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ISSN: 03875547
Volume 47, Issue 05, May, 2024
8413
Evaluating the Public's Awareness and Acceptance of
AI Technologies in Personalized Pharmacotherapy
Anas Alhur1*, Wajan Alqathanin2, Rahaf Aljafel2, Layan Al Mudawi2, Elaf Alshehri2, Afnan Asiri2, Rahaf
Alamri3, Haifa Alaowad2, Zaina Abuhomoud4, Batoul Alqahtani5, Reem Alshahrani2, Raghad M. Alrashed2,
Tagreed AlShahrani6, Rasha M. Alqahtani2, Fouz Odhayb7
College of Public Health and Health Informatics, Dept. of Health Informatics, University of Hail, Hail,
Saudi Arabia1
Department of Pharmacy, King Khalid University, Abha, Saudi Arabia2
Deem Pharmacy3
Princess Nourah Bint Abdulrahman University4
Deryaq Pharmacy, Abha5
Armed Forces Hospital Southern Region6
Al-Hayat National Hospital7
Corresponding Author: 1*
Keywords:
ABSTRACT
Artificial Intelligence,
Personalized Pharmacotherapy,
Public Awareness
This cross-sectional study evaluates public awareness and acceptance of
Artificial Intelligence (AI) technologies in personalized pharmacotherapy
across a diverse demographic. Utilizing a stratified random sampling
method, we surveyed 3,216 participants to quantitatively assess their
awareness, acceptance, perceived benefits, and concerns about AI in
pharmacotherapy. The results highlight a moderate level of awareness of
AI applications in healthcare, with 43.84% of respondents acknowledging
familiarity. Acceptance of AI-driven treatment plans is cautiously
optimistic, evidenced by 46.70% of participants expressing some level of
trust in AI for diagnostics. However, concerns regarding privacy, data
security, and the potential for AI errors are prevalent, with over 57% of
respondents citing these as significant deterrents. Education level emerged
as a critical determinant of AI acceptance, with higher educational
attainment correlating with more favorable perceptions. This study
underscores the necessity for healthcare professionals, policymakers, and
technologists to collaboratively enhance public education and address
privacy and security concerns to facilitate the ethical integration of AI into
healthcare.
This work is licensed under a Creative Commons Attribution Non-Commercial 4.0
International License.
1. INTRODUCTION
The integration of Artificial Intelligence (AI) in healthcare, particularly in pharmacotherapy, represents a
paradigm shift towards more personalized and efficient medical treatments. The advent of AI technologies,
including machine learning and data analytics, has the potential to revolutionize the field of pharmacology
Alhur, et.al, 2024 Teikyo Medical Journal
8414
by enabling the development of personalized medicine, optimizing drug discovery, and enhancing patient
care.
2. Literature Review
Recent advancements in AI have shown significant promise in drug discovery, formulation, and testing. [1]
highlighted AI's capability to analyze extensive biological data, thereby identifying disease-associated targets
and predicting drug interactions, which is crucial for personalized medicine. Similarly, [2] discussed AI's
potential in optimizing therapy for personalized medicine by predicting drug release profiles and
incorporating patient-specific factors. These studies demonstrate AI's transformative role in developing
targeted therapies and optimizing drug development, aligning with the objectives of personalized
pharmacotherapy.
The integration of AI in healthcare raises questions about public awareness and acceptance. [5] emphasized
the digital transformation in medicine driven by genomics, imaging, and new data sources, necessitating AI
support for personalized treatments. However, they also pointed out the need to address issues such as
explainability, liability, and privacy to mainstream AI in healthcare. [6] explored the adaptation of AI in
clinical pharmaceutical services, highlighting the importance of AI literacy among clinical pharmacists to
navigate the AI era effectively. These insights are crucial for understanding public perceptions and the factors
influencing the acceptance of AI in healthcare.
