Cybersecurity in The Health Sector in The Reality of Artificial Intelligence, And Information Security Conceptually
Bibliographic record
Abstract
Healthcare service delivery, especially in terms of safeguarding personal data, requires ensuring the confidentiality of information. In this regard, establishing cybersecurity systems that ensure information security is highly necessary. The rapid advancement of technologies increases the likelihood of cyberattacks, and particularly, AI-supported threats can cause serious harm in service delivery. In the current era, attacks not only come from humans but also from AI tools, posing threats to information security. Considering that AI technology is expected to further advance in the future, it's evident that this technology could become even more menacing. This is especially pertinent to the healthcare sector. Cyberattacks can lead to breaches in healthcare system data and disrupt service delivery to the extent of paralyzing the healthcare system. Our study, which includes case examples, is a compilation-type research. Within the scope of our research, searches were conducted using the keywords healthcare sector, information security, and cybersecurity on Google Scholar and Web of Science. The most current topic headings intersecting information security with the healthcare sector were examined based on the articles found on the subject. Our study evaluates the following topics in order: information and cyber security concepts, cyber threats and public services, electronic health records and security, major cyber-attacks in the health sector, why healthcare data is attractive for cyberattacks, information security in the artificial intelligence era, and information security policies for Türkiye and other countries in the world. Ransomware holds a significant place among cyberattacks. Therefore, users within the healthcare system are advised to pay particular attention to this issue. Attacks generally occur via email, starting with enticing the user into a cyber-threat through email. Artificial intelligence can also be used to get rid of such spam mails. Hence, it is strongly recommended that users in the healthcare sector undergo training on this matter. These trainings should be conducted regularly and continuously, with the institution's IT center offering an institutional approach in this regard.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".