By Jennifer Bires, LCSW, OSW-C, CST

Artificial Intelligence (AI) is all the buzz right now. Advances are happening at a rapid pace generating excitement, uncertainty and fear. As a profession, oncology social work prides itself on relationships – the ones we build with patients and communities and the ones we help patients build with their communities, families and providers. Through this lens, AI might at best seem like something that does not belong in our wheelhouse and at worst seem like something that is antagonistic to our values as a profession.

There seems to be more questions than answers right now. There is the potential for AI to help identify patients in need and connect them with evidence-based, tailored interventions. That tailoring could help make interventions more culturally relevant or adapt what a patient sees based on their need, time to participate or literacy level. With a shrinking work force and a growing need, AI has the potential to act as extenders for oncology social workers already feeling stretched thin. New technologies can help manage more simple needs leaving more bandwidth for complex challenges or even doing some of the initial problem solving and resource identification. AI is being used to train clinicians and could be utilized as a tool for student learners, those new to oncology social work or even experienced clinicians – all while not taking existing oncology social workers away from patient care. AI also has the potential to help with conversations that feel uncomfortable for patients to bring up, and it can be available at 2 a.m. if that’s when a patient needs it.

While there are few articles specifically in the space of psychosocial oncology, there are advances being made every day. One recent study looked at using generative AI as a means to provide information on the topic of sexual health, which can be difficult for patients and providers alike to bring up (Hanai et al., 2024). Another study used virtual reality in combination with AI to treat hot flashes in women with breast and ovarian cancer and showed a reduction in hot flashes and sleep difficulties, and improvements in quality of life (Horesh et al., 2022). Leung et. al. (2023), in Canada, researched AI as a “co-facilitator” for an online support group for people with cancer to help identify patients in distress and offer potential interventions and resources though with mixed results. These are just three examples of some of the work happening right now. These studies are small, they help show that AI is not a magic want, but that it has potential to be additive if developed thoughtfully and used intentionally.

It is hard to begin to imagine all the possibilities that might be available to us as oncology social workers in the future with AI-enabled technology. While it is clear that there will always be a need for oncology social workers, the opportunities on the horizon could dramatically change how care is provided. This new technology does not come without risk. It’s in its infancy and in need of further development. We need to ensure that these new technologies are developed and utilized in an ethical way that adds to patient care and access rather than distracts or harms patients. As with anything else, it will be important to let the evidence guide us, but it will also be our job to create the evidence. As oncology social workers, it is often our job to advocate for patients and ensure their needs are met, and that will be equally important as AI plays a bigger role in medicine and psychosocial care of patients and families.

If this is a topic you find interesting and you want to learn more, here are a few articles you could start with:

• Stade, E.C., Stirman, S.W., Ungar, L.H. et al. Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation. npj Mental Health Res 3, 12 (2024). https://doi.org/10.1038/s44184-024-00056-z
• Smrke U, Mlakar I, Lin S, Musil B, Plohl N. Language, Speech, and Facial Expression Features for Artificial Intelligence-Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review. JMIR Ment Health. 2021 Dec 6;8(12):e30439. doi: 10.2196/30439. PMID: 34874883; PMCID: PMC8691410.
• Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim HC, Jeste DV. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep. 2019 Nov 7;21(11):116. doi: 10.1007/s11920-019-1094-0. PMID: 31701320; PMCID: PMC7274446.

References:

Hanai, A., Ishikawa, T., Kawauchi, S., Iida, Y., & Kawakami, E. (2024). Generative artificial intelligence and non-pharmacological bias: An experimental study on cancer patient sexual health communications. BMJ Health & Care Informatics, 31(1), e100924. https://doi.org/10.1136/bmjhci-2023-100924

Horesh, D., Kohavi, S., Shilony-Nalaboff, L., Rudich, N., Greenman, D., Feuerstein, J. S., & Abbasi, M. R. (2022). Virtual Reality Combined with Artificial Intelligence (VR-AI) Reduces Hot Flashes and Improves Psychological Well-Being in Women with Breast and Ovarian Cancer: A Pilot Study. Healthcare (Basel, Switzerland), 10(11), 2261. https://doi.org/10.3390/healthcare10112261

Leung, Y. W., Ng, S., Duan, L., Lam, C., Chan, K., Gancarz, M., Rennie, H., Trachtenberg, L., Chan, K. P., Adikari, A., Fang, L., Gratzer, D., Hirst, G., Wong, J., & Esplen, M. J. (2023). Therapist Feedback and Implications on Adoption of an Artificial Intelligence–Based Co-Facilitator for Online Cancer Support Groups: Mixed Methods Single-Arm Usability Study. JMIR Cancer, 9, e40113. https://doi.org/10.2196/40113