imaging and Communications in Medicine and Fast Healthcare Interoperability Resources, has enabled these modalities to be computationally tractable by modern artificial intelligence (AI) architectures. Approach: This article examines how state-of-the-art artificial intelligence, spanning convolutional neural networks, vision transformers, multimodal deep learning, federated learning, and foundation models, is being operationalized within medical imaging domains. The analysis includes deployment strategies across clinical workflows, infrastructure requirements, and interoperability frameworks that support AI scalability. Special attention is given to regulatory, financial, and operational challenges associated with enterprise-level deployment. Results: Globally, leading institutions and national health systems are transitioning from pilot AI models to full-stack diagnostic platforms, with imaging-AI pipelines augmenting throughput, accuracy, and therapeutic precision. The evolution toward video-native diagnostics and continuous multimodal monitoring indicates a trajectory where imaging serves not only as a retrospective diagnostic tool but as a real-time predictive engine. Conclusion: Imaging is no longer an adjunct to diagnosis, it is emerging as the computational core of integrated, AI-first precision medicine. Health systems and executives who prioritize imaging-AI convergence are better positioned to unlock high-throughput, individualized care at scale.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".