Attitude and Readiness towards Artificial Intelligence and its Utilisation: A Cross-sectional Study among Undergraduate Medical Students in a Medical College, Kolkata

Attitude and Readiness towards
Artificial Intelligence and its
Utilisation: A Cross-sectional
Study among Undergraduate
Medical Students in a Medical
College, Kolkata
Group B1 of Batch 2022 (Roll nos. 51-75)

Introduction
• Artificial Intelligence (AI): The theory and development of computer
systems able to perform tasks normally requiring human intelligence, such
as visual perception, speech recognition, decision-making, and translation
between languages.[1]
• The advantages of using AI in medicine are that many AI-driven software,
chatbots, and robotics surgery can help in increasing knowledge and
efficiency, diagnosing, counseling patients, and assisting surgeons during
procedures to reduce human errors and increase precision.[2]
1) Catherine Soanes, Sara Hawker, Julia Elliott (eds). Pocket Oxford English Dictionary. 10th Edn.New Delhi.Oxford University Press.2005;44
2) Driver CN, Bowles BS, Bartholmai BJ, Greenberg-Worisek AJ. Artificial Intelligence in Radiology: A Call for Thoughtful Application. Clin Transl Sci. 2020
Mar;13(2):216-218.
2

Introduction (contd.)
• A study conducted by Al Zaabi A et al. in 2023 in Gujarat, India, revealed
that 87.2% of physicians and medical students are interested in learning the
application of AI in healthcare.[3]
• Another study by Garrel et al. in 2023 in Germany, showed that every fourth
student (25.2%) uses AI-based tools and applications very frequently.[4]
3) AlZaabi A, AlMaskari S, et al. Are physicians and medical students ready for artificial intelligence applications in healthcare? Digit Health. 2023
Jan 26;9:20552076231152167.
4) von Garrel, J., Mayer, J. Artificial Intelligence in studies—use of ChatGPT and AI-based tools among students in Germany. Humanit Soc Sci
Commun 10, 799 (2023). https://doi.org/10.1057/s41599-023-02304-7
3

Introduction (contd.)
• There is dearth of studies regarding attitude, readiness, and utilization of AI
among undergraduate medical students in West Bengal.
• Hence, assessment of the attitude, readiness, and utilization of AI among the
undergraduate medical students is crucial for understanding the way AI is
gradually transforming healthcare, from the grassroot level, so that the future
doctors would be better equipped with the knowledge and skills for applying AI
in healthcare delivery judiciously.
• With this background in mind, the current study was conducted among
undergraduate students of a medical college in Kolkata.
4

Research Question
What is the attitude, readiness, and utilization of Artificial
Intelligence (AI) among the undergraduate medical students
of a medical college in Kolkata?
6

Objectives
1. To evaluate the attitude towards AI among undergraduate medical
students of a medical college in Kolkata
2. To identify the readiness towards AI among the study participants
3. To assess the utilization of AI among the study participants
8

Methodology
 Study Type: Descriptive, observational study
 Study Design: Cross-sectional
 Study Setting: Institute of Post Graduate Medical Education and Research
(IPGME&R), Kolkata
 Study Duration: 10th June 2024 to 6th July 2024
10
The M.B.B.S. course started in IPGME&R, Kolkata in 2004 with a batch
strength of 100 and since 2019 with the new C.B.M.E. curriculum, the
annual batch strength has been increased to 200. So, our study
population including the current four phases of M.B.B.S. was 800.