While AI offers numerous benefits in healthcare, it also raises concerns regarding privacy, accuracy, and the
lack of human oversight. [3] stressed the importance of robust evaluation to ensure AI tools enhance clinical
practice safely and equitably. [4] discussed the potential and pitfalls of AI in clinical pharmacology,
highlighting the need for an ethical framework to support AI integration. Addressing these concerns is
essential for fostering public trust and acceptance of AI technologies in healthcare.
Demographic and psychographic characteristics significantly impact public acceptance of AI in healthcare.
Understanding these factors is crucial for developing strategies to increase public trust in AI-driven solutions.
Moreover, providing additional information about the benefits and workings of AI in personalized
pharmacotherapy could alter perceptions and increase acceptance. The role of information and education in
shaping public attitudes towards AI in healthcare cannot be overstated. The primary aim of this study is to
systematically evaluate and understand the public's awareness, acceptance, and perceptions of Artificial
Intelligence (AI) technologies in personalized pharmacotherapy.
3. Methodology
This study's methodology was designed to systematically assess public awareness and acceptance of AI
technologies in personalized pharmacotherapy. It utilized a quantitative research approach within a cross-
sectional survey framework, aiming to capture and analyze prevailing attitudes and perceptions toward the
application of AI in healthcare settings.
3.1 Sample Selection
The study employed a stratified random sampling technique to ensure a representative sample of the
population. The stratification criteria included age, gender, education level, and previous experience with
healthcare technology. This approach aimed to capture a wide range of perspectives on AI in personalized
pharmacotherapy from a diverse population sample.
3.2 Survey Design
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The survey was structured to include only closed-ended questions, facilitating quantitative analysis. These
questions were designed to assess quantitatively:
Level of acceptance of AI-driven personalized pharmacotherapy.
Specific concerns related to privacy, data security, and the reliability of AI recommendations.
Each question offered a set of predefined response options, such as Likert scale ratings, yes/no options, or
multiple-choice answers, to quantify participants' attitudes and perceptions.
3.3 Data Collection
Data collection was conducted through an online survey platform, enabling efficient and wide-reaching
participation. The survey was disseminated via various online channels, including social media, healthcare
forums, and patient advocacy groups, to ensure a broad and diverse respondent base.
3.4 Data Analysis
Statistics analysis was employed to succinctly summarize the collected data, providing an overview of the
participants' responses. This included calculating means, medians, and modes for continuous variables and
frequencies and percentages for categorical variables to clearly show the central tendencies and variability
within the data set. This statistical analysis helped identify significant patterns and correlations between
demographic factors and participants' levels of awareness, acceptance, and concerns regarding AI in
personalized pharmacotherapy.
3.5 Ethical Considerations
This study followed strict ethical guidelines, focusing on protecting participant confidentiality and data
security. Approval was granted by the Research Ethics Committee at the University of Hail (Approval No.
H-2024-204, dated 25/3/2024). We obtained informed consent from all participants, ensuring they understood
the study's purpose, the anonymity of their responses, and their right to withdraw at any time.
The study, titled "Evaluating the Public's Awareness and Acceptance of AI Technologies in Personalized
Pharmacotherapy," was thoroughly reviewed and approved, reflecting our commitment to ethical standards
4. Result
The demographic profile of survey participants is detailed as follows: The gender distribution showed that
females constituted 69.84% (n=2,246) of the respondents, with males making up 30.16% (n=970). This
indicates a higher participation rate among females as seen in (Table 1). In terms of age distribution, the
younger age groups were more prominently represented. The 18-24 age group included 26.00% (n=836) of
the participants, closely followed by the 25-34 age group at 25.44% (n=818). The 35-44 and 45-54 age groups
comprised 19.84% (n=638) and 19.59% (n=630) of the respondents, respectively. The 55-64 age group
accounted for 8.08% (n=260) of the participants, while those aged 65 and above represented 1.00% (n=32).
