Artificial Intelligence (AI) in Healthcare Provision: Putting the Human into Pain AI

Joletta is a fierce advocate for the integration of lived experience into the research, study, and treatment of pain. She came to this work through her own experiences of living with chronic pain and struggling to find adequate care, resources, and support after an on-the-job injury as a firefighter. She began writing a blog to try and make sense of things, which led to invitations to speak at conferences and to partner in pain research, education, and publishing. 

She was invited to a meeting at the 2018 World Congress on Pain to discuss how IASP could meaningfully integrate lived experience into its work. In 2019, the Presidential Task Force that became the Global Alliance of Partners for Pain Advocacy (GAPPA) was formed, and Joletta became the founding Co-Chair with Blair. She currently serves as GAPPA Past Co-chair. 

Dr. Barreveld is Medical Director of the Pain Management Services and co-Founder and Director of Education and Outreach for the Substance Use Services (SUS). She is Associate Professor of Anesthesiology at Tufts University School of Medicine, and an Anesthesiologist with Commonwealth Anesthesia Associates at Newton-Wellesley Hospital since 2012. Dr. Barreveld is also a Clinical Researcher at Harvard Medical School in the Department of Anesthesiology, Perioperative and Pain Medicine at Brigham and Women's Hospital in Boston, MA. She regularly teaches medical students on pain management principles at Harvard Medical School and Tufts University School of Medicine. She graduated from the University of California San Francisco Medical School and completed her Residency in Anesthesiology and Fellowship in Pain Medicine at Brigham and Women's Hospital. Her clinical interests are in managing chronic pelvic pain in men and women, acute and chronic postoperative pain, interdisciplinary spine care, safe practices in co-managing pain and addiction, and complex interventional procedures from diagnostic nerve blocks to spinal cord stimulation including dorsal root ganglion stimulation.

Dr. Daniel Buchman is a bioethicist and scientist at the Centre for Addiction and Mental Health (CAMH) where he directs the Everyday Ethics Lab. He also serves an associate professor in the Dalla Lana School of Public Health at the University of Toronto and a member of the University of Toronto Joint Centre for Bioethics. In addition, he is a member of the Canadian Institutes of Health Research (CIHR) Advisory Committee on Ethics and a member of the recently established Bioethics Council for Canada. Dr. Buchman’s research is at the forefront of exploring ethical issues at the intersection of clinical practice and population health, with a particular focus on ethical issues related to mental health, substance use, and chronic pain. His work delves into themes related to stigma, social justice, identity, and compassion, employing a multidisciplinary approach that combines conceptual and empirical methods. He has a strong commitment to advancing ethical practices in healthcare, which continues to shape policies and improve patient care. Some of Dr Buchman’s current projects explore ethical issues related to stigma, chronic pain, and neurotechnologies; industry-healthcare relationships; opioids; psychedelics; palliative psychiatry; and artificial intelligence and digital health.

Dr. Vollert is a chronic pain researcher from Germany, where he did his PhD in neurophysiology at the University of Heidelberg. After six years at Imperial College in London,UK, he has joined the University of Exeter, UK, as a Lecturer (Assistant Professor) in October 2023.

His background is in data science and statistics, and he collaborates with clinicians across the UK and Europe to analyze multifaceted datasets searching for predictors of developing chronic pain (for example after surgery) and predicting response to treatment to enable personalized pain medicine. He uses a wide array of methods -sensory phenotyping, patient-reported outcomes, -omics – and machine learning to identify mechanistic subgroups.