Evidence map›Paper›PMID 37519896›Full record

ReviewFrontiers in digital health2023

Conceptualizing socially-assistive robots as a digital therapeutic tool in healthcare.

Cedomir Stanojevic, Casey C Bennett, Selma Sabanovic, Sawyer Collins, Kenna Baugus Henkel, Zachary Henkel, Jennifer A Piatt

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Can social robots improve the hospital experience of children?Frontiers in rehabilitation sciences · 2026
    Article
  3. Article
  4. Adoption of Digital Therapeutics in Europe.Therapeutics and clinical risk management · 2024
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Cedomir StanojevicDepartment of Parks Recreation and Tourism Management, College of Behavioral and Social Health Sciences, Clemson University, Clemson, SC, United States.
Casey C BennettDepartment of Intelligence Computing, Hanyang University, Seoul, Republic of Korea.
Selma SabanovicSchool of Informatics and Computing, Indiana University, Bloomington, IN, United States.
Sawyer CollinsSchool of Informatics and Computing, Indiana University, Bloomington, IN, United States.
Kenna Baugus HenkelDepartment of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, United States.
Zachary HenkelDepartment of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, United States.
Jennifer A PiattDepartment of Health and Wellness Design, School of Public Health, Indiana University, Bloomington, IN, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI)-driven Digital Health (DH) systems are poised to play a critical role in the future of healthcare. In 2021, $57.2 billion was invested in DH systems around the world, recognizing the promise this concept holds for aiding in delivery and care management. DH systems traditionally include a blend of various technologies, AI, and physiological biomarkers and have shown a potential to provide support for individuals with various health conditions. Digital therapeutics (DTx) is a more specific set of technology-enabled interventions within the broader DH sphere intended to produce a measurable therapeutic effect. DTx tools can empower both patients and healthcare providers, informing the course of treatment through data-driven interventions while collecting data in real-time and potentially reducing the number of patient office visits needed. In particular, socially assistive robots (SARs), as a DTx tool, can be a beneficial asset to DH systems since data gathered from sensors onboard the robot can help identify in-home behaviors, activity patterns, and health status of patients remotely. Furthermore, linking the robotic sensor data to other DH system components, and enabling SAR to function as part of an Internet of Things (IoT) ecosystem, can create a broader picture of patient health outcomes. The main challenge with DTx, and DH systems in general, is that the sheer volume and limited oversight of different DH systems and DTxs is hindering validation efforts (from technical, clinical, system, and privacy standpoints) and consequently slowing widespread adoption of these treatment tools.

Indexed as

artificial intelligencedigital healthdigital therapeuticsinternet of thingsmachine learningsocially assistive robots

Identifiers

PMID37519896
PMCPMC10382135

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.