CoChat
Sign Up
Cited by 61Artificial Intelligence in Healthcare and EducationHuman-Automation Interaction and SafetyRadiology practices and education

Improving the radiologist-CAD interaction: designing for appropriate trust

Open access
Jorritsma, W., Cnossen, F., van Ooijen, P.M.A.·2015·Clinical Radiology
DOI 10.1016/j.crad.2014.09.017
Open this paper in CoChat — free account

Abstract

Computer-aided diagnosis (CAD) has great potential to improve radiologists' diagnostic performance. However, the reported performance of the radiologist-CAD team is lower than what might be expected based on the performance of the radiologist and the CAD system in isolation. This indicates that the interaction between radiologists and the CAD system is not optimal. An important factor in the interaction between humans and automated aids (such as CAD) is trust. Suboptimal performance of the human-automation team is often caused by an inappropriate level of trust in the automation. In this review, we examine the role of trust in the radiologist-CAD interaction and suggest ways to improve the output of the CAD system so that it allows radiologists to calibrate their trust in the CAD system more effectively. Observer studies of the CAD systems show that radiologists often have an inappropriate level of trust in the CAD system. They sometimes under-trust CAD, thereby reducing its potential benefits, and sometimes over-trust it, leading to diagnostic errors they would not have made without CAD. Based on the literature on trust in human-automation interaction and the results of CAD observer studies, we have identified four ways to improve the output of CAD so that it allows radiologists to form a more appropriate level of trust in CAD. Designing CAD systems for appropriate trust is important and can improve the performance of the radiologist-CAD team. Future CAD research and development should acknowledge the importance of the radiologist-CAD interaction, and specifically the role of trust therein, in order to create the perfect artificial partner for the radiologist. This review focuses on the role of trust in the radiologist-CAD interaction. The aim of the review is to encourage CAD developers to design for appropriate trust and thereby improve the performance of the radiologist-CAD team.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.crad.2014.09.017