Methodology (contd.)
Figure 1. Timeline of the research project represented with a Gantt chart
11

 Study Participants: Undergraduate medical students belonging to Phase I, II,
and Part-1 and 2 of Phase III of IPGME&R, Kolkata
 Selection criteria:
• Inclusion criteria: Undergraduate medical students who filled up the
Microsoft form
• Exclusion criteria:
o The students who did not provide consent to participate in the study
o Those who could not be approached
12

 Sample size: The sample size (n) was obtained by using Cochran’s formula, which is-
n=z2pq/d2
o z → Standard normal deviate= 1.96,
o p → Prevalence (assuming readiness of medical students towards AI= 50%),
o q → (1-p)
o d → Relative error (10% of p)
o After applying 15% non-response rate, the final sample size was calculated as 443
[At Confidence Interval (CI) of 95%, power (1-β) = 80%]
 Sampling Technique: Simple random sampling
13

 Study tools: A predesigned, pretested, and semi-structured questionnaire,
collecting data across the following domains-
1. Sociodemographic characteristics of the study participants
2. Attitude towards AI: 11 questions were adopted from a questionnaire
previously developed by Sit et al. (to evaluate UK medical students’
attitudes toward AI) to assess attitude in this study. The attitude
questions were framed into a 5-point Likert scale, ranging from “strongly
agree” to “strongly disagree”.[4]
4) Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, Poon DS. Attitudes and perceptions of UK medical students towards artificial
intelligence and radiology: a multicentre survey. Insights Imaging. 2020 Feb 5;11(1):14.
14

 Study tools (contd.):
3. MAIRS-MS (Medical Artificial Intelligence Readiness Scale for Medical
Students) scale [4]: It is a reliable tool for evaluating the perceived
readiness levels of medical students on AI technology, and is validated by
Karaca et al. It is a 5-point Likert scale, consisting of 22 items for
assessment of readiness, divided under 4 domains: cognition, ability,
vision, and ethics.
4) Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, Poon DS. Attitudes and perceptions of UK medical students towards
artificial intelligence and radiology: a multicentre survey. Insights Imaging. 2020 Feb 5;11(1):14.
15

 Cognition domain (8 questions): knowledge of AI terminology and
logic
 Ability domain (8 questions): readiness in choosing AI applications
 Vision domain (3 questions): the ability to explain AI's limitations,
strengths, and weaknesses
 Ethics domain (3 questions): adherence to legal and ethical
regulations when using AI
16

Each item on the MAIRS-MS is scored between 1 (minimum) to 5 (maximum)
points as follows-
“Strongly disagree” is given a score of 1
“Disagree” is given a score of 2
“Neutral” is given a score of 3
“Agree” is given a score of 4
“Strongly agree” is given a score of 5
4. Utilisation of AI among the study participants
17

 Study technique:
• The questionnaire was prepared using Microsoft forms and shared with
the study participants through online method
• The study participants were well-versed in English language, so the
questionnaire was developed in English.
• Data collection was carried out for 1 week
18

Statistical analysis:
 Data were tabulated and analysed using Microsoft Office Excel (version 2021)
 Descriptive statistics were represented using Mean (± S.D.), Median, frequency,
and percentage, along with suitable diagrams wherever applicable
 Attitude towards AI was assessed by the Attitude scale developed by Sit et al.,
which is a 5-point Likert scale, having a total of 11 items for the attitude
domain, with options for each item ranging from ‘strongly agree’ (score 5) to
‘strongly disagree’ (score 1)
19

 Statistical analysis (contd.):
 The Item number 3 in the attitude domain of the scale was reversely scored
(‘strongly agree’- score 1; ‘strongly disagree- score 5’)
 Attitude was categorized as ‘favourable’ and ‘unfavourable’ based on the
median of the overall attitude score. Score ≥ median was classified as
‘favourable’ while score < median was considered as ‘unfavourable’
 Mean (± S.D.) scores for each of the four sub-domains under the readiness
domain of MAIRS-MS were calculated, and an overall Mean (± S.D.) score was
obtained by summing up the mean scores of the four sub-domains
20

 Ethical considerations:
 The proposal for the study was written to obtain clearance from the
Institutional Ethics Committee (IEC) of IPGME&R, Kolkata
 The study participants were explained the purpose of the study and online
consent (via Microsoft forms) was obtained from them
 Anonymity and confidentiality of data were maintained throughout the study
21