Regarding education levels, the majority of respondents, 72.64% (n=2,336), reported having a
college/bachelor’s degree. Those with a High School Graduate diploma constituted 13.68% (n=440),
followed by individuals with a Graduate Degree at 9.70% (n=312). A smaller portion of the participants,
3.98% (n=128), indicated having an education level of Less than High School. This demographic overview
highlights a diverse participant group, predominantly younger and female, with a significant proportion
holding a college or bachelor's Degree.
Table 1: Demographic Characteristics of Study Participants
Alhur, et.al, 2024 Teikyo Medical Journal
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Demographic
Category
Description
Frequency
(N)
Percentage
(%)
Gender
Female
2246
69.84
Male
970
30.16
Age Group
18-24
836
26
25-34
818
25.44
35-44
638
19.84
45-54
630
19.59
55-64
260
8.08
65+
32
1
Education
Level
College/bachelor’s degree
2336
72.64
High School Graduate
440
13.68
Graduate Degree
312
9.7
Less than High School
128
3.98
The survey explored various aspects related to the awareness and understanding of AI in personalized
pharmacotherapy among participants. The results revealed that 43.84% (n=1,409) of respondents were aware
of AI applications in medicine, while 39.05% (n=1,256) were not. The mean response value for awareness,
calculated based on a binary encoding scheme, was 1.47 with a standard deviation of 0.50, indicating a slight
leaning toward awareness among participants as indicated in (Table 2).
In assessing the understanding of AI technologies in healthcare, responses varied across the scale from 'Very
Good' to 'Very Poor'. A notable 23.69% (n=761) rated their understanding as 'Very Good', and 24.56%
(n=791) as 'Good'. Moderate understanding was reported by 21.52% (n=692), while 21.95% (n=705) found
their understanding to be 'Poor', and 8.21% (n=264) 'Very Poor'. The overall mean response for understanding
was 3.34, with a standard deviation of 1.28, suggesting that, on average, respondents felt they had a moderate
to good understanding of AI technologies in healthcare.
When asked about the usage of AI in healthcare services or products, 15.80% (n=508) of the participants
acknowledged having used such services, contrasting with 68.97% (n=2,218) who had not, and 15.24%
(n=490) who were unsure. The mean response for this category, calculated with a ternary encoding scheme,
was 1.99 with a standard deviation of 0.56, reflecting a predominant lack of personal experience with AI
healthcare services among the survey respondents.
Table 2: Awareness and Understanding of AI in Personalized Pharmacotherapy
Variable
Response
Options
Frequency
(N)
Frequency
(%)
Mean
Response
Standard
Deviation
Awareness of AI in Medicine
Yes
1,409
43.84%
1.47
0.5
No
1,256
39.05%
Understanding of AI in
Healthcare
Very Good
761
23.69%
3.34
1.28
Good
791
24.56%
Moderate
692
21.52%
Poor
705
21.95%
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8417
Very Poor
264
8.21%
Used AI Healthcare Service
Yes
508
15.80%
1.99
0.56
No
2,218
68.97%
Not Sure
490
15.24%
The survey also delved into participants' acceptance of AI in pharmacotherapy, focusing on trust in AI for
diagnostics, comfort with AI-driven medication recommendations, and willingness to share health data with
AI for personalized treatment plans. In the realm of diagnostics, a significant portion of respondents, 46.70%
(n=1,501), expressed 'Somewhat Trust' in AI for diagnosing health conditions, while 12.90% (n=415) showed
complete trust. This suggests a cautious optimism towards AI's diagnostic capabilities among the surveyed
individuals.
Regarding medication recommendations made by AI, 13.90% (n=447) of participants felt 'Very Comfortable'
with AI's suggestions, and a more substantial 34.00% (n=1,093) were 'Comfortable'. This indicates a general
acceptance and readiness to consider AI-driven recommendations in medication plans, highlighting the
potential for AI to support personalized pharmacotherapy. When asked about their willingness to share
personal health data with AI to enhance treatment personalization, 33.30% (n=1,070) of the respondents were
'Definitely Yes', and 37.20% (n=1,196) were 'Probably Yes'. This demonstrates a considerable openness
among participants to engage with AI technologies for improved healthcare outcomes, contingent on the
assurance of data privacy and security for more information (Table 3).