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 73

  1. [1]1. [Beam CA, Layde PM, Sullivan DC. Variability in the interpretation of screening mammograms by US radiologists: fi ndings from a national sample. Arch Intern Med 1996; 156 :209 e 13.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref1)
  2. [2]2. [Berg WA, Campassi C, Langenberg P, et al. Breast imaging reporting and data system: inter- and intraobserver variability in feature analysis and fi nal assessment. Am J Roentgenol 2000; 174 :1769 e 77.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref2)
  3. [3]3. [Kundel HL, Nodine CF, Carmody D. Visual scanning, pattern recognition and decision-making in pulmonary nodule detection. Invest Radiol 1978; 13 :175 e 81.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref3)
  4. [4]4. [Austin JH, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma: radiographic fi ndings in 27 patients with a potentially resectable lesion evident in retrospect. Radiology 1992; 182 :115 e 22.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref4)
  5. [5]5. [Pinto A, Brunese L. Spectrum of diagnostic errors in radiology. World J Radiol 2010; 2 :377 e 83.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref5)
  6. [6]6. [Fitzgerald R. Error in radiology. Clin Radiol 2001; 56 :938 e 46.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref6)
  7. [7]7. [Robinson PJA. Radiology ' s Achilles ' heel: error and variation in the interpretation of the R € ontgen image. Br J Radiol 1997; 70 :1085 e 98.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref7)
  8. [8]8. [Renfrew DL, Franken EA, Berbaum KS, et al. Error in radiology: classifi cation and lessons in 182 cases presented at a problem case conference. Radiology 1992; 183 :145 e 50.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref8)
  9. [9]9. [Martin JE, Moskowitz M, Milbrath JR. Breast cancer missed by mammography. Am J Roentgenol 1979; 132 :737 e 9.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref9)
  10. [10]10. [Chan H, Doi K, Vyborny CJ, et al. Improvement in radiologists ' detection of clustered microcalci fi cations on mammograms: the potential of computer-aided diagnosis. Invest Radiol 1990; 25 :1102 e 10.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref10)
  11. [11]11. [Nawano S, Murakami K, Moriyama N, et al. Computer-aided diagnosis in full digital mammography. Invest Radiol 1999; 34 :310 e 6.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref11)
  12. [12]12. [Awai K, Murao K, Ozawa A, et al. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists ' detection performance. Radiology 2004; 230 :347 e 52.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref12)
  13. [13]13. [Li F, Aoyama M, Shiraishi J, et al. Radiologists ' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. Am J Roentgenol 2004; 183 :1209 e 15.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref13)
  14. [14]14. [Kasai S, Li F, Shiraishi J, et al. Usefulness of computer-aided diagnosis schemes for vertebral fractures and lung nodules on chest radiographs. Am J Roentgenol 2008; 191 :260 e 5.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref14)
  15. [15]15. [White CS, Pugatch R, Koonce T, et al. Lung nodule CAD software as a second reader: a multicenter study. Acad Radiol 2008; 15 :326 e 33.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref15)
  16. [16]16. [Drew T, Cunningham C, Wolfe JM. When and why might a computeraided detection (CAD) system interfere with visual search? An eyetracking study. Acad Radiol 2012; 19 :1260 e 7.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref16)
  17. [17]17. [Nishikawa RM, Schmidt RA, Linver MN, et al. Clinically missed cancer: how effectively can radiologists use computer-aided detection? Am J Roentgenol 2012; 198 :708 e 16.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref17)
  18. [18]18. [Brem RF, Schoonjans JM. Radiologist detection of microcalci fi cations with and without computer-aided detection: a comparative study. Clin Radiol 2001; 56 :150 e 4.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref18)
  19. [19]19. [Taylor PM, Champness J, Given-Wilson RM, et al. An evaluation of the impact of computer-based prompts on screen readers ' interpretation of mammograms. Br J Radiol 2004; 77 :21 e 7.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref19)
  20. [20]20. [Taylor P, Given-Wilson R, Champness J, et al. Assessing the impact of CAD on the sensitivity and speci fi city of fi lm readers. Clin Radiol 2004; 59 :1099 e 105.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref20)
  21. [21]21. [Zheng B, Swensson RG, Golla S, et al. Detection and classi fi cation performance levels of mammographic masses under different computeraided detection cueing environments. Acad Radiol 2004; 11 :398 e 406.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref21)
  22. [22]22. [Gur D, Sumkin JH, Rockette HE, et al. Changes in breast cancer detection and mammography recall rates after the introduction of a computeraided detection system. J Natl Cancer Inst 2004; 96 :185 e 90.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref22)
  23. [23]23. [De Hoop B, De Boo DW, Gietema HA, et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. Radiology 2010; 257 :532 e 40.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref23)
  24. [24]24. [Brown MS, Goldin JG, Rogers S, et al. Computer-aided lung nodule detection in CT: results of large-scale observer test. Acad Radiol 2005; 12 :681 e 6.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref24)
  25. [25]25. [Petrick N, Haider M, Summers RM, et al. CT colonography with computer-aided detection as a second reader: observer performance study. Radiology 2008; 246 :148 e 56.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref25)
  26. [26]26. [Povyakalo AA, Alberdi E, Strigini L, et al. How to discriminate between computer-aided and computer-hindered decisions: a case study in mammography. Med Decis Mak 2013; 33 :98 e 107.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref26)
  27. [27]27. [Lee JD, Moray N. Trust, self-con fi dence, and operators ' adaptation to automation. Int J Hum Comput Stud 1994; 40 :153 e 84.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref27)
