The use of telemedicine in radiodiagnosis in the 1920–1980s
Bibliographic record
Abstract
\n \n Abstract\n \n In 2017, amendments to the Federal legislation on health care were adopted, which confirmed the possibility of using telemedicine technologies within the health care system of the Russian Federation. Telemedicine has been successfully used for about 150 years. Since the advent of the first electronic telecommunications, the possibilities for their medical use have been sought. This article systematises information about the history of the use of telecommunications for remote interaction in radiology and presents the way from experiments on facsimile transfer of radiographic images (the 1920–1930s) to the establishment of the teleradiology concept as a tool for solving diagnostic and organisational/managerial problems of radiology (the late 1970s). The first experiments on the remote transmission of photographic copies of X-ray images by telegraph were conducted in the mid-1920s. The first interhospital network for the exchange of medical images was launched in Canada in 1957 – a successful exchange of fluoroscopic images to improve diagnostics took place in Montreal between two hospitals. In the 1940–1960s, under J. Gershon-Cohen’s supervision, several teleradiological networks ensuring the transmission of photographic copies of X-ray images for remote interpretation were launched in the United States. For the first time ever, methodological foundations of teleradiology were formulated as a tool for organising and managing public health care. The term “teleradiology” was introduced by W.S. Andrus and T.K. Bird in 1972. The same researchers carried out the first scientific assessment of the diagnostic accuracy of remote interpretation of the results of radiographic examinations. In the late 1960–1970s, television systems (cable, slow-scan, etc.) were used to broadcast medical images, and their complexity and high cost were hampering the advancement of teleradiology. However, by the early 1980s, it was convincingly shown that teleradiology significantly expanded the capabilities of health care systems, sped up diagnosis and optimised hospital resources and staff time.\n
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".