A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness

Figures & data

Figure 1. The study selection flowchart.

Table 1. The characteristics of included studies.

Study	Country setting	Chronic illness	Aim of studies	Methods	Study population	Outcomes
						User satisfaction	Health related measures	Patient safety
Al-Hilli et al. [26]	USA	Breast cancer	Education	RCT	IG = 19; CG = 18; women with stage 0–III breast cancer	No significant difference in median satisfaction score between Chatbot and traditional counseling (30 vs. 30, p = .19)	Not reported	Not reported
Bibault et al. [27]	France	Breast cancer	Self-management	RCT	The study included female subjects aged 18 years or older who were either undergoing treatment for breast cancer or in remission. These participants demonstrated nonopposition and possessed internet literacy.	The findings indicate that among the patients, 59% expressed a desire for more information compared to 65% in the control group. In the study, 85% of the patients reported finding the information provided by the chatbot to be valuable, and the same percentage expressed satisfaction with the quantity of information received. This stands in contrast to the control group, where 83.1% and 77% reported similar levels of usefulness and satisfaction, respectively. Moreover, when comparing the ratings of chatbot responses to those provided by physicians in real-time, patients rated the chatbot responses more favourably. Overall, the study demonstrated noninferiority in the perceived quality of responses between the chatbot and physician-provided responses.	Not reported	The research exhibits a deficiency in transparent reporting concerning various facets of adverse events. These include the absence of specific data on the quantity of adverse events communicated through the electronic clinical assistant (ECA) and directly to the clinic by the patient, the duration taken to identify and rectify adverse events, as well as the rate of false alarms associated with adverse events reported through the ECA and directly to the clinic by the patient.
Echeazarra et al. [28]	Spain	Hypertension	Self-management	RCT	Individuals experiencing elevated blood pressure Total = 112; intervention group (IG) = 55; control group (CG) = 57	The participants gave the bot a highly favourable rating, found it to be valuable and user-friendly, and maintained its use even after the intervention period.	The bot group demonstrated comparable adherence to the blood pressure check schedule (bot group mean = 12.0, control group mean = 13.4, p = .109). Additionally, the intervention group (IG) significantly enhanced knowledge of proper blood pressure self-monitoring practices compared to the control group (CG) (IG mean = 24.126, CG mean = 17.591, p = .03737).	Not reported
Gong et al. [29]	Australia	Type 2 diabetes mellitus	Self-management	RCT	Adult with type 2 diabetes mellitus, N = 187; IG = 93; CG = 94	92 samples (98.9%) engaged in at least one chat with chatbot, and 83 participants (89.2%) uploaded their blood glucose levels to the app at least once. These 92 participants had a combined total of 1942 chats over the 12-month program, averaging 243 min per person (SD = 212).	At 12 months, there was a notable difference between the two groups: quality of life (difference: 0.04, 95% CI [0.00, 0.07]; p = .04), not in HbA1c levels. In comparison to baseline, both groups experienced a reduction in mean estimated HbA1c levels at 12 months (IG: mean estimated change: −0.33%; p = .03 and control arm: −0.28%; p = .05).	Not reported
Greer et al. [14]	USA	Post cancer treatment	After cancer treatment	Pilot RCT	The research encompassed a cohort of 45 young adults, segregated into two distinct groups. Group 1, constituting 25 young adults, was designated as the experimental cohort, while group 2, comprising 20 young adults, functioned as the control cohort. At the outset, all participants underwent a comprehensive survey assessment.	Their assessments indicated a positive reception, with an average rating of 2.0 out of 3 (SD 0.72) for perceived helpfulness, and an expressed likelihood to recommend it to others, with a mean score of 6.9 out of 10 (SD 2.6).	The intervention group showed an average anxiety reduction of 2.58 standardized t-score units. Results showed a nearly significant group–time interaction (p = .09), resulting in an effect size of 0.41. Additionally, the IG experienced greater anxiety reduction with more frequent engagement sessions (p = .06). However, there were no significant differences between the groups in terms of changes in depression, positive or negative emotion.	Not reported
Hauser-Ulrich et al. [30]	Germany and Switzerland	Chronic pain	Self-management of chronic pain	Pilot RCT	The study involved 102 participants recruited from the SELMA app. Their average age was 43.7 years. Of these, 14 were male and 88 were female. The duration of their participation in the study spanned two months.	Acceptance: 63% of users found the app fun, 47% found it useful, and 84% found it easy to use among 38 respondents. Adherence: the regular adherence percentage was 71% for 200 conversations initiated on Instagram.	There was a significant positive intention to alter behaviour concerning impairment and pain intensity (p = .01). There was no statistically significant alteration in pain-related impairment (p = .01) observed in the IG compared CG.	Not reported
Hunt et al. [31]	USA	Irritable bowel syndrome (IBS)	Education and self-management	RCT	121 adult with IBS, IG = 62, CG = 59	Completion rate 94%	Immediate treatment group improved significantly compared to wait-list CG in: Gastrointestinal symptom severity (Cohen’s d = 1.02, p < .001) QoL (Cohen’s d = 1.25, p < .001) GI-specific catastrophizing (Cohen’s d = 1.47, p < .001) Fear of food (Cohen’s d = 0.62, p = .001) Visceral anxiety (Cohen’s d = 1.07, p < .001) Depression (Cohen’s d = 1.07, p = .002) However, anxiety did not show a significant result for the IG.	Not reported
Tawfik et al. [32]	Egypt	Breast cancer	Education, self-management	RCT	150 participants (50 per group), aged 20+, recently diagnosed with early-stage non-metastatic breast cancer (stage 0–III), scheduled for first or second chemotherapy, able to read/write, and own a smartphone.	Easy to use: 94% Useful and informative responses: 94% Understanding: 72% Welcoming setup: 88% Women find it navigable: 70% Realistic and engaging personality: 72% Some reported mistakes: 76%	Participants showed significantly fewer, milder and less distressing symptoms, along with better self-care (p > .001), compared to nurse-led education and routine care groups.	Not reported

