A survey of published research, from the Essentia Memoria newsroom. It lists real papers, each looked up at its DOI registry; the game has not repeated their results.
Research survey
What AI research has found: medicine and diagnosis
Happened: · Published:
5 published works on medicine and diagnosis, each looked up at its DOI registry before it was listed here, with what each one reports in plain words.
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Transcript
News from the Essentia Memoria game universe. What AI research has found: medicine and diagnosis. 5 published works on medicine and diagnosis, each looked up at its DOI registry before it was listed here, with what each one reports in plain words. This survey lists what each paper reports. The game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited. This report was written from the world record by a fixed template. The full story and its source records are on essentiamemoria.com.
The question
Where does a model read a medical image as well as a specialist, and where does it not?
Claims, and where each one comes from
- Where this research lives in the game (a place the world grammar builds)clinic → clinic
Game simulation · Set in code
src/lib/world-grammar/place-grammar.ts · resolvePurpose('clinic') - Published works in this survey, each resolved at its DOI registry5
Real-world knowledge · Source [1] [2] [3] [4] [5]
- A network sorted photographs of skin lesions about as well as the dermatologists it was tested against.Esteva 2017
Real-world knowledge · Source [1]
- Convolutional neural networks: an overview and application in radiologyYamashita 2018
Real-world knowledge · Source [2]
- Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast CancerBejnordi 2017
Real-world knowledge · Source [3]
- Artificial intelligence in radiologyHosny 2018
Real-world knowledge · Source [4]
- Deep learning models for plant disease detection and diagnosisFerentinos 2018
Real-world knowledge · Source [5]
References, checked at their registry
[1] Esteva (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature. doi:10.1038/nature21056
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:50 PM UTC · the registry holds “Dermatologist-level classification of skin cancer with deep neural networks” (2017)
[2] Yamashita (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging. doi:10.1007/s13244-018-0639-9
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:50 PM UTC · the registry holds “Convolutional neural networks: an overview and application in radiology” (2018)
[3] Bejnordi (2017). Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. doi:10.1001/jama.2017.14585
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:50 PM UTC · the registry holds “Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer” (2017)
[4] Hosny (2018). Artificial intelligence in radiology. Nature reviews. Cancer. doi:10.1038/s41568-018-0016-5
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:50 PM UTC · the registry holds “Artificial intelligence in radiology” (2018)
[5] Ferentinos (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture. doi:10.1016/j.compag.2018.01.009
Verified: the work exists and matches · checked at Crossref on Oct 28, 2026, 10:50 PM UTC · the registry holds “Deep learning models for plant disease detection and diagnosis” (2018)
Review
Not peer reviewed. No human scientist has reviewed this survey. Its template and landmark summaries were written with an AI assistant (Claude); the papers it lists are real and each was checked at its registry, so anyone can follow the links and read them.
- Each listed DOI was resolved, and its title, year and first author matched the listing.
- Each work the search found is held by Crossref, a registry independent of the search.
- Each number in the headline and summary is accounted for by a listed claim.
- Not shown: 2 candidate work(s) whose registry record did not confirm them (10.4230/oasics.dx.2024.27 verified (not in Crossref); 10.1176/appi.books.9780890425596 mismatch).
Code version: fa127b6f988c
What the record does not say
This survey lists what each paper reports; the game has not repeated these results. A search finds papers that match its words, so a listed paper may use AI only in part. The search is limited to the fifty most-cited matches, so the newest results may be missing until they are cited.
How this was written
Landmark works named by the desk, then the most-cited works with a DOI that OpenAlex returns when titles and abstracts are searched for (diagnosis OR diagnostic OR "medical imaging" OR radiology OR dermatology OR pathology OR "skin cancer") AND ("machine learning" OR "deep learning" OR "neural network" OR "neural networks" OR "artificial intelligence" OR convolutional OR "reinforcement learning" OR "language model" OR transformer); a hit is kept only if its own title names the field and its title or abstract names an AI method, as whole words, and the hits that name the most of these words in their title come first. Each candidate was looked up at Crossref (OpenAlex as second resolver) and its title, year and first author compared; a work the search found is shown only when Crossref holds it. Landmark summaries were written by the desk; the other works are listed by their own titles.
Source records: code:src/lib/world-grammar/place-grammar.ts#resolvePurpose · doi:10.1038/nature21056 · doi:10.1007/s13244-018-0639-9 · doi:10.1001/jama.2017.14585 · doi:10.1038/s41568-018-0016-5 · doi:10.1016/j.compag.2018.01.009