Table 3: Acceptance of AI in Pharmacotherapy
Variable
Response
Options
Frequency
(N)
Frequency
(%)
Trust AI for Diagnosing Health Conditions
Completely
Trust
415
12.90%
Somewhat Trust
1,501
46.70%
Comfort with AI Recommendations on Medication
Very
Comfortable
447
13.90%
Comfortable
1,093
34.00%
Willingness to Share Health Data with AI
Definitely Yes
1,070
33.30%
Probably Yes
1,196
37.20%
The survey further explored participants' perceptions and concerns about the integration of AI in
pharmacotherapy, revealing insightful viewpoints on several critical issues. Privacy and data security
emerged as significant concerns, with approximately 36.90% (n=1,187) of respondents highlighting this as a
key issue. This underscores the paramount importance of robust data protection measures in the deployment
of AI technologies in healthcare. About 41.60% (n=1,405) of the participants also expressed concern about
the accuracy of AI recommendations. This highlights the need for continuous improvement and validation of
AI algorithms to ensure their reliability and effectiveness in clinical settings.
Loss of human oversight was a concern for 50.00% (n=1,670) of respondents, indicating a strong desire for
maintaining a human element in healthcare decision-making, even as AI technologies become more prevalent.
Potential for AI errors was the most cited concern, with 57.00% (n=1,833) of participants wary of the
implications of incorrect AI-driven decisions. This reflects the critical need for comprehensive testing,
transparency, and mechanisms for oversight in AI applications within healthcare. A smaller proportion of
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respondents, 5.60% (n=190), expressed other unspecified concerns, suggesting that there may be additional,
less common issues that need to be addressed as AI technologies continue to evolve and integrate into
pharmacotherapy practices. The mean concern score of 38.22, along with a standard deviation of 8.27,
quantitatively encapsulates the overall level of concern among participants. These figures indicate a moderate
level of concern across the surveyed population, with a notable spread in the degree of concern among
individuals as demonstrated in (Table 4).
Table 4: Perceptions and Concerns about AI in Pharmacotherapy
Concerns
Frequency (N)
Frequency (%)
Privacy and Data Security
1,187
36.90%
Accuracy of AI Recommendations
1,405
41.60%
Loss of Human Oversight
1,670
50.00%
Potential for AI Errors
1,833
57.00%
Other
190
5.60%
Mean Concern Score
-
38.22
Standard Deviation of Concern Score
-
8.27
The survey's exploration into the impact of additional information on the acceptance of AI in
pharmacotherapy yielded notable insights. A substantial portion of participants, 56.90% (n=1,828), indicated
that additional information would lead to a somewhat increased acceptance of AI ("Yes, somewhat"),
highlighting the value of transparent and accessible information in fostering a positive perception of AI
technologies. A significant number, 22.50% (n=723), felt that more information would significantly enhance
their trust in AI ("Yes, significantly"), demonstrating the potential of detailed, quality information to shift
perceptions and acceptance substantially.
Conversely, 9.40% (n=302) of the respondents believed that additional information would not alter their
stance ("No Change"), suggesting a segment of the population with fixed perceptions of AI in healthcare,
regardless of new information. A smaller group, 5.10% (n=164), expressed that further information might
decrease their trust in AI ("No, Would Trust Less"), pointing to concerns that might be exacerbated by more
detailed knowledge of AI applications or limitations. The mean response value of 3.03 and a standard
deviation of 0.75 indicate an overall tendency towards a positive impact of additional information on AI
acceptance. The mean suggests that, on average, participants lean towards the belief that more information
would somewhat increase their acceptance of AI. The standard deviation reflects a moderate range of opinions
among respondents, highlighting diverse perspectives on how additional information influences AI
acceptance for more information (Table 5).
