1. Artificial Intelligence in Medicine: a survey of interests and beliefs of health researchers from four backgrounds Turma 7 Orientador: Pedro Pereira Rodrigues 18 de Maio de 2010 Faculdade de Medicina da Universidade do Porto Introdução à Medicina II

2. Contents Introduction Research question Aim Specific Objectives Study design Methods Statistical Analysis Results Conclusion References Acknowledgements

3. Introduction – AI definition Artificial Intelligence (AI) is, in a modern definition, the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions which maximise chances of success [1]. [1] VITERBO, L; AI: definition and recommended links; http://www.eeglossary.com/artificial- intelligence.htm; 01/10/2009, accessed on October 2009

4. Introduction – impacts and applications of AI The AIM field has already produced systems/programs/machines that demonstrate that the application of AI techniques to medical decision is a fruitful methodology. [2] Integrating AIM modules into the daily-routine software environment of our care providers gives us a great chance for maintaining and improving quality of care. [3] [2] SHORTLIFFE EH; AI in medicine comes of age: blossoming or languishing?; 2009; ICAART, 5-6 [3] ABBOD MF, LINKENS DA, DOUNIAS G, MAHFOUF M; Survey on the use of smart and adaptive engineering systems in medicine; Artif Intell Med; 26, 179-209; 2002

5. Research question Is Artificial Intelligence relevant and valuable for current applications in Medicine? And in the future?

6. Aim To survey the different interests and beliefs of four different types of health researchers about Artificial Intelligence in Medicine and its current and future applications.

7. Specific Objectives Assess the different opinions across different types of health researchers: researchers of artificial intelligence in medicine, general clinical practitioners, critical care researchers and medical school deans; Identify the different applications/areas of impact/medical fields of AI in Medicine;

8. Study the importance and the impact of AI in medical progress; Verify the contribution of medical researchers in the development of AI in Medicine; Infer on the future application of AI in Medicine. Specific Objectives

9. Study design Observational and transversal study, using an online survey, answered by: Directors of European Medical Schools AIM researchers Critical Care researchers General Care researchers

10. Methods 4 populations Directors of European Medical Schools Researchers of Artificial Intelligence Researchers of Critical Care Researchers of General Practice Journals AIM JAMIA SMMR Journals Critical Care Critical Care in Medicine Intensive Care Journals New England Lancet JAMA

11. Methods Contacts collection Journals Search for e-mails of researchers in each area in the site of the journal in articles of the last 3 years Deans Search for e-mails of directors of european medical schools in each university site Definition of samples Random selection of 100 contacts of each population

12. Methods – Internal Pilot The questionnaires were sent to teachers of the department to analize and give their opinion about them; After this, the surveys were reformulated and they were sent to the target samples.

13. Methods – survey Personalized e-mails were sent to each population. Specific information concerning AI in Medicine was provided to each group. As stated, we will send feedback on the results to the researchers who answered the survey.

14. Methods – sending the survey Send the survey to 100 e-mails of each population If the number of answer is low, new random selection is made in our populations The surveys are sent again

15. First 400 e-mails (for 400 contacts) – as the number of answers was low, the survey was sent to those who hadn't answered. We had to repeat this step 2 times. Other 400 e-mails were sent to other 400 contacts (randomly selected). E-mails were sent to a total number of 800 contacts. Methods – survey

16. Statistical Analysis All the analysis was made using the SPSS. Variables were recoded for better analysis and compared using mainly cross tabs. Chi-squared test was used to analise two categorical variables and Mann-Whitney test to analyse a continuous variable with a categorical one.

17. Statistical Analysis For Lickert Scale questions we used percentages and, if adequated, the median, minimum and maximum. As level of significance, we considered p=0,05.

18. 08/05/2010 – all the data received until this date was introduced in the database. Results – answer's rate Total number of answers: 92 (in 800)

19. 59% of the received answers were from the AIM population. Results – samples

20. Results - age

21. Results – Mann-Whitney test p = 0,031 p = 0,029 p = 0,001

22. Results – academic background

23. 56 researchers said that they had already contacted with AI. Results - “Have already contact with AI”

24. 67,7% of researchers said that the impact of AI in the experience was positive. Results - impact of AI in the experience

25. 0 Results - personal experience with AI 21,6% of researchers consider themselves as expert, considering experience with AI.