Figure 2. Risk of bias summary.

Table 2. Details of the AI-powered chatbots intervention in the included studies.

Study	Theory or core element	AI Methods	Duration	Type	Input	Output	Dialogue management	Dialogue Initiative	Task-Oriented	Therapist involved	Comparison
Al-Hilli et al. [26]	Health education and counseling	NLP	Flexible	Chatbot	Written	Written	Frame based	System	No	Yes	Traditional counselling
Bibault et al. [27]	Health education	Machine learning	Flexible	Web based or smartphone application	Written	Written	Frame based	System	Yes	No	Physician
Echeazarra et al. [28]	Education	Not reported	Daily, for 2 years	Smartphone app, chatbot	Written	Written	Finite state	System	No	Yes	Traditional writing
Gong et al. [29]	Behaviour change theories	NLP	Weekly up to one year	Smartphone app, chatbot	Spoken, and written	Spoken, and written	Frame based	System	Yes	Yes	Usual care
Greer et al. [14]	Stress and coping theory	Not reported	4 weeks exposure	Facebook messenger; chatbot.	Written	Written, visual	Finite state	System	Yes	No	Waitlist control
Hauser-Ulrich et al. [30]	Cognitive behaviour therapy, psychological therapy	Not reported	Every day, for 8 weeks	Smartphone app; chatbot	Written	Written, visual	Finite state	System	No	No	Education
Hunt et al. [31]	Cognitive behaviour therapy	NLU	8 weeks	Smartphone app; chatbot	Written	Written	Frame based	System	No	No	Waiting list control
Tawfik et al. [32]	Self-care behaviour	NLP	Flexible	WhatsApp; chatbot	Written	Written	Frame based	System	No	Yes	Nurse-led education and routine care

Table 3. Characterization of AI-powered chatbots [19].

Dialogue management	Finite-state	Users are guided through a predetermined series of steps or states in the dialogue.
	Frame-state	Users are presented with inquiries to fill in placeholders within a template, allowing for task-specific execution based on their input.
	Agent-based	Agent-based systems facilitate advanced communication between multiple agents, enabling them to reason about their own actions and beliefs while considering others’. The dialogue model in these systems adapts dynamically, incorporating contextual information at each interaction step.
Dialogue initiative	User	Users assume an active role in guiding and directing the ongoing conversation.
	System	The conversation is steered and directed by the system, with the system taking the lead in guiding the interaction.
	Mixed both	Both the user and the system have the capacity to initiate and guide the conversation, assuming the role of conversation leaders interchangeably.
Input modality	Spoken	Users interact with the system using spoken language.
	Written	Users engage with the system through written language for interaction purposes.
Output modality	Spoken, written and visual modes of communication encompass various channels of expression, including verbal interaction, written text and visual cues such as facial expressions and body movements.
Task-oriented	Yes	The system is designed for a specific task, engaging in concise conversations to acquire essential information and accomplish objectives, like scheduling consultations.
	No	The system’s objective is not solely focused on quickly achieving a predetermined outcome like casual chatbots do.

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Data availability statement

The data underpinning the results of this study can be obtained from the corresponding author upon a reasonable request.