Literature DB >> 32611217

Public Perceptions of Artificial Intelligence and Robotics in Medicine.

Bethany Stai1, Nick Heller1, Sean McSweeney2, Jack Rickman1, Paul Blake2, Ranveer Vasdev2, Zach Edgerton2, Resha Tejpaul1, Matt Peterson2, Joel Rosenberg2, Arveen Kalapara2, Subodh Regmi2, Nikolaos Papanikolopoulos1, Christopher Weight2.   

Abstract

Objective: To understand better the public perception and comprehension of medical technology such as artificial intelligence (AI) and robotic surgery. In addition to this, to identify sensitivity to their use to ensure acceptability and quality of counseling. Subjects and
Methods: A survey was conducted on a convenience sample of visitors to the MN Minnesota State Fair (n = 264). Participants were randomized to receive one of two similar surveys. In the first, a diagnosis was made by a physician and in the second by an AI application to compare confidence in human and computer-based diagnosis.
Results: The median age of participants was 45 (interquartile range 28-59), 58% were female (n = 154) vs 42% male (n = 110), 69% had completed at least a bachelor's degree, 88% were Caucasian (n = 233) vs 12% ethnic minorities (n = 31) and were from 12 states, mostly from the Upper Midwest. Participants had nearly equal trust in AI vs physician diagnoses. However, they were significantly more likely to trust an AI diagnosis of cancer over a doctor's diagnosis when responding to the version of the survey that suggested that an AI could make medical diagnoses (p = 9.32e-06). Though 55% of respondents (n = 145) reported that they were uncomfortable with automated robotic surgery, the majority of the individuals surveyed (88%) mistakenly believed that partially autonomous surgery was already happening. Almost all (94%, n = 249) stated that they would be willing to pay for a review of medical imaging by an AI if available.
Conclusion: Most participants express confidence in AI providing medical diagnoses, sometimes even over human physicians. Participants generally express concern with surgical AI, but they mistakenly believe that it is already being performed. As AI applications increase in medical practice, health care providers should be cognizant of the potential amount of misinformation and sensitivity that patients have to how such technology is represented.

Entities:  

Keywords:  active surveillance; artificial intelligence; patient counseling; robotic surgery

Mesh:

Year:  2020        PMID: 32611217      PMCID: PMC7578175          DOI: 10.1089/end.2020.0137

Source DB:  PubMed          Journal:  J Endourol        ISSN: 0892-7790            Impact factor:   2.942


  7 in total

1.  Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.

Authors:  James H Thrall; Xiang Li; Quanzheng Li; Cinthia Cruz; Synho Do; Keith Dreyer; James Brink
Journal:  J Am Coll Radiol       Date:  2018-02-04       Impact factor: 5.532

2.  Public perceptions on robotic surgery, hospitals with robots, and surgeons that use them.

Authors:  Joshua A Boys; Evan T Alicuben; Michael J DeMeester; Stephanie G Worrell; Daniel S Oh; Jeffrey A Hagen; Steven R DeMeester
Journal:  Surg Endosc       Date:  2015-07-15       Impact factor: 4.584

Review 3.  Predictive models in urology.

Authors:  Andrea Cestari
Journal:  Urologia       Date:  2013-02-14

4.  Robotic surgery: current perceptions and the clinical evidence.

Authors:  Arif Ahmad; Zoha F Ahmad; Jared D Carleton; Ashish Agarwala
Journal:  Surg Endosc       Date:  2016-05-18       Impact factor: 4.584

5.  Illness uncertainty and quality of life of patients with small renal tumors undergoing watchful waiting: a 2-year prospective study.

Authors:  Patricia A Parker; Frances Alba; Bryan Fellman; Diana L Urbauer; Yisheng Li; Jose A Karam; Nizar Tannir; Eric Jonasch; Christopher G Wood; Surena F Matin
Journal:  Eur Urol       Date:  2013-02-09       Impact factor: 20.096

Review 6.  Artificial Intelligence in Precision Cardiovascular Medicine.

Authors:  Chayakrit Krittanawong; HongJu Zhang; Zhen Wang; Mehmet Aydar; Takeshi Kitai
Journal:  J Am Coll Cardiol       Date:  2017-05-30       Impact factor: 24.094

Review 7.  Future of robotic surgery in urology.

Authors:  Jens J Rassweiler; Riccardo Autorino; Jan Klein; Alex Mottrie; Ali Serdar Goezen; Jens-Uwe Stolzenburg; Koon H Rha; Marc Schurr; Jihad Kaouk; Vipul Patel; Prokar Dasgupta; Evangelos Liatsikos
Journal:  BJU Int       Date:  2017-04-22       Impact factor: 5.588

  7 in total
  7 in total

1.  Anticipating Ambulatory Automation: Potential Applications of Administrative and Clinical Automation in Outpatient Healthcare Delivery.

Authors:  Kevin Yang; Vinod E Nambudiri
Journal:  Appl Clin Inform       Date:  2021-12-29       Impact factor: 2.342

2.  Computer-Generated R.E.N.A.L. Nephrometry Scores Yield Comparable Predictive Results to Those of Human-Expert Scores in Predicting Oncologic and Perioperative Outcomes.

Authors:  N Heller; R Tejpaul; F Isensee; T Benidir; M Hofmann; P Blake; Z Rengal; K Moore; N Sathianathen; A Kalapara; J Rosenberg; S Peterson; E Walczak; A Kutikov; R G Uzzo; D A Palacios; E M Remer; S C Campbell; N Papanikolopoulos; Christopher J Weight
Journal:  J Urol       Date:  2021-12-30       Impact factor: 7.600

3.  Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey.

Authors:  Thomas Ploug; Anna Sundby; Thomas B Moeslund; Søren Holm
Journal:  J Med Internet Res       Date:  2021-12-13       Impact factor: 5.428

4.  Public understanding of artificial intelligence through entertainment media.

Authors:  Karim Nader; Paul Toprac; Suzanne Scott; Samuel Baker
Journal:  AI Soc       Date:  2022-04-02

Review 5.  Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review.

Authors:  Han Shi Jocelyn Chew; Palakorn Achananuparp
Journal:  J Med Internet Res       Date:  2022-01-14       Impact factor: 5.428

6.  COVID-19 and public support for autonomous technologies-Did the pandemic catalyze a world of robots?

Authors:  Michael C Horowitz; Lauren Kahn; Julia Macdonald; Jacquelyn Schneider
Journal:  PLoS One       Date:  2022-09-28       Impact factor: 3.752

7.  Drivers behind the public perception of artificial intelligence: insights from major Australian cities.

Authors:  Tan Yigitcanlar; Kenan Degirmenci; Tommi Inkinen
Journal:  AI Soc       Date:  2022-10-03
  7 in total

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