Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2024 Oct 27;16(10):e72501.
doi: 10.7759/cureus.72501. eCollection 2024 Oct.

Navigating AI: A Quick Start Guide for Healthcare Professionals

Piyush Mathur  1   2 , Hajra Arshad  3   2 , Rachel Grasfield  4   2 , Reem Khatib  1 , Avneep Aggarwal  1 , Moises Auron  5 , Avneesh Khare  6
Affiliations
Review

Navigating AI: A Quick Start Guide for Healthcare Professionals

Piyush Mathur et al. Cureus. .

Abstract

The rapid and nimble growth of artificial intelligence (AI) in healthcare has generated significant excitement among healthcare professionals. The most common question asked by clinicians about AI therefore is: "How do I get started?". We outline a strategic approach for clinicians to integrate AI into their knowledge base, focusing on goal setting, creating a learning roadmap, identifying essential resources, and establishing success metrics. We have outlined practical steps, including acquiring programming skills and utilizing low-code platforms, based on an individual's goal. Additionally, we present a resource toolkit that emphasizes continuous learning, collaboration, and mentorship to successfully adopt and implement AI in healthcare. We highlight the importance of understanding AI fundamentals and provide a roadmap to navigate a successful start.

Keywords: artificial intelligence; checklist; computer programming; deep learning; machine learning.

PubMed Disclaimer

Conflict of interest statement

Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: Piyush Mathur MD declare(s) non-financial support and stock/stock options from BrainX,LLC., BrainXCommunity, LLC. Co-founder of BrainX, LLC and BrainX Community,LLC. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.

Figures

Figure 1
Figure 1. Setting goals and developing a personalized roadmap for AI in healthcare
AI: artificial intelligence; ML: machine learning Image Credits: Hajra Arshad
Figure 2
Figure 2. Identifying key resources to support AI in healthcare learning and development
Image Credits: Hajra Arshad
Figure 3
Figure 3. Developing measures of success for AI in healthcare journey
Image Credits: Hajra Arshad
Figure 4
Figure 4. Navigating AI checklist to assist fulfill clinicians’ goals and measure success
Image Credits: Hajra Arshad
See this image and copyright information in PMC

References

    1. Awasthi R, Mishra M, Grasfield R, et al. Awasthi R, Mishra S, Grasfield R, et al. Artificial Intelligence in Healthcare: 2023 Year in Review. medRxiv. [ Jun; 2024 ]. 2023. https://doi.org/10.1101/2024.02.28.24303482 https://doi.org/10.1101/2024.02.28.24303482
    1. An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research. Zhang J, Whebell S, Gallifant J, et al. Lancet Digit Health. 2022;4:0–3. - PMC - PubMed
    1. Large language models encode clinical knowledge. Singhal K, Azizi S, Tu T, et al. https://doi.org/10.1038/s41586-023-06291-2. Nature. 2023;620:172–180. - PMC - PubMed
    1. Inclusion of clinicians in the development and evaluation of clinical artificial intelligence tools: A systematic literature review. Tulk Jesso S, Kelliher A, Sanghavi H, Martin T, Henrickson Parker S. Front Psychol. 2022;13 - PMC - PubMed
    1. Do no harm: A roadmap for responsible machine learning for health care. Wiens J, Saria S, Sendak M, et al. https://doi.org/10.1038/s41591-019-0548-6. Nat Med. 2019;25:1337–1340. - PubMed

LinkOut - more resources