 Operational definitions:
1. Attitude: the way that one thinks and feels about somebody/something or
how you behave towards somebody/something that shows how you think and
feel [1]
2. Readiness: the state of being ready or prepared for something [1]
3. Utilization: the act of using something, especially for a practical purpose [1]
1) Catherine Soanes, Sara Hawker , Julia Elliott (eds). Pocket Oxford English Dictionary. 10th Edn. New Delhi.Oxford
University Press.2005;50, 751-2, 1017
22

 Workplan of the project:
Literature review was conducted via dedicated online databases (PubMed, Google
Scholar) for the selection of topic for the project
After topic selection, the title was finalized, proposal was written and submitted
for obtaining ethical clearance from the IEC of IPGME&R, Kolkata
The questionnaire was designed (in Microsoft Forms) and pretesting was done
Questionnaire was distributed via online method to the study participants
23

• Workplan of the project (contd.):
The purpose of the study was explained to the participants and electronic
consent was obtained from them through the Microsoft forms prior to data
collection
Data collection, analyses and interpretation were done
A PowerPoint presentation of the project was prepared
24

Table 1- Distribution of study participants according to their Age-group and Gender (n=443)
Socio-demographic characteristics Frequency Percentage (%)
Age group (in
completed years)
18 -21 256 57.78
22-24 153 34.53
≥ 25 34 7.62
Total 443 100
Gender
Male 329 74.26
Female 114 25.74
Total 443 100
Inference: 74.26% of the study participants were males and 57.78% belonged to the
age group of 18-21 years
26

Figure 2: Pie diagram showing the distribution of study participants according
to their Phase of MBBS (n=443)
Inference: 32.27% participants belonged to Phase I while 29.79% belonged to
Phase II of MBBS
32.27%
29.79%
18.05%
19.86%
Phase 1
Phase 2
Phase 3 part 1
Phase 3 part2
27

28
Table 2- Distribution of study participants according to their current and permanent
residences (n=443)
Socio-demographic characteristics Frequency Percentage (%)
Current residence
Hostel 273 61.62
Home 130 29.34
Paying Guest 40 9.02
Total 443 100
Permanent residence
Urban and/or
Suburban area
272 57.78
Rural area 171 34.53
Total 443 100
Inference: 61.62% of the study participants currently reside in hostel while 61.39%
of the participants belong to urban and/or sub-urban areas

29
Figures 3 and 4: Doughnut diagrams showing the distribution of study participants according to
the highest level of education attained by their fathers and mothers (n=443)
Inference: Fathers of 67.49% of participants and mothers of 53.95% of participants had completed
their graduation
1.58% 2.25%
8.35% 10.15%
23.70%
53.95%
Fig. 4. Mother's education
Illiterate Primary school
Middle school Secondary
Higher secondary Graduation & above
1.08%
1.08% 2.70%
5.64%
20.54%
67.49%
Fig. 3. Father's education
Illiterate Primary school
Middle school Secondary
Higher secondary Graduation & above

378
182
236 238
108
0
50
100
150
200
250
300
350
400
Social media Journals/textbooks Newspaper/
television
Friends Family
Figure 5: Bar diagram showing the distribution of study participants based on sources
of information regarding AI (n=443)*
Inference: Source of information for most of the study participants was social media
(378) followed by friends (238)
*multiple responses
30

Table 4: Distribution of study participants according to their attitude towards AI
(n=443)
Sl. No. Items
Strongly
Disagree
Disagree Neutral Agree
Strongly
Agree
1.
AI will play an important role in
healthcare
Frequency (%)
25
(5.64)
15
(3.38)
59
(13.32)
196
(44.24)
148
(33.42)
2.
I am less likely to consider a
career in radiology, given the
advancement of AI
54
(12.19)
99
(22.35)
150
(33.86)
96
(21.67)
44
(9.93)
3.
Some specialties will be replaced
by AI during my lifetime
44
(9.93)
64
(14.44)
97
(21.89)
167
(37.69)
71
(16.02)
31

