The AI Fever: Can Artificial Intelligence Replace Compassionate Human Care?
Bibliographic record
Abstract
As healthcare professionals and educators, we have seen compassion as the backbone of quality of care.We vividly remember moments at patients' bedsides, where a gentle touch or a few calm words carried more weight than any medical treatment.These experiences remind us that healing is rooted in human connections as much as clinical skill.Compassion is a fundamental principle of healthcare, emphasized in ethical codes, care standards, and policy documents.It plays a unique role in delivering highquality treatment and serves as the foundation of human interactions in nursing. 1 Crawford et al define compassion as sensitivity to others' suffering, prompting verbal, non-verbal, or physical responses that help ease suffering. 2Zamanzadeh et al describe compassionate care as empathetic connection and active efforts to address patient concerns. 3olistic human connection, marked by attention to details and emotions and supportive care, is integral to positive healthcare experiences.The rise of advanced technologies such as artificial intelligence (AI) has increased concerns about whether human aspects of care might be replaced. 4Today, AI can analyze huge amounts of data in real time, assisting in disease identification, early diagnosis, and personalized treatment planning, and facilitating clinical decision-making. 5Advancements in AI have enhanced healthcare efficiency and accuracy.In nursing, AI can automate repetitive time-consuming tasks, such as recording patient data.As a result, it can help solve the problem of nursing shortages. 6Additionally, AI supports clinical practice by automating routine tasks and providing decision-support tools for healthcare
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".