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Poster session 12

1834P - Accuracy of recommendations by a conversational Artificial Intelligence (AI) cancer mentor application (app): A multi-disciplinary, multi-institutional evaluation report

Date

14 Sep 2024

Session

Poster session 12

Topics

Cancer Intelligence (eHealth, Telehealth Technology, BIG Data);  Cancer Research

Tumour Site

Pancreatic Adenocarcinoma

Presenters

Talia Golan

Citation

Annals of Oncology (2024) 35 (suppl_2): S1077-S1114. 10.1016/annonc/annonc1612

Authors

T. Golan1, O. Purim2, I. Iofin3, D. Rosin4, E. Sapir5, M. Gatt6, T. Charas7, I. Wolf8, B. Haikin9, D.A. Vorobiof10

Author affiliations

  • 1 Oncology, Sheba Tel Hashomer Medical Center, 52621 - Ramat Gan/IL
  • 2 Oncology, Shaare Zedek Medical Center, Jerusalem/IL
  • 3 Oncology, Mount Sinai Hospital, 10029 - New York/US
  • 4 Oncology, Shaare Zedek Medical Center, 9103102 - Jerusalem/IL
  • 5 Radiation Therapy, Samson Assuta Ashdod Academic Medical Center, 7747629 - Ashdod/IL
  • 6 Hematology Oncology, Hadassah-Hebrew University Medical Center, 91120 - Jerusalem/IL
  • 7 Rambam Oncology, Technion - Israel Institute of Technology, 32000 - Haifa/IL
  • 8 Oncology, Tel Aviv Sourasky Medical Center-(Ichilov), 6423906 - Tel Aviv/IL
  • 9 Informatics Research, Belong. life, 6099100 - Bnei Atarot/IL
  • 10 Medical Director, Belongtail Ltd, 10001 - New York/US

Resources

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Abstract 1834P

Background

The first conversational AI Cancer mentor app was recently launched by Belong.life, a global oncology social network for cancer patients (pts) and caregivers. The app objectives are to provide uninterrupted support and guidance with empathetic, relevant information in cancer management. Belong’s unique and large datasets of pts to physicians and pts to pts interactions provide the basis for the AI platform, adding information from current guidelines, supplying solid up to date data with wide understanding of pts diverse cancer journeys.

Methods

Belong members enquiries, replied by the AI Cancer mentor were validated by 9 oncology experts in solid and haematological cancers, affiliated to various medical centres. 578 member enquiries coupled with the AI Cancer mentor replies, were randomly chosen and distributed according to cancer groups: Breast, Gastrointestinal, Pancreatic, Genitourinary, Musculoskeletal, Haematology and Radiation therapy. The oncologists assessed the AI mentor accuracy of recommendations by relevance and helpfulness, aligning them to evidence-based medicine and current international guidelines.

Results

527/578 (91.18%) AI Cancer mentor replies received a positive validation for their suitable and relevant recommendations. Conversely, 46/578 (7.96%) received a negative validation, while only 5/578 (0.86%) graded as not helpful. Minor variations were noticed in the cancer groups, with positive validations as follows: Genitourinary 100%, Breast 95.7%, Pancreatic 92%, Musculoskeletal 91.2%, Radiation therapy 88%, Gastrointestinal 86.4%, Haematological cancers 84%.

Conclusions

The reported accuracy (91.18%) on 578 pts enquiries and AI Cancer mentor replies, evaluated by a multi-disciplinary, multi-institutional oncologist group, are very promising, indicating that the Belong’s AI cancer mentor provides valuable and reliable recommendations. The findings offered insights into the AI mentor’s performance across a wide group of cancers. Based on this report, the addition of an AI mentor in cancer care, provides helpful, relevant guidance on pts diverse cancer journeys, while supporting oncologists in their pts management.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

B. Haikin, D.A. Vorobiof: Financial Interests, Personal, Full or part-time Employment: Belong.life. All other authors have declared no conflicts of interest.

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