Sl. No. Items
Strongly
Disagree
Strongly
Agree
4.
I have an understanding of the
basic computational principles of
AI
Frequency (%)
44
(9.93)
82
(18.51)
129
(29.11)
134
(30.25)
54
(12.20)
5.
I am comfortable with the
nomenclature related to artificial
intelligence
34
(7.67)
66
(14.89)
149
(33.69)
145
(32.73)
49
(11.06)
6.
I have an understanding of the
limitations of artificial intelligence
26
(5.68)
45
(10.15)
109
(24.60)
199
(44.92)
64
(14.44)
Table 4 contd.
32

Sl. No. Items
Strongly
Disagree
Strongly
Agree
7.
Teaching in artificial intelligence
will be beneficial for my career
Frequency(%)
27
(6.09)
33
(7.45)
118
(26.63)
165
(37.24)
100
(22.57)
8.
All medical students should
receive teaching in artificial
intelligence
34
(7.67)
45
(10.15)
82
(18.51)
167
(37.69)
115
(26.00)
9.
At the end of my medical degree, I
will be confident in using basic
healthcare AI tools if required
23
(5.19)
31
(6.99)
127
(28.67)
173
(39.06)
89
(20.00)
Table 4 contd.
33

Sl. No. Items
Strongly
Disagree
Strongly
Agree
10.
At the end of my medical degree, I
will better understand the methods
used to assess healthcare AI
algorithm performance.
Frequency (%)
22
(4.96)
33
(7.44)
118
(26.63)
193
(43.56)
77
(17.38)
11.
Overall, At the end of my medical
degree, I feel I will possess the
knowledge needed to work with AI
in routine clinical practice
22
(4.96)
38
(8.57)
119
(26.86)
178
(40.18)
86
(19.41)
Table 4 contd.
34

Inference from Table 4:
• 44.24% of the study participants have agreed that AI will play an important
role in healthcare
• 22.35% of the study participants have disagreed about considering career in
radiology, given the advancement of AI
• 37.69% of the study participants agreed that some specialties will be
replaced by AI in their lifetime
• 30.25% of the study participants agreed to have a basic understanding of
the computational principles of AI
35

Inference from Table 4 (contd.):
• 32.73% of the study participants agreed with the nomenclature related
to AI
• 44.92% of the study participants agreed that they had an understanding
of the limitations of AI
• 37.24% of the study participants agreed that teaching in AI will be
beneficial for careers
• 37.69% of the study participants agreed that all medical students should
receive teaching in AI
36

Inference from Table 4 (contd.):
• 39.06% of them agreed that they will be confident in using basic healthcare AI
tools if required at the end of their medical degree
• 43.56% agreed that they will have a better understanding of the methods
used to assess healthcare AI algorithm performance at the end of their
medical degree
• 40.18% agreed that they feel they will possess the knowledge needed to work
with AI in routine clinical practice at the end of their medical degree
37

Figure 6: Pie chart showing distribution of the study participants based on their
attitude towards AI (n=443)
Inference: 56.20% of the study participants had favourable attitude towards AI
56.20%
43.80% Favourable
Unfavourable
The median of the overall attitude score was 15
38

Figure 7: Component-Bar Diagram showing the distribution of study participants
according to their readiness on the cognitive factor towards AI implementation (n=443)
16.93%
12.19%
16.70%
16.48%
15.12%
15.35%
15.12%
14.22%
24.61%
16.48%
28.89%
26.18%
22.57%
25.28%
25.51%
16.70%
27.31%
28.67%
30.92%
32.05%
34.76%
32.05%
34.08%
30.47%
22.79%
36.12%
17.38%
19.41%
20.76%
20.99%
18.28%
29.35%
8.35%
6.54%
6.09%
5.86%
6.77%
6.32%
6.99%
9.25%
Can define the basic concepts of data science
Can define the basic concepts of statistics
Can explain how AI systems are trained
Can define the basic concepts and terminology of AI
Can properly analyze the data obtained by AI in healthcare
Can differentiate between the functions and features of AI-
related tools and applications
Can organize workflows in accordance with logic of AI
Can express the importance of data collection, analysis,
evaluation and safety; for the development of AI in…
Strongly Disagree Disagree Neutral Agree Strongly Agree
39

