Multi-institutional validation survey on Belong.life’s conversational artificial intelligence (AI) oncology mentor, “Dave”.

Authors: Prof. Talia Golan , Dr. Ofer Purim, Prof. Danny Rosin, Dr. Eli Sapir, Prof. Moshe Gatt, Dr. Tomer Charas, Dr. Ilya Iofin, Prof. Ido Wolf, Bozhena Haikin , Dr. Daniel A. Vorobiof.

Researched by: investigators from Sheba Medical Center, Shaarei Tzedek Medical Center, Assuta Ashdod University Medical Center, Hadassah Medical Center, Rambam Hospital, Ichilov Hospital, Israel; Mount Sinai Medical Center, New York, NY, and Belong.Life.

Abstract published in ASCO’s Annual Congress Abstract Book 2024.

Background:

Belong.life, a global oncology social network for patients and caregivers, recently launched “Dave” the first conversational AI oncology mentor and companion. “Dave’s” objectives are to provide uninterrupted support, clarify relevant clinical issues and guide patients and caregivers with relevant and empathetic information and education in all aspects of cancer, from diagnosis to treatment protocols and side effects management. “Dave” underwent training on Belong’s unique and large datasets of patients to physicians, and patients to patients’ interactions, as well as incorporating high quality information from the latest international cancer guidelines, providing it with a robust up-to-date supportive data and a wide understanding of the patients’ cancer journey.

Methods:

“Dave’s” responses to inquiries from Belong members were subjected to a validation survey conducted by eight oncologists, each specializing in various solid and haematological cancers and affiliated with several medical institutions. From the social network datasets, 471 questions from patients and caregivers were randomly selected and categorized into groups, including Breast, Gastrointestinal and Pancreatic, Genito-urinary, Bone, Haematological cancers, and Radiation therapy. The oncologists assessed the AI mentor’s replies for their relevance and helpfulness, aligning them with evidence-based medicine, current recommendations, and guidelines.

Results:

Results were categorized into positive or negative assessments. Impressively, 432 out of 471 “Dave’s” responses (91.8%) received positive validation for providing suitable and relevant recommendations. Conversely, 39/471 responses (8.2%) received a negative validation, of which only 5/471 (1%) were graded as not at all helpful or relevant. Minor variations were observed within diagnostic groups, with positive validation rates as follows: Genito-urinary 100%, Breast 98.4%, Musculoskeletal 97%, Radiation therapy 88%, Gastrointestinal and Pancreatic 86%, and Haematological cancers 84%.

Conclusions:

The documented results on 471 patients’ posts are very promising, indicating that “Dave” provides valuable and reliable recommendations, achieving a positive validation rate of nearly 92%, with only 1% graded as not at all helpful or relevant. The findings also offer insights into the AI mentor’s performance across distinct medical domains. This validation study provides a solid foundation and adds confirmation that the addition of an AI oncology mentor and companion, like “Dave”, improves patients’ knowledge and coping mechanisms and provides helpful and relevant guidance during their cancer journey, while supporting physicians in the daily management of their cancer patients.

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