Table 5: Impact of Information on Acceptance of AI in Pharmacotherapy
Impact of
Additional
Information
Frequency
(N)
Frequency
(%)
Assigned
Value
Yes, significantly
723
22.50%
4
Yes, somewhat
1828
56.90%
3
No change
302
9.40%
2
No, would trust
Less
164
5.10%
1
Mean response
-
-
3.03
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Standard Deviation
-
-
0.75
The provided bar chart (Figure 1), under the title "Perceptions and Concerns," shows the distribution of
responses across different apprehensions relating to AI. It reveals that general concerns categorized as 'Other'
garnered the least attention, with only 176 responses. Privacy and data security issues prompted a significantly
higher response, with 1,192 individuals acknowledging it as a concern. The accuracy of AI recommendations
was noted by 1,298 respondents, indicating a moderate level of concern. A more substantial concern is
reflected in the 1,576 responses regarding the loss of human oversight in healthcare decisions. Topping the
chart, the potential for AI to make errors emerges as the predominant worry, with the highest number of
responses at 1,808. This hierarchy of concerns showcases individuals' varying levels of unease towards
different aspects of AI integration into society.
Figure 1: Comfort with the Use of AI in Healthcare Based on Information Type
The second bar chart (Figure 2), titled "Information Type," presents the count of responses to various types
of information people find valuable. Detailed explanations lead to the highest number of responses at 2,040,
suggesting a strong preference for thorough information. Success stories and case studies followed closely,
which received 1,620 responses, indicating a significant interest in real-world applications and outcomes.
Endorsements from trusted sources also hold considerable sway, evidenced by 1,602 responses. While less
than the previous categories, information on privacy and security still shows a notable concern with 1,162
responses. Lastly, the 'Other' category, encompassing various unspecified types of information, has the fewest
responses at 254, reflecting its relatively lesser direct importance to the respondents.
Alhur, et.al, 2024 Teikyo Medical Journal
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Figure 2: Comfort with the Use of AI in Healthcare Based on Information Type
Our analysis extends to examining the statistical associations between various demographic factors and key
variables related to AI perceptions in pharmacotherapy, as presented in Table 6. The findings from Chi-
Square tests reveal significant relationships, emphasizing the influence of demographic nuances on public
perceptions and acceptance of AI in healthcare.
Table 6: Associations Between Demographic Factors and Key Variables
Demographic
Factor
Key Variable
Chi-
Square
Statistic
p-value
Age Group
Awareness of AI in Medicine
30.09
0.037
Understanding of AI in
Healthcare
52.2
0.007
Gender
Understanding of AI in
Healthcare
22.23
<0.001
Education
Level
Awareness of AI in Medicine
26.48
0.002
In assessing the predictive power and accuracy of logistic regression and Random Forest models in
forecasting public acceptance of AI-driven pharmacotherapy, (Table 7) encapsulates the performance metrics
of these models. The enhanced performance of the Random Forest model, in particular, highlights the efficacy
of advanced machine learning techniques in navigating the complex landscape of public perceptions and
acceptance of AI in healthcare.
Table 7: Performance Metrics of Predictive Models
Model
Metric
Value
Logistic
Regression
Accuracy
49.28%
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Precision
(Overall)
45.77%
Recall (Overall)
49.28%
F1-Score
(Overall)
41.19%
RandomForest
Accuracy
64.18%
Precision
(Overall)
63.99%
Recall (Overall)
64.18%
F1-Score
(Overall)
63.82%
5. Discussion
AI integration in healthcare, particularly in personalized pharmacotherapy, has been met with a mix of
enthusiasm and scepticism by the public. This study's findings contribute to a growing body of literature that
seeks to understand public perceptions, acceptance, and concerns regarding AI in healthcare.