  28. [28]28. [Lee J, Moray N. Trust, control strategies and allocation of function in human-machine systems. Ergonomics 1992; 35 :1243 e 70.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref28)
  29. [30]30. [Muir BM. Trust between humans and machines, and the design of decision aids. Int J Man Mach Stud 1987; 27 :527 e 39.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref30)
  30. [31]31. [Wickens CD, Hollands JG. Engineering psychology and human performance . 3rd ed. Upper Saddle River, NJ: Prentice Hall; 1999.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref31)
  31. [36]36. [Beck HP, Dzindolet MT, Pierce LG. Automation usage decisions: controlling intent and appraisal errors in a target detection task. Hum Factors 2007; 49 :429 e 37.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref36)
  32. [37]37. [Wang L, Jamieson GA, Hollands JG. Trust and reliance on an automated combat identi fi cation system. Hum Factors 2009; 51 :281 e 91.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref37)
  33. [38]38. [Nishikawa RM, Edwards A, Schmidt RA, et al. Can radiologists recognize that a computer has identi fi ed cancers that they have overlooked?. In: Proceedings of SPIE 6146 . San Diego, CA: SPIE; 2006. p. 1 e 8.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref38)
  34. [39]39. [De Boo DW, Uffmann M, Weber M, et al. Computer-aided detection of small pulmonary nodules in chest radiographs: an observer study. Acad Radiol 2011; 18 :1507 e 14.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref39)
  35. [40]40. [Lee KH, Goo JM, Park CM, et al. Computer-aided detection of malignant lung nodules on chest radiographs: effect on observers ' performance. Korean J Radiol 2012; 13 :564 e 71.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref40)
  36. [41]41. [Halligan S, Altman DG, Mallett S, et al. Computed tomographic colonography: assessment of radiologist performance with and without computer-aided detection. Gastroenterology 2006; 131 :1690 e 9.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref41)
  37. [42]42. [Taylor SA, Robinson C, Boone D, et al. Polyp characteristics correctly annotated by computer-aided detection software but ignored by reporting radiologists during CT colonography. Radiology 2009; 253 :715 e 23.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref42)
  38. [43]43. [Shiraishi J, Abe H, Engelmann R, et al. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists ' performance d initial experience. Radiology 2003; 227 :469 e 74.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref43)
  39. [45]45. [Itoh M. Toward over-trust-free advanced driver assistance systems. Cogn Technol Work 2012; 14 :51 e 60.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref45)
  40. [46]46. [Alberdi E, Povyakalo A, Strigini L, et al. Effects of incorrect computeraided detection (CAD) output on human decision-making in mammography. Acad Radiol 2004; 11 :909 e 18.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref46)
  41. [47]47. [Povyakalo AA, Alberdi E, Strigini L, et al. Evaluating ' human þ advisory computer ' systems: a case study. In: Proceedings of the 18th British HCI Group Annual Conference . Leeds: British HCI Group; 2004. p. 93 e 6.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref47)
  42. [48]48. [Zheng B, Ganott MA, Britton CA, et al. Breast imaging soft-copy mammographic readings with different computer-assisted detection cuing environments: preliminary fi ndings. Radiology 2001; 221 :633 e 40.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref48)
  43. [49]49. [Beyer F, Zierott L, Fallenberg EM, et al. Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader. Eur Radiol 2007; 17 :2941 e 7.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref49)
  44. [50]50. [Gilbert FJ, Astley SM, McGee MA, et al. Single reading with computeraided detection and double reading of screening mammograms in the United Kingdom national breast screening program. Radiology 2006; 241 :47 e 53.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref50)
  45. [51]51. [Shiraishi J, Abe H, Li F, et al. Computer-aided diagnosis for the detection and classi fi cation of lung cancers on chest radiographs: ROC analysis of radiologists ' performance. Acad Radiol 2006; 13 :995 e 1003.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref51)
  46. [52]52. [Samulski M, Hupse R, Boetes C, et al. Using computer-aided detection in mammography as a decision support. Eur Radiol 2010; 20 :2323 e 30.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref52)
  47. [53]53. [Hupse R, Samulski M, Lobbes M, et al. Standalone computer-aided detection compared to radiologists ' performance for the detection of mammographic masses. Eur Radiol 2013; 23 :93 e 100.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref53)
  48. [54]54. [Chan H, Sahiner B, Helvie MA, et al. Improvement of radiologists ' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. Radiology 1999; 212 :817 e 27.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref54)
  49. [55]55. [Hadjiiski L, Chan H, Sahiner B, et al. Improvement in radiologists ' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study. Radiology 2004; 233 :255 e 65.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref55)
  50. [56]56. [Horsch K, Giger ML, Vyborny CJ, et al. Classi fi cation of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set. Radiology 2006; 240 :357 e 68.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref56)
  51. [57]57. [Jiang Y, Nishikawa RM, Schmidt RA, et al. Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol 1999; 6 :22 e 33.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref57)
  52. [58]58. [Kashikura Y, Nakayama R, Hizukuri A, et al. Improved differential diagnosis of breast masses on ultrasonographic images with a computer-aided diagnosis scheme for determining histological classi fi cations. Acad Radiol 2013; 20 :471 e 7.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref58)
  53. [59]59. [Awai K, Murao K, Ozawa A, et al. Pulmonary nodules: estimation of malignancy at thin-section helical CT d effect of computer-aided diagnosis on performance of radiologists. Radiology 2006; 239 :276 e 84.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref59)
