The Clinical Researcher Journey in the Artificial Intelligence Era: The PAC-MAN's Challenge

Overview

Journal Healthcare (Basel)

Specialty Health Services

Date 2023 Apr 13

PMID 37046900

Authors

Elena Giovanna Bignami

Alessandro Vittori

Roberto Lanza

Christian Compagnone

Marco Cascella

Valentina Bellini

Affiliations

Soon will be listed here.

Abstract

Artificial intelligence (AI) is a powerful tool that can assist researchers and clinicians in various settings. However, like any technology, it must be used with caution and awareness as there are numerous potential pitfalls. To provide a creative analogy, we have likened research to the PAC-MAN classic arcade video game. Just as the protagonist of the game is constantly seeking data, researchers are constantly seeking information that must be acquired and managed within the constraints of the research rules. In our analogy, the obstacles that researchers face are represented by "ghosts", which symbolize major ethical concerns, low-quality data, legal issues, and educational challenges. In short, clinical researchers need to meticulously collect and analyze data from various sources, often navigating through intricate and nuanced challenges to ensure that the data they obtain are both precise and pertinent to their research inquiry. Reflecting on this analogy can foster a deeper comprehension of the significance of employing AI and other powerful technologies with heightened awareness and attentiveness.

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References

Harnett J . Research Ethics for Clinical Researchers. Methods Mol Biol. 2021; 2249:53-64. DOI: 10.1007/978-1-0716-1138-8_4. View

Collins G, Dhiman P, Andaur Navarro C, Ma J, Hooft L, Reitsma J . Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021; 11(7):e048008. PMC: 8273461. DOI: 10.1136/bmjopen-2020-048008. View

Yu K, Beam A, Kohane I . Artificial intelligence in healthcare. Nat Biomed Eng. 2019; 2(10):719-731. DOI: 10.1038/s41551-018-0305-z. View

Crossnohere N, Elsaid M, Paskett J, Bose-Brill S, Bridges J . Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks. J Med Internet Res. 2022; 24(8):e36823. PMC: 9459836. DOI: 10.2196/36823. View

Bellini V, Montomoli J, Bignami E . Poor quality data, privacy, lack of certifications: the lethal triad of new technologies in intensive care. Intensive Care Med. 2021; 47(9):1052-1053. DOI: 10.1007/s00134-021-06473-4. View

Hogg H, Al-Zubaidy M, Talks J, Denniston A, Kelly C, Malawana J . Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. J Med Internet Res. 2023; 25:e39742. PMC: 9875023. DOI: 10.2196/39742. View

Klumpp M, Hintze M, Immonen M, Rodenas-Rigla F, Pilati F, Aparicio-Martinez F . Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals. Healthcare (Basel). 2021; 9(8). PMC: 8393951. DOI: 10.3390/healthcare9080961. View

Groot Lipman K, de Gooijer C, Boellaard T, van der Heijden F, Beets-Tan R, Bodalal Z . Artificial intelligence-based diagnosis of asbestosis: analysis of a database with applicants for asbestosis state aid. Eur Radiol. 2022; 33(5):3557-3565. PMC: 10121486. DOI: 10.1007/s00330-022-09304-2. View

Ahn E . Introducing big data analysis using data from National Health Insurance Service. Korean J Anesthesiol. 2020; 73(3):205-211. PMC: 7280883. DOI: 10.4097/kja.20129. View

10.

Bellini V, Cascella M, Cutugno F, Russo M, Lanza R, Compagnone C . Understanding basic principles of Artificial Intelligence: a practical guide for intensivists. Acta Biomed. 2022; 93(5):e2022297. PMC: 9686179. DOI: 10.23750/abm.v93i5.13626. View

11.

Petrucci E, Vittori A, Cascella M, Vergallo A, Fiore G, Luciani A . Litigation in Anesthesia and Intensive Care Units: An Italian Retrospective Study. Healthcare (Basel). 2021; 9(8). PMC: 8392650. DOI: 10.3390/healthcare9081012. View

12.

Cascella M, Racca E, Nappi A, Coluccia S, Maione S, Luongo L . Bayesian Network Analysis for Prediction of Unplanned Hospital Readmissions of Cancer Patients with Breakthrough Cancer Pain and Complex Care Needs. Healthcare (Basel). 2022; 10(10). PMC: 9601725. DOI: 10.3390/healthcare10101853. View

13.

Vasey B, Nagendran M, Campbell B, Clifton D, Collins G, Denaxas S . Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022; 28(5):924-933. DOI: 10.1038/s41591-022-01772-9. View

14.

Moussaid A, Zrira N, Benmiloud I, Farahat Z, Karmoun Y, Benzidia Y . On the Implementation of a Post-Pandemic Deep Learning Algorithm Based on a Hybrid CT-Scan/X-ray Images Classification Applied to Pneumonia Categories. Healthcare (Basel). 2023; 11(5). PMC: 10000749. DOI: 10.3390/healthcare11050662. View

15.

Cruz Rivera S, Liu X, Chan A, Denniston A, Calvert M . Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med. 2020; 26(9):1351-1363. PMC: 7598944. DOI: 10.1038/s41591-020-1037-7. View

16.

Esteva A, Kuprel B, Novoa R, Ko J, Swetter S, Blau H . Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639):115-118. PMC: 8382232. DOI: 10.1038/nature21056. View

17.

Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y . Deep learning in clinical natural language processing: a methodical review. J Am Med Inform Assoc. 2019; 27(3):457-470. PMC: 7025365. DOI: 10.1093/jamia/ocz200. View

18.

Szajewska H . Evidence-Based Medicine and Clinical Research: Both Are Needed, Neither Is Perfect. Ann Nutr Metab. 2018; 72 Suppl 3:13-23. DOI: 10.1159/000487375. View

19.

Cascella M, Coluccia S, Monaco F, Schiavo D, Nocerino D, Grizzuti M . Different Machine Learning Approaches for Implementing Telehealth-Based Cancer Pain Management Strategies. J Clin Med. 2022; 11(18). PMC: 9502863. DOI: 10.3390/jcm11185484. View

20.

Bellini V, Valente M, Pelosi P, Del Rio P, Bignami E . Big Data and Artificial Intelligence in Intensive Care Unit: From "Bla, Bla, Bla" to the Incredible Five V's. Neurocrit Care. 2022; 37(Suppl 2):170-172. DOI: 10.1007/s12028-022-01472-9. View