[7] conducted a study exploring lay individuals' perceptions of AI-empowered healthcare systems, finding a
generally positive attitude towards such systems despite low awareness and experience among participants.
This aligns with our findings, where a significant portion of respondents expressed comfort and willingness
to use health AI systems. The study by Zhang et al. also highlighted the importance of intrinsic factors such
as education background and technology literacy in shaping perceptions, suggesting that efforts to enhance
public understanding of AI in healthcare could improve acceptance rates.
Researchers in 2023 explored American public opinion on AI in healthcare, revealing a strong preference for
human medical professionals over AI for medical decision-making, despite acknowledging the potential for
AI to reduce cultural biases in decisions [8], [9]. This dichotomy reflects the complex relationship the public
has with AI in healthcare, where the trust in AI's capabilities is tempered by a desire for human oversight and
empathy. Our study's findings on concerns regarding privacy, data security, and the accuracy of AI
recommendations further demonstrate the need for transparent and ethical AI deployment to build public
trust.
[10- 13] have undertaken a suite of studies within the Kingdom of Saudi Arabia (KSA) aimed at evaluating
the public's knowledge and receptiveness towards Artificial Intelligence (AI). These investigations uncovered
that a noteworthy segment of participants acknowledged AI's advantages while exhibiting a fair level of
comprehension regarding the subject matter. In a separate vein, [15] harnessed data from social media
platforms to probe into societal attitudes towards AI in the medical sphere. Their research unearthed a
predominantly affirmative stance towards AI, with a considerable number of individuals endorsing the notion
that AI holds the capacity to augment or even entirely substitute human physicians in certain contexts [14].
This inquiry, rooted in social media discourse, affords a unique vantage point on the collective sentiment,
often portraying a more optimistic perspective on medical AI among the general populace as opposed to
medical practitioners. This discrepancy demonstrates the necessity for sustained dialogue and educational
efforts to mitigate extant apprehensions and rectify misconceptions about AI's role in healthcare, thereby
fostering a more informed public consensus.
In 2020, a study investigated the acceptance of AI in medicine among Japanese doctors and the public, finding
that doctors were more optimistic about AI-driven medicine than the general public [15]. This study highlights
Alhur, et.al, 2024 Teikyo Medical Journal
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the variability in acceptance levels across different demographic groups and demonstrates the importance of
considering diverse perspectives in the implementation of AI in healthcare. Multiple recent studies in KSA
indicated that the Saudi Arbai population perceives the benefits and is willing to use different types of
technologies for multiple aspects of their lives [16- 23].
6. Conclusion
This comprehensive investigation of public attitudes towards AI in personalized pharmacotherapy has
demonstrated the multifaceted nature of societal engagement with emerging healthcare technologies. It
highlights the critical need to address the awareness-comprehension gap, manage privacy and security
concerns, and enhance public trust through transparent, informative communication. As healthcare continues
to advance with the integration of AI, healthcare providers, policymakers, and technology developers must
collaborate to foster an informed, receptive environment. Future research should aim to track the shifting
dynamics of public perceptions and investigate the impact of targeted educational interventions on enhancing
the acceptance and ethical integration of AI in healthcare.
7. References
[1] L. K. Vora et al., "Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design,"
Pharmaceutics, vol. 15, no. 7, 2023, Accessed: May 2, 2024. [Online]. Available:
https://doi.org/10.3390/pharmaceutics15071916
[2] K. S. Vidhya et al., "Artificial Intelligence's Impact on Drug Discovery and Development From Bench to
Bedside," Cureus, 2023, Accessed: May 2, 2024. [Online]. Available: https://doi.org/10.7759/cureus.47486
[3] D. K. Ryan et al., "AI and Machine Learning for Clinical Pharmacology," 2023, Accessed: May 2, 2024.
