Artificial Intelligence–Based Consumer Health Informatics Application: Scoping Review

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Abstract

Background

There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health.

Objective

This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care.

Methods

We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review.

Results

We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients.

Conclusions

This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients’ perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow.

Related collections

Most cited references 78

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PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

Adrian Aldcroft, Chantelle Garritty, Simon Lewin … (2018)

Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.

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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin (2019)

Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward - it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.

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Artificial intelligence in healthcare

Kun-Hsing Yu, Andrew Beam, Isaac Kohane (2018)

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Author and article information

Contributors

Onur Asan:

ORCID: https://orcid.org/0000-0002-9239-3723

School of Systems and EnterprisesStevens Institute of Technology1 Castle Point TerraceHoboken, NJ, 07030United States1 4145264330oasan@stevens.edu

Journal

Journal ID (nlm-ta): J Med Internet Res

Journal ID (iso-abbrev): J Med Internet Res

Journal ID (publisher-id): JMIR

Title: Journal of Medical Internet Research

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Print): 1439-4456

ISSN (Electronic): 1438-8871

Publication date Collection: 2023

Publication date (Electronic): 30 August 2023

Volume: 25

Electronic Location Identifier: e47260

Affiliations

[1 ] School of Systems and Enterprises Stevens Institute of Technology Hoboken, NJ United States

[2 ] Department of Computer Science Stevens Institute of Technology Hoboken, NJ United States

[3 ] Department of Industrial Engieering University of Louisville Louisville, KY United States

Author notes

Corresponding Author: Onur Asan oasan@ 123456stevens.edu

Author information

Onur Asan https://orcid.org/0000-0002-9239-3723

Euiji Choi https://orcid.org/0000-0002-3715-4877

Xiaomei Wang https://orcid.org/0000-0002-3432-0227

Article

Publisher ID: v25i1e47260

DOI: 10.2196/47260

PMC ID: 10500367

PubMed ID: 37647122

SO-VID: eb4a16b4-5084-4004-8a24-650f18a96dc8

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

History

Date received : 13 March 2023

Date revision requested : 23 June 2023

Date revision received : 2 July 2023

Date accepted : 18 July 2023

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