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Where Empathy and AI Work Together

At mpathic, the scientific principles underlying AI are taken seriously. Several of mpathic’s team members actively engage in innovative thought leadership, focusing on how AI can support precision healthcare for all. 

Below, you can see the areas in which the mpathic team is well-versed in.

The Science Behind mpathic

Accurate understanding and communication that leads to behavior change is a well-researched science. This is not about being warm-fuzzy but learning how people build trust with each other. Psychologists have dedicated decades to studying how conversations can shape medical outcomes. Now, this same science has been validated for commercial use with mpathic.

  • Unmatched expertise in AI with 90+ human behavior detections 
  • 500k expert-labeled communications
  • Commercialized and established gold-standard measures of fidelity
  • Expert evaluators in organizational psychology, healthcare, life sciences and commercial sales globally
  • Prioritization of data quality and privacy

Research Links

Research Title

Authors

Summary

Cerezo, A., Palat, V., Jolley-Page, J., Rbeiz, K., Srivastava, A., Chakraborty, T., Lord, S. P., Schlosser, D. 

Exploring how AI and empathy synergize in psychotherapy, the research focuses on leveraging AI for precision healthcare and fostering improved mental health outcomes.

Cerezo, A., Cooper, D., Palat, V., Jolley, A., & Lord, S.P. 

This study explores how culturally responsive AI can improve mental health equity for racial and ethnic minorities by incorporating cultural context in care and addressing challenges in creating sensitive AI.

Dougherty, R. F., Clarke, P., Atli, M., Kuc, J., Schlosser, D., Dunlop, B. W., Hellerstein, D. J., Aaronson, S. T., Zisook, S., Young, A. H., Carhart-Harris, R., Goodwin, G. M., & Ryslik, G. A.

This analysis uses a machine learning classifier and a BART language model to predict outcomes in psilocybin therapy, highlighting AI and NLP's potential in personalizing psychiatric treatments.

Srivastava, A., Gupta, T., Cerezo, A., Lord, S. P., Akhtar, M. S., & Chakraborty, T.

Comprehensive research centered on AI that identifies key behavioral characteristics promoting peer interaction in online mental health forums.

Creed, T. A., Oziel, R., Reich, D., Thomas, M., O'Connor, S., Imel, Z. E., Hirsch, T., Narayanan, S. & Atkins, D. C.

This study probes an AI tool's potential in revolutionizing mental health supervision, emphasizing its applications in training and remote supervision.

Lord, S. P., Bertagnolli, N.

The study utilizes AI and ML in assessing data and recommending improvements in communication techniques based on evidential models.

Flemotomos, N., Martinez, V. R., Chen, Z., Singla, K., Ardulov, V., Peri, R., Caperton, D. D., Gibson, J., Tanana, M. J., Georgiou, P., Van Epps, J., Lord, S. P., Hirsch, T., Imel, Z. E., Atkins, D. C., & Narayanan, S.

This study emphasizes the use of AI and ML to mechanize the appraisal of psychotherapy skills, leveraging speech and language technologies.

Srivastava, A., Suresh, T., Lord, S. P., Akhtar, M. S., Chakraborty, T.

An extensive study that applies AI, ML, and NLP to generate exhaustive counseling summaries using knowledge-guided utterance filtering.

Flemotomos, N., Martinez, V. R., Chen, Z., Singla, K., Ardulov, V., Peri, R., Caperton, D. D., Gibson, J., Tanana, M. J., Georgiou, P. G., Van Epps, J., Lord, S. P., Hirsch, T., Imel, Z. E., Atkins, D. C., & Narayanan, S.

The investigation presents an AI tool that assesses psychotherapy through audio analysis, providing feedback to support therapist education and skill development.

Darnell, D., Areán, P. A., Dorsey, S., Atkins, D. C., Tanana, M. J., Hirsch, T., Mooney, S. D., Boudreaux, E. D., & Comtois, K. A.

The research on Project WISE involves an AI-enhanced suicide safety planning e-learning tool for nurses, aiming to improve suicide prevention strategies.

Creed, T. A., Oziel, R., Reich, D., Thomas, M., O'Connor, S., Imel, Z. E., Hirsch, T., Narayanan, S. & Atkins, D. C.

The investigation explores mental health providers' views on an AI tool for Cognitive Behavioral Therapy, revealing the perceived benefits and concerns.

Imel, Z. E., Pace, B. T., Soma, C. S., Tanana, M., Hirsch, T., Gibson, J., Georgiou, P., Narayanan, S., & Atkins, D. C.

This study develops an automated machine learning tool for psychotherapy evaluation, focusing on "contestability" and human accountability enhancement methods.

Hirsch, T., Soma, C., Merced, K., Kuo, P., Dembe, A., Caperton, D. D., Atkins, D. C., & Imel, Z. E.

The research introduces CORE-MI, an automatic ML feedback tool for mental health professionals, discussing its clinical uses and potential issues.

Lord, S. P., Sheng, E., Imel, Z. E., Baer, J., & Atkins, D. C.

The research suggests that empathy in motivational interviews includes synchronization in language style between therapists and clients.