 Inference from Figure 7:
 24.61% of study participants disagreed with explaining the basic concepts of
data science
 36.12% of the study participants agreed to have a basic conception of statistics
 28.89% of the study participants disagreed with explaining how AI systems are
trained
 26.18% of study participants disagreed to define the basic concepts and
terminology of AI
40

 Inference from Figure 7 (contd..):
 22.57% of study participants disagreed to properly analyse the data obtained by AI
in healthcare
 Around one-fourth of the study participants (25%) disagreed with differentiating
between the functions and features of AI-related tools and applications.
 25.51% of the study participants disagreed with organising workflows by the logic
of AI
 29.35% of the participants agreed to express the importance of data collection,
analysis, evaluation, and safety; for the development of AI in healthcare
41

Figure 8 : Component-Bar Diagram showing the distribution of study participants
according to their readiness on ability factor towards AI implementation (n=443)
11.51%
10.38%
10.38%
10.60%
9.70%
8.80%
10.38%
11.51%
13.31%
17.15%
12.41%
11.96%
18.05%
7.22%
16.70%
15.57%
23.70%
30.47%
26.18%
28.44%
32.27%
26.18%
32.50%
34.76%
31.82%
32.27%
41.08%
37.92%
31.15%
37.69%
31.37%
30.02%
11.96%
9.70%
9.93%
11.06%
8.80%
20.09%
9.02%
8.12%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
I can use Al-based information in combination with my
professional knowledge.
I can use Al technologies effectively and efficiently in healthcare
delivery
I can use artificial intelligence applications in accordance with
its purpose
I can access, evaluate, use, share and create new knowledge
using information and communication technologies
I can explain how Al applications in healthcare offer a solution
to which problem
I find it valuable to use Al for education, service and research
purposes
I can explain the Al applications used in healthcare services to
the patient.
I can choose the proper Al applications for the problem
encountered in healthcare
strongly disagree disagree neutral agree strongly agree
42

 30.02% of the study participants have agreed to choose AI applications for the
problem encountered in healthcare
 31.37% of the study participants have agreed to explain the AI applications
used in healthcare services to the patient
 37.69% of the study participants have agreed that they found it valuable to
use AI for education, service, and research purposes
 31.15% of the participants agreed to explain how AI applications in healthcare
offer a solution to which problem
43

 Inference from Figure 8 (contd.):
 37.92% of the study participants agreed to access, evaluate, use, share and
create new knowledge using information and communication technologies
 41.08% of the study participants agreed to use AI applications in accordance
with its purpose
 32.27% of the study participants agreed to use AI technologies effectively and
efficiently in healthcare delivery
 31.82% of the study participants agreed to use AI-based information in
combination with my professional knowledge
44

Figure 9: Component-Bar Diagram showing the distribution of study participants
according to their readiness on vision factor towards AI implementation (n=443)
45

 41.76% of the study participants agreed to foresee the opportunities
and threats that AI technology can create
 42.21% of the study participants agreed to explain the strengths and
limitations of AI technology
 34.76% of the study participants agreed to explain the limitations of AI
technology
46

Figure 10. Component-Bar Diagram showing the distribution of study participants
according to their readiness on ethical factors towards AI implementation (n=443)
47

Inference from Figure 10:
 40.63% of the study participants agreed to follow the legal regulations
regarding the use of AI technology in healthcare
 41.08% of study participants agreed to act in accordance with ethical
principles while using AI technology
 39.27% of the study participants agreed to use health data in accordance
with legal and ethical norms while using AI technology
48

Mean ± SD Range
Cognitive Factor 19.86 ± 6.84 8-40
Ability Factor 25.70 ± 8.09 8-40
Vision 10.04 ± 3.02 3-15
Ethics 10.13 ± 2.99 3-15
Total Mean 66.13 ± 17.45 22-110
Table 5: Distribution of Mean and Standard deviation (S.D.) scores of each sub-
domain under the ‘readiness’ towards AI domain (n=443)
Inference: Overall mean and S.D. score of readiness towards AI was 66.13 ± 17.45
The ability factor domain was found to have the highest Mean ± SD score (25.70 ± 8.09)
49

