Physician Perspectives on the Impact of Artificial Intelligence on the Therapeutic Relationship in Mental Health Care: Qualitative Study
Bibliographic record
Abstract
Background: The therapeutic relationship is a professional partnership between clinicians and patients that supports open communication and clinical decision-making. This relationship is critical to the delivery of effective mental health care. The integration of artificial intelligence (AI) into mental health care has the potential to support accessibility and personalized care; however, little is known about how AI might affect the dynamics of the therapeutic relationship. Objective: This study aimed to ascertain how physicians anticipate AI tools will impact the therapeutic relationship in mental health care. Methods: We conducted 42 in-depth interviews with psychiatrists and family medicine practitioners to investigate physician perceptions regarding the impact of AI on mental health care. Results: Physicians identified several disruptions from AI use, noting that these tools could impact the dyad of the patient-physician relationship in ways that are both positive and negative. The main themes that emerged included potential disruptions to the therapeutic relationship, shifts in shared decision-making dynamics, and the importance of transparent AI use. Participants suggested that AI tools could create efficiencies that allow for relationship building as well as help avoid issues with miscommunication during psychotherapeutic interactions. However, they also expressed concerns that AI tools might not adequately capture aspects of the therapeutic relationship, such as empathy, that are vital to mental health care. Physicians also raised issues related to the impact that AI tools will have on maintaining relationships with patients. Conclusions: As AI applications become increasingly integrated into mental health care, it is crucial to assess how this integration may support or disrupt the therapeutic relationship. Physician acceptance of emerging AI tools may be highly dependent on how well the human elements of mental health care are preserved.
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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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".