Information and Communication Technology Medicine: Integrative Specialty for the Future of Medicine
Bibliographic record
Abstract
The impact of information and communication technology (ICT) on medicine is unprecedented and ever-increasing. This has made it more and more difficult for doctors to keep pace with ICT developments and to adequately match the input of ICT experts. As a result, medical disciplines may not be able to take full advantage of growing possibilities. In this personal viewpoint paper, I argue for the establishment of a novel medical specialty, ICT medicine. ICT medicine is needed to optimally face the challenges of ICT-based developments, including artificial intelligence (AI), and to ensure their efficient and beneficial use. ICT medicine is rooted in both medicine and ICT, and in contrast to existing medical specialties it is integrative in nature, as long-standing structural collaborations with ICT and other stakeholders cross the boundaries between disciplines. Thus, new concepts and theories may evolve that are better suited to addressing ICT-related issues in medicine. ICT doctors will be instrumental in the conception, development, implementation, and evaluation of digital tools, systems, and services. They provide a bridge between ICT professionals and clinical users and educate doctors in digital applications and services. Notably, ICT doctors may have a pivotal role in the validation, verification, and evaluation of AI models. ICT medicine institutes offer a home to these new professionals, enhancing their independence within health care organizations and in relation to ICT companies. Importantly, in an era of growing technicalization and use of AI algorithms, ICT doctors may safeguard the human factor in medicine. And, from a societal perspective, they may promote digital inclusion and the continuing high quality of digital services and provide leadership in the future digitalization of medicine.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 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".