Is Explanation a Marketing Problem? The Quest for Trust in Artificial Intelligence and Two Conflicting Solutions

The effects of loving-kindness meditation on doctors' communication anxiety, trust, calling and defensive medicine practice

OBJECTIVE

The study investigated the effects of loving-kindness meditation (LKM) on doctors' communication anxiety, trust, calling, and defensive medicine practice.

METHODS

This study recruited 94 doctors from a hospital in China, randomized them to an LKM group (n = 47), and waited for the control group (n = 47). The experimental group accepted an 8-week LKM interference while the waiting for the control group underwent no interference. Researchers measured four major variable factors (communication anxiety, trust, calling, and defensive medicine practice) before and after the LKM intervention.

RESULTS

In the experimental group, trust, and calling were significantly higher, and communication anxiety, and defensive medicine practice were significantly lower than in the control group. In the control group, there were no noticeable differences in any of the four variables between the pre-test and post-test.

CONCLUSIONS

The results of this study demonstrate that LKM may help to improve trust, and calling, and reduce communication anxiety and defensive medicine practice. The finding of LKM's effect extends the understanding of the integrative effects of positive psychology on the decrease of defensive medicine practice.

TRIAL REGISTRATION

ChiCTR2300074568. Registered in Chinese Clinical Trial Registry (ChiCTR), 9 August, 2023.

Evaluating the clinical utility of artificial intelligence assistance and its explanation on the glioma grading task

Clinical evaluation evidence and model explainability are key gatekeepers to ensure the safe, accountable, and effective use of artificial intelligence (AI) in clinical settings. We conducted a clinical user-centered evaluation with 35 neurosurgeons to assess the utility of AI assistance and its explanation on the glioma grading task. Each participant read 25 brain MRI scans of patients with gliomas, and gave their judgment on the glioma grading without and with the assistance of AI prediction and explanation. The AI model was trained on the BraTS dataset with 88.0% accuracy. The AI explanation was generated using the explainable AI algorithm of SmoothGrad, which was selected from 16 algorithms based on the criterion of being truthful to the AI decision process. Results showed that compared to the average accuracy of 82.5±8.7% when physicians performed the task alone, physicians' task performance increased to 87.7±7.3% with statistical significance (p-value = 0.002) when assisted by AI prediction, and remained at almost the same level of 88.5±7.0% (p-value = 0.35) with the additional assistance of AI explanation. Based on quantitative and qualitative results, the observed improvement in physicians' task performance assisted by AI prediction was mainly because physicians' decision patterns converged to be similar to AI, as physicians only switched their decisions when disagreeing with AI. The insignificant change in physicians' performance with the additional assistance of AI explanation was because the AI explanations did not provide explicit reasons, contexts, or descriptions of clinical features to help doctors discern potentially incorrect AI predictions. The evaluation showed the clinical utility of AI to assist physicians on the glioma grading task, and identified the limitations and clinical usage gaps of existing explainable AI techniques for future improvement.

A "Third Wheel" Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective

In the near future, Artificial Intelligence (AI) is expected to participate more and more in decision making processes, in contexts ranging from healthcare to politics. For example, in the healthcare context, doctors will increasingly use AI and machine learning devices to improve precision in diagnosis and to identify therapy regimens. One hot topic regards the necessity for health professionals to adapt shared decision making with patients to include the contribution of AI into clinical practice, such as acting as mediators between the patient with his or her healthcare needs and the recommendations coming from artificial entities. In this scenario, a "third wheel" effect may intervene, potentially affecting the effectiveness of shared decision making in three different ways: first, clinical decisions could be delayed or paralyzed when AI recommendations are difficult to understand or to explain to patients; second, patients' symptomatology and medical diagnosis could be misinterpreted when adapting them to AI classifications; third, there may be confusion about the roles and responsibilities of the protagonists in the healthcare process (e.g., Who really has authority?). This contribution delineates such effects and tries to identify the impact of AI technology on the healthcare process, with a focus on future medical practice.