Figure 11. Bar of Pie diagram showing distribution of study participants based on
various types of AI application used by them (n*= 443)
Inference: About 66% of the participants used an AI application, of which ChatGPT
(248) was the most commonly used, followed by Google Assistant (215)
(Others- Meta AI, Bixby,
copilot,gemini etc)
No, 34%
Siri, 113
Alexa, 117
ChatGPT, 248
Google Assistant,
215
Others, 55
Yes, 66%
*Multiple responses
50
(Meta AI, August AI, Research Rabbit,
Gemini AI)

Figure 12. Pie of pie diagram showing the distribution of study participants according
to the frequency of usage of AI-based applications (n1=292)
Inference: Most of the participants used an AI-based application weekly (50%) and
among them, 34% used AI-based application once a week.
Daily, 30%
Occasional 20%
34.24%
28.08%
20.54%
17.12%
Weekly, 50%
daily
Others
once weekly
2 days a week
3 days a week
More than 3 days a week
Occasional
51
Daily
Once weekly

Figure 13. Bar diagram showing the distribution of study participants based on
the frequency of usage of AI-based applications (n2=87)
Inference: Among daily users (30%), most of them (64%) used AI-based
applications for less than an hour
52
64.36%
17.24%
10.34% 8.04%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
Less than one hour daily One to two hours daily Two to three hours daily More than three hours
daily

283
187
43
6
0
50
100
150
200
250
300
Studying Entertainment Clinical practice others
No.
of
Study
participants
Purpose of AI based application
Figure 14. Bar diagram showing the distribution of study participants according to
their purpose of AI application usage (n1=292)*
Inference: Most of the study participants used AI-based applications for the purpose
of studying (283) followed by entertainment (187)
(*Multiple responses)
53

Figure 15. Bar of pie diagram showing the distribution of study participants
based on various difficulties faced by them while using AI applications (n1=292)
Inference: 12% of the study participants faced difficulty while using AI
applications, as most of them were unable to interpret (11)
No, 88%
11
9
9
5
Yes,12%
No
Unable to interpret
Lack of
understanding
Limitation of data
Paid version
Faulty data
54

Figure 16 : Bar diagram showing distribution of study participants according to
the different AI based applications that were first used by them (n1=292)*
Inference : Nearly two-fifth (127) of study participants used Google assistant as
the first AI based application
28
108
127
19
10
0
20
40
60
80
100
120
140
Alexa Chat GPT Google Assistant Siri Others
(Others- wix AI,
Meta AI, DALL-E)
(*Multiple responses)
55

Summary
●A descriptive, cross-sectional study was carried out from 10th June- 6th July
2024, among 443 undergraduate medical students of Phase I, II and Part 1 and 2
of Phase III of IPGME&R, Kolkata, selected using a purposive sampling technique
●A predesigned, pretested, and semi-structured questionnaire was used to collect
data through Microsoft forms, containing sociodemographic characteristics and
attitudes towards artificial intelligence scale developed by Sit et al., MAIRS-MS
scale, and utilization of AI in healthcare
●Data were tabulated and analysed using MS Office Excel 2021
57

Summary (contd.)
 Sociodemographic profile:
●57.78% of the study participants belonged to the age group of 18-21 years
●74.26% of the study participants were males and 32.27% of the study
participants belonged to Phase 1 of the academic year
●67.49% of fathers and 53.95% of mothers of the study participants had
completed their graduation
● The most common source of information for the study participants on AI was
social media (378)
58

Summary (contd.)
 Attitude towards AI:
●Around 44% of the study participants have agreed that AI will play an important
role in healthcare, while 37% of the study participants agreed that some
specialties will be replaced by AI in their lifetime
●30% of the study participants agreed to have a basic understanding of the
computational principles of AI
●Almost 38% of the study participants agreed that all medical students should
receive teaching in artificial intelligence
●39% of the study participants agreed that they will be confident in using basic
healthcare AI tools if required at the end of their medical degree
59