  54. [60]60. [Matsuki Y, Nakamura K, Watanabe H, et al. Usefulness of an arti fi cial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT. Am J Roentgenol 2002; 178 :657 e 63.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref60)
  55. [61]61. [Andre AD, Cutler HA. Displaying uncertainty in advanced navigation systems. In: Proceedings of the Human Factors and Ergonomics Society 42nd Annual Meeting . Santa Monica, CA: Human Factors and Ergonomics Society; 1998. p. 31 e 5.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref61)
  56. [63]63. [Beller J, Heesen M, Vollrath M. Improving the driver-automation interaction: an approach using automation uncertainty. Hum Factors 2013; 55 :1130 e 41.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref63)
  57. [64]64. [Madhavan P, Wiegmann DA, Lacson FC. Automation failures on tasks easily performed by operators undermine trust in automated aids. Hum Factors 2006; 48 :241 e 56.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref64)
  58. [65]65. [Hartswood M, Procter R, Rounce fi eld M, et al. ' Repairing ' the machine: a case study of the evaluation of computer-aided detection tools in breast screening. In: Proceedings of the 8th European Conference on Computer-Supported Cooperative Work . Helsinki: Kluwer Academic Publishers; 2003. p. 375 e 94.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref65)
  59. [66]66. [Malich A, Sauner D, Marx C, et al. In fl uence of breast lesion size and histologic fi ndings on tumor detection rate of a computer-aided detection system. Radiology 2003; 228 :851 e 6.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref66)
  60. [67]67. [Bahner JE, H € uper A, Manzey D. Misuse of automated decision aids: complacency, automation bias and the impact of training experience. Int J Hum Comput Stud 2008; 66 :688 e 99.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref67)
  61. [69]69. [Ye LR, Johnson PE. The impact of explanation facilities on user acceptance of expert systems advice. MIS Q 1995; 19 :157 e 72.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref69)
  62. [70]70. [Eining MM, Jones DR, Loebbecke JK. Reliance on decision aids: an examination of auditors ' assessment of management fraud. Audit A J Pract Theory 1997; 16 :1 e 19.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref70)
  63. [71]71. [Herlocker JL, Konstan JA, Riedl J. Explaining collaborative fi ltering recommendations. In: Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work . Philadelphia: ACM Press; 2000. p. 241 e 50.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref71)
  64. [72]72. [Cramer H, Evers V, Ramlal S, et al. The effects of transparency on trust in and acceptance of a content-based art recommender. User Model UserAdapt Interact 2008; 18 :455 e 96.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref72)
  65. [73]73. [Wang W, Benbasat I. Recommendation agents for electronic commerce: effects of explanation facilities on trusting beliefs. J Manag Inf Syst 2007; 23 :217 e 46.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref73)
  66. [75]75. [Gurung J, Maataoui A, Khan M, et al. Automated detection of lung nodules in multidetector CT: in fl uence of different reconstruction protocols on performance of a software prototype. Fortschr R € ontgenstr 2006; 178 :71 e 7.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref75)
  67. [76]76. [Marten K, Grillh € osl A, Seyfarth T, et al. Computer-assisted detection of pulmonary nodules: evaluation of diagnostic performance using an expert knowledge-based detection system with variable reconstruction slice thickness settings. Eur Radiol 2005; 15 :203 e 12.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref76)
  68. [77]77. [Petrick N, Sahiner B, Armato SG, et al. Evaluation of computer-aided detection and diagnosis systems. Med Phys 2013; 40 :087001.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref77)
  69. [78]78. [Roy MC, Dewit O, Aubert BA. The impact of interface usability on trust in web retailers. Internet Res 2001; 11 :388 e 98.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref78)
  70. [79]79. [Huo Z, Summers RM, Paquerault S, et al. Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use. Med Phys 2013; 40 :077001.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref79)
  71. [80]80. [Wang Y, Jiang S, Wang H, et al. CAD algorithms for solid breast masses discrimination: evaluation of the accuracy and interobserver variability. Ultrasound Med Biol 2010; 36 :1273 e 81.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref80)
  72. [81]81. [Balleyguier C, Kinkel K, Fermanian J, et al. Computer-aided detection (CAD) in mammography: does it help the junior or the senior radiologist? Eur J Radiol 2005; 54 :90 e 6.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref81)
  73. [82]82. [Kobayashi T, Xu XW, MacMahon H, et al. Effect of a computer-aided diagnosis scheme on radiologists ' performance in detection of lung nodules on radiographs. Radiology 1996; 199 :843 e 8.](http://refhub.elsevier.com/S0009-9260(14)00453-X/sref82)

Cited by

Sorted by citations

Authors
Jorritsma, W., Cnossen, F., & van Ooijen, P. M. A.
Affiliations
  • University Medical Center Groningen (NL)
  • University of Groningen (NL)
Corresponding author
Jorritsma, W.
ORCID
Cnossen, F.
Venue
Clinical Radiology
Publisher
Elsevier BV
Published
2015-02
Volume
70
Issue
2
Pages
115-122
ISSN
0009-9260 (ISSN-L)
DOI
10.1016/j.crad.2014.09.017
OpenAlex
W2158646765
Cited by
61
References
8
Citations by year
2026: 3 · 2025: 6 · 2024: 11 · 2023: 3 · 2022: 5 · 2021: 8 · 2020: 8 · 2019: 6 · 2018: 6 · 2017: 2 · 2016: 3
FWCI
0.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Clinical Radiology (publishedVersion)
  • PubMed (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
Keywords
computer-aided diagnosis, trust in automation, human-automation interaction, CAD system design, diagnostic performance, observer study, diagnostic errors, trust calibration
MeSH terms
Attitude to Computers, Humans, Radiographic Image Interpretation, Computer-Assisted, Radiology, Reproducibility of Results, Trust

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review