26. Results - areas in which AI was applied Globaly the most chosen application was “treatment”.

27. Results – AI – Medical Education The use of AI can enhance medical education in numerous domains. 53,5 % of researchers agree that AI has potential to enhance several domains of medical education.

28. Rate of the relevance given to AI by the educational institution Results – Specific Questions (Deans) 5 Directors of Medical schools answered that the relevance given to AI is adequate.

29. Medical Institutions should give more relevance to AI. Results – Specific Questions (Deans) 6 directors agree that Medical Institutions should give more relevance to AI and 1 disagrees.

30. In which situations do you consider the application of AI most useful, concerning to Critical Care? Results – Specific Questions (Critical Care) 4 Researchers of Critical Care said that the most useful application of AI, in what concerns to Critical Care Units is data monitoring.

31. "My investigation receives the necessary finantial support." Results – Specific Questions (AIM)

32. AI may improve the work of a general practioner. Results – Specific Questions (General Practice) Most researchers of General Practice agree that AI may improve their work.

33. Results – open answers 8 – What is the future scenario that you predict for the link between AI and Medicine? Do you believe that, nowadays, it is important to apply AI in Medicine? Why? What benefits may, from your point of view, come out of this association? Question:

34. Results – open answers AIM ● Clinical support for assigning therapies on an individual-patient basis will be the greatest benefit of this association; ● It is important to enhance AIM to confront the knowledge explosion in medicine; ● Improve care while controlling costs; ● Good predictive models (build with data mining techniques' help) could reveal the propensity for a certain disease; ● Decreasing the use of unnecessary diagnostic testing or treatments that have a high probability of failure.

35. Results – open answers Deans ● Introduction of AI in the curriculum of the medical education both in gradual and postgradual levels. ● Nowadays AI is not well introduced in Medicine. It is important to promote the applications of AI to: AI-based clinical decision- making; Intelligent medical information systems and databases; Meta-analysis in medicine; AI -based systems in medical education and research.

36. Results – open answers Critical Care ● Analysis of bedside brain multimodality monitoring data(ICP, PbtO2, CBF, EEG, microdialysis). ● Management, evaluation and interpretation of data currently generated by critically ill patients. ● I think I can help by remaining totally skeptical and trying to get others to see what a waste of time and money it is.

37. Results – open answers General Practice ● As we require new drugs to prove utility and effectiveness, we should require 'AI' systems to prove utility and effectiveness. ● Closed loop decision making. The system detects patient parameters, detects patterns of changes and suggests treatments by modeling many different scenarios. (…) ● Patients could explore possible diagnoses and their prognosis. They could also help determine their own individual preferences and how they impact care.

38. Results – Comparative analysis % within “have already contact with AI”: Population Vs Have already contact with AI

39. Results – Comparative analysis Population Vs AI future relevance P = 0,0001 - Significant results with the Chi-square test.

40. Results – Comparative analysis Academic Background Vs “I believe I can give a contribute to the development of AI in Medicine”. 84,6 % (44 of 52) of non-physicians agree with the statement 57,6 % (19 of 33) of physicians agree with the statement (p = 0,006 for Chi-square test - Significant results) Academic Background Vs “Governments should invest more in AI applications in Medicine”. 86,8 % (46 of 53) of non-physicians agree with the statement 52 % (19 of 34) of physicians agree with the statement (p = 0,001 for Chi-square test - Significant results)

41. Academic Background Vs “AI may increase treatment efficiency”. 64,7% (22 of 34) of physicians agree with the statement 86,8% (46 of 53) of non-physicians agree with the statement. (p = 0,015 for Chi-square test - Significant results) Academic Background Vs AI future relevance: 38,2% (14) of the physicians think that AI doesn’t have future relevance in Medicine or they don’t have a solid opinion. 83,0% (44) of the non physicians agree that AI has future relevance in Medicine. (p = 0,0001 for Chi-square test - Significant results) Results – Comparative analysis