Summary (contd.)
 Readiness towards AI:
 Around 45% of the study participants disagreed with explaining how AI systems
are trained while 42.70% of the study participants disagreed with defining the
basic concepts and terminology of AI
 22.57% of the study participants disagreed to properly analyse the data obtained
by AI in healthcare
 About one-fourth (25%) of the study participants disagreed to differentiate
between the functions and features of AI-related tools and applications.
60

Summary (contd.)
 Readiness towards AI (contd.):
 31.37% of the study participants agreed to explain the AI applications used in
healthcare services to the patient while 37.69% of the study participants found
it valuable to use AI for education, service, and research purposes
 32.27% of the study participants agreed to using AI technologies effectively and
efficiently in healthcare delivery and 31.82% of the study participants agreed to
use AI-based information in combination with their professional knowledge
61

Summary (contd.)
 Readiness towards AI (contd.):
 42.21% of the study participants agreed to explain the strengths and limitations
of AI technology while 41.76% of study participants agreed to foresee the
opportunities and threats that AI technology can create
 40.63% of the study participants agreed to follow the legal regulations
regarding the use of AI technology in healthcare
62

Summary (contd.)
Utilization of AI:
 66.0% of the study participants use an AI-based application, out of which, 85.0%
of the study participants use ChatGPT
 Among 96.9% of the study participants, studying is the most common purpose for
using an AI-based application
 Almost half of them (50%) use it every week more specifically once a week
(34.2%)
63

Conclusion
65
 Nearly half of the study participants showed a favorable attitude towards
role of AI in healthcare
 Around three-fifth of the participants could define basic concepts of data
sciences and AI and were ready to choose AI based applications for
healthcare; they were willing to accept AI usage despite feeling a lack of
cognitive skills
 Most of them used AI-based applications for studying (ChatGPT), however,
some of them faced difficulties in using them
65

STRENGTHS, LIMITATIONS
AND
RECOMMENDATIONS
66

Strengths
68
1. There is a scarcity of studies on attitude, readiness, and utilization of AI
among undergraduate medical students of West Bengal
2. Large sample size
3. Study was done on undergraduate medical students in all four phases

Limitation
• The study could have been conducted among undergraduate students in all
medical colleges across West Bengal, but it could not be done due to time
constraints
69

Recommendations
67
 Incorporation of AI-related foundation courses, lectures, workshops,
seminars, etc. into the medical curriculum
 Creation of opportunity by the Government for the use of AI in healthcare
and the teaching-learning process
 Development of ethical guidelines for AI utilization in healthcare, and
recommend continuous monitoring and evaluation of AI systems
once implemented

Acknowledgement
70
We would like to thank our respected Director, Prof (Dr.) Manimoy Bandopadhyay.
We would like to thank Prof. (Dr.) Avijit Hazra, Dean of Students Affairs, IPGME&R.
We would like to thank Prof. (Dr.) Mausumi Basu, Head, Dept. of Community Medicine, IPGME&R.
We extend our gratitude to Dr. Kuntala Ray, Associate Professor, IPGME&R, for guiding us throughout the
project.
We would like to thank Dr. Shalini Pattanayak, Dr. Kalpana Gupta, and Dr. Subhosri Saha, Post Graduate
Trainees, Dept. of Community Medicine, IPGME&R, for constantly guiding us throughout the project.
We would also like to thank the entire Community Medicine department of IPGME&R, for giving us an
amazing opportunity to work on this project.
Finally, we would also like to thank all the participants for providing the data required for the project.

Attitude and Readiness towards Artificial Intelligence and its Utilisation: A Cross-sectional Study among Undergraduate Medical Students in a Medical College, Kolkata

Attitude and Readiness towards Artificial Intelligence and its Utilisation: A Cross-sectional Study among Undergraduate Medical Students in a Medical College, Kolkata

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