mpathic Research

Research Title	Authors	Summary
Leveraging AI for Standardized COA Administration: Improving Rater Consistency and Scoring Accuracy in Psychedelic Trials (2025)	Cerezo, A., Robledo Yamamoto, F., Palat, V., Marafon, T., Penn, T. & Schlosser, D.	Poster that presents pilot findings from the development and testing of an AI-enabled COA quality oversight tool designed to evaluate administration quality and scoring accuracy on the MADRS.
Enhancing Non-technical Performance of Robot-Assisted Surgical Skills Using Artificial Intelligence (2025)	Fernandez, N., Lendvay, T.S. & Cerezo, A.	Poster that describes mpathic's real-time, AI-driven surgical coaching tool that analyzes intraoperative communication and overarching behaviors, including hesitancy, urgency, and communication breakdowns, during robotic procedures.
Sentiment-guided Commonsense-aware Response Generation for Mental Health Counseling. (2025)	Srivastava, A., Naik, G., Cerezo, A., Chakraborty, T., & Akhtar, M. S.	This study introduces 'EmpRes,' a novel tool that improves mental health counseling using sentiment analysis and commonsense reasoning, achieving over 91% user satisfaction.
Common Factors in Psychotherapy: Enhancing Provider-to-Patient Dynamics to Improve Patient Outcomes. (2024)	Cerezo, A., Palat, V., Jolley-Page, J., Rbeiz, K., Srivastava, A., Chakraborty, T., Lord, S. P., Schlosser, D.	Poster exploring how AI and empathy synergize in psychotherapy, the research focuses on leveraging AI for precision healthcare and fostering improved mental health outcomes.
Leveraging AI to Deliver Culturally Responsive Mental Health Care at Scale. (2024)	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.
Psilocybin therapy for treatment resistant depression: prediction of clinical outcome by natural language processing. (2023)	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.
Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities. (2023)	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.
Knowledge and attitudes toward an artificial intelligence-based fidelity measurement in community cognitive behavioral therapy supervision. (2022)	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.
System and method for increasing effective communication through evaluation of multimodal data, auto-correction and behavioral suggestions based on models from evidence-based. (2022)	Lord, S. P., Bertagnolli, N.	The study utilizes AI and ML in assessing data and recommending improvements in communication techniques based on evidential models.
Automated evaluation of psychotherapy skills using speech and language technologies (2022)	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.
Counseling Summarization using Mental Health Knowledge Guided Utterance Filtering (2022)	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.
Am I a good therapist? Automated evaluation of psychotherapy skills using speech and language technologies. (2021)	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.
Harnessing innovative technologies to train nurses in suicide safety planning with hospitalized patients: Protocol for formative and pilot feasibility research. (2021)	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.
Knowledge and attitudes toward an artificial intelligence-based fidelity measurement in cognitive behavioral therapy supervision. (2020)	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.
Design feasibility of an automated, machine-learning based feedback system for motivational interviewing. (2019)	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.
"It's hard to argue with a computer": Investigating Psychotherapists' Attitudes towards Automated Evaluation. (2018)	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.
More than reflections: Empathy in motivational interviewing includes language style synchrony between therapist and client. (2015)	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.