42. Population Vs “Governments should invest more in AI applications in Medicine”. 88,2% (45 of 51) of the “AIM” participants agree with the statement. 55,6% (20 of 36) of the “NOT AIM” participants agree with the statement. (p=0,001 for Chi-square test - Significant results) Population Vs “AI is relevant for future applications in Medicine”. 82,4% (42 of 51) of the “AIM” participants agree with the statement. 41,7% (15 of 36) of the “NOT AIM” participants agree with the statement. (p = 0,0001 for Chi-square test - Significant results) Results – Comparative analysis

43. Population Vs “Governments should invest more in AI applications in Medicine”. 88,2% (45 of 51) of the “AIM” participants agree with the statement. 55,6% (20 of 36) of the “NOT AIM” participants agree with the statement. (p=0,001 for Chi-square test - Significant results) Population Vs “AI is relevant for future applications in Medicine”. 82,4% (42 of 51) of the “AIM” participants agree with the statement. 41,7% (15 of 36) of the “NOT AIM” participants agree with the statement. (p = 0,0001 for Chi-square test - Significant results) Results – Comparative analysis

44. AGE Vs “I believe I can give a contribute to the development of AI in Medicine” 82,8% (24) with age under 40 agree with the statement. 69,6% (16) aged between 41 and 50 agree with the statement. 73,7% (14) aged between 51 and 60 agree with the statement. 37,5% (3) with age above 61 agree with the statement. Results – Comparative analysis

45. Other Significant results Academic Background Vs Personal experience with AI In what concerns to personal experience, physicians assume to be less experienced than the “non-physicians” – the results are statistically significant (p=0,0001) for the Chi-Square test Nationality Vs “Governments should invest more in AI applications in Medicine” The European population does not support financial support, whereas the American population is divided. Both groups agree on the efficiency of AI; the percentage of “non-physicians” who agrees is higher than the physicians one. The results are statistically significant (p= 0,015) for the Chi-Square test.

46. Results – Comparative analysis Other Significant results Academic Background Vs “The use of AI can enhance medical education in numerous domains.” Both physicians and non-physicians believe that AI may improve medical teaching and although the non-physicians group has the highest “agree” rate, the discrepancy is not very large. The results are statistically significant (0,610), for Chi-Square test.

47. The definition of AI is not consensual, since it means many things to different people. Our definition in the survey's introduction probably was restrictive and some people possibly weren't able to diferentiate AI applications from other ones. Instead of the nationality, we should have asked the researchers' place of work. Results - reviews

48. Main Conclusions We obtained a greater number of answers from the AIM researchers group. This population is probably more receptive to this issue, as it consists on their work area. Both AIM and “not AIM” agree that AI will not increase the distance between doctor and patient, which indicates that this matter does not worry the populations investigated, but this opinion is remarkably stronger on the AIM group.

49. Main Conclusions There are more physicians disagreeing with the future applications of AI than the “non physicians”; in other words, the physicians showed to be more reticent in what concerns to the possible applications of AI in the future, whereas the “non physicians” are more optimistic. Overall, there is not a high divergence between people of different nationalities when it is said that the relevance of AIM has increased nowadays.

50. Main Conclusions We believe that this survey was relevant to distinguish opinions concerning to AI, within different academic backgrounds, social and demographic aspects.

51. Main Conclusions We think that another question may now arise: Does AI has real conditions to be applied and generalized in the Healthcare Institutions? Or will it be restricted to some applications in singular areas?

52. AI would certainly be useful in both situations...

53. References [1] VITERBO, L; AI: definition and recommended links; http://www.eeglossary.com/artificial-intelligence.htm; 01/10/2009, accessed on October 2009 [2] SHORTLIFFE EH; AI in medicine comes of age: blossoming or languishing?; 2009; ICAART, 5-6 [3] ABBOD MF, LINKENS DA, DOUNIAS G, MAHFOUF M; Survey on the use of smart and adaptive engineering systems in medicine; Artif Intell Med; 26, 179-209; 2002

54. Acknowledgments Eng. Jorge Jácome Gomes Prof. Pedro Pereira Rodrigues Dr. Altamiro Rodrigues Pereira