Healthcare ex Machina: Are conversational agents ready for prime time in oncology?
Open accessAbstract
Chatbots, also known as conversational agents or digital assistants, are artificial intelligence-driven software programs designed to interact with people in a conversational manner. They are often used for user-friendly customer-service triaging. In healthcare, chatbots can create bidirectional information exchange with patients, which could be leveraged for follow-up, screening, treatment adherence or data-collection. They can be deployed over various modalities, such as text-based services (text messaging, mobile applications, chat rooms) on any website or mobile applications, or audio services, such as Siri, Alexa, Cortana or Google Assistant. Potential applications are very promising, particularly in the field of oncology. In this review, we discuss the available publications and applications and the ongoing trials in that setting.
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Existing reviews lack a dedicated focus on user perspectives, limiting insights into how CAs can be optimised for patient needs.
“Existing reviews lack a dedicated focus on user perspectives, limiting insights into how CAs can be optimised for patient needs.”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Bibault, Chaix, Nectoux, et al. 2019
The Introduction (page not provided) describes this review as an overview of conversational agents in oncology, rather than a review dedicated to user perspectives. The Conclusion identifies a lack of objective evidence about chatbot relevance and efficacy, but the paper does not establish that existing reviews generally lack a user-perspective focus or that this limits insights into optimising CAs for patient needs. (LLM, conf=0.75)
Experimental — accuracy not yet measuredOnly two existing reviews investigated the use of CAs in oncology: one examined the landscape of CAs and the other assessed applications, limitations, and challenges of employing CAs in cancer care.
“There are only two existing reviews investigating the use of CAs in oncology: one examined the landscape of the use of CAs (Bibault, Chaix, Nectoux, et al. 2019) and the other assessed the applications, limitations and challenges of employing CAs in cancer care (Wang et al. 2023).”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Bibault, Chaix, Nectoux, et al. 2019
These existing reviews did not explicitly focus on the domain of health education for patients.
“However, these reviews did not explicitly focus on the domain of health education for patients.”
From The Usability and Experience of Artificial Intelligence‐Based Conversational Agents in Health Education for Cancer Patients: A Scoping Review (2026) · cites this paper as Bibault, Chaix, Nectoux, et al. 2019
Conversational AI can encourage health care consumers to engage with systems that imitate human conversations using text.
“More specifically, conversational AI (or conversational agents) can encourage health care consumers to engage with systems that imitate human conversations using text [23,24].”
A review of the literature using conversational AI reveals that the focus area is limited in scale and tends to be restricted to apps and not SMS.
“A review of the literature using conversational AI reveals that the focus area is limited in scale and tends to be restricted to apps [24] and not SMS.”
Conversational agents mimic human interaction using natural language processing to analyze user inputs and respond appropriately using human language via auditory or textual methods.
“Conversational agents mimic human interaction using natural language processing to analyze user inputs and respond appropriately using human language via auditory or textual methods [5].”
From Effectiveness of Conversational Agents (Virtual Assistants) in Health Care: Protocol for a Systematic Review (2020) · cites this paper as [5]
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- Authors
- Bibault, J.-E., Chaix, B., Nectoux, P., Pienkowski, A., Guillemasé, A., & Brouard, B.
- Affiliations
- Assistance Publique – Hôpitaux de Paris (FR)
- Hôpital Gui de Chauliac (FR)
- Pitié-Salpêtrière Hospital (FR)
- Corresponding author
- Bibault, Jean-Emmanuel
- Venue
- Clinical and Translational Radiation Oncology
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 16
- Pages
- 55-59
- ISSN
- 2405-6308 (ISSN-L)
- PMC
- PMC6454131
- OpenAlex
- W2931716593
- Cited by
- 84
- References
- 23
- Citations by year
- 2026: 4 · 2025: 13 · 2024: 8 · 2023: 15 · 2022: 5 · 2021: 17 · 2020: 15 · 2019: 6
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Clinical and Translational Radiation Oncology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- chatbots, oncology, artificial intelligence in healthcare, patient follow-up, cancer screening, medication adherence, virtual assistants, voice assistants, mobile health apps, clinical trials
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