From Algorithms to Architecture: Why Model Serving Is the Strategic Bottleneck in Healthcare AI Platforms
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2025
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| _version_ | 1866901748088569856 |
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| author | Lohrasbi, Nader |
| author_facet | Lohrasbi, Nader |
| contents | <p>The prevailing discourse in healthcare AI has been disproportionately focused on algorithms, datasets, and model accuracy. This technological monomania has generated an illusion of progress: we assume that superior models will inevitably transform clinical workflows. However, this assumption collapses upon operational contact. Models do not create value unless they can be served—reliably, predictably, and at clinical speeds. This paper argues that the true strategic differentiator in healthcare AI is no longer model construction but model serving: the infrastructural and architectural capability that converts algorithmic potential into actionable clinical cognition at scale. Drawing on concepts such as Dynamic Inference Graphs, multi-tenant inference orchestration, latency governance, and trust preservation, we reinterpret healthcare AI as an architectural and systems challenge rather than a mathematical contest. The market leaders of the next decade will not be those who build the smartest models, but those who deliver intelligence without erosion of clinical trust.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17867240 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Algorithms to Architecture: Why Model Serving Is the Strategic Bottleneck in Healthcare AI Platforms Lohrasbi, Nader Model Serving Healthcare AI Latency Engineering Digital Nervous System Architecture Dynamic Inference Graphs <p>The prevailing discourse in healthcare AI has been disproportionately focused on algorithms, datasets, and model accuracy. This technological monomania has generated an illusion of progress: we assume that superior models will inevitably transform clinical workflows. However, this assumption collapses upon operational contact. Models do not create value unless they can be served—reliably, predictably, and at clinical speeds. This paper argues that the true strategic differentiator in healthcare AI is no longer model construction but model serving: the infrastructural and architectural capability that converts algorithmic potential into actionable clinical cognition at scale. Drawing on concepts such as Dynamic Inference Graphs, multi-tenant inference orchestration, latency governance, and trust preservation, we reinterpret healthcare AI as an architectural and systems challenge rather than a mathematical contest. The market leaders of the next decade will not be those who build the smartest models, but those who deliver intelligence without erosion of clinical trust.</p> |
| title | From Algorithms to Architecture: Why Model Serving Is the Strategic Bottleneck in Healthcare AI Platforms |
| topic | Model Serving Healthcare AI Latency Engineering Digital Nervous System Architecture Dynamic Inference Graphs |
| url | https://doi.org/10.5281/zenodo.17867240 |