[Online]. Available: https://doi.org/10.1111/bcp.15930
[4] M. Johnson et al., "The Potential and Pitfalls of Artificial Intelligence in Clinical Pharmacology," 2023,
Accessed: May 2, 2024. [Online]. Available: https://doi.org/10.1002/psp4.12902
[5] K. Paranjape et al., "Mainstreaming Personalized Healthcare–Transforming Healthcare Through New Era
of Artificial Intelligence," 2020, Accessed: May 2, 2024. [Online]. Available:
https://doi.org/10.1109/JBHI.2020.2970807
[6] H. Sato, "Development of Clinical Pharmaceutical Services via Artificial Intelligence Adaptation," 2022,
Accessed: May 2, 2024. [Online]. Available: https://doi.org/10.1248/yakushi.21-00178-4
[7] Z. Zhang et al., "Lay individuals' perceptions of artificial intelligence (AI)‐empowered healthcare
systems," 2020, Accessed: May 2, 2024. [Online]. Available: https://doi.org/10.1002/pra2.326
[8] J. Rojahn et al., "American public opinion on artificial intelligence in healthcare," 2023, Accessed: May
2, 2024. [Online]. Available: https://doi.org/10.1371/journal.pone.0294028
[9] S. Das, R. Dey, and A. K. Nayak, "Artificial intelligence in pharmacy," Indian journal of pharmaceutical
education and research, vol. 55, no. 2, pp. 304-318, 2021.
[10] A. Alhur and A. A. Alhur, "The acceptance of digital health: what about telepsychology and
telepsychiatry?," Jurnal Sistem Informasi, vol. 18, no. 2, pp. 18-35, 2022.
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[11] A. A. Alhur et al., "Telemental health and artificial intelligence: knowledge and attitudes of Saudi
Arabian individuals towards ai-integrated telemental health," Journal of Population Therapeutics and Clinical
Pharmacology, vol. 30, no. 17, pp. 1993-2009, 2023.
[12] A. A. Alhur, "Public health informatics: the importance of Covid-19 dashboard in KSA for sharing and
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43-59, 2023.
[13] A. Alhur et al., "Evaluating Computer Science Students' Experiences and Motivation Towards Learning
Artificial Intelligence," British Journal of Teacher Education and Pedagogy, vol. 2, no. 3, pp. 49-56, 2023.
[14] H. Tamori et al., "Acceptance of the Use of Artificial Intelligence in Medicine Among Japan’s Doctors
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[15] S. Gao et al., "Public Perception of Artificial Intelligence in Medical Care: Content Analysis of Social
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[16] A. Alhur and A. A. Alhur, "The acceptance of digital health: what about telepsychology and
telepsychiatry?," Jurnal Sistem Informasi, vol. 18, no. 2, pp. 18-35, 2022.
[17] A. Alhur, "An Investigation of Nurses' Perceptions of the Usefulness and Easiness of Using Electronic
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Questionnaire
Research Title: Evaluating the Public's Awareness and Acceptance of AI Technologies in Personalized
Pharmacotherapy: A Cross-Sectional Population Survey
Alhur, et.al, 2024 Teikyo Medical Journal
8424
Purpose of the Questionnaire
This questionnaire aims to assess public awareness, acceptance, and perceptions of Artificial Intelligence (AI)
in pharmacotherapy. The questionnaire seeks to understand how different demographics perceive AI's role in
healthcare, particularly in medication management and personalized medicine. It will explore trust levels,
comfort with AI-assisted services, and concerns about privacy, data security, and the reliability of AI
decisions. Insights gained will help healthcare professionals and AI developers address barriers and enhance
the integration of AI in healthcare.
Participation Voluntariness:
Participation in this study is entirely voluntary. You have the right to withdraw from the research at any point
without any penalty or loss of benefits to which you are otherwise entitled. Your decision to participate will
not affect your current or future relations with affiliated institutions or organizations. All responses will be
kept confidential and will only be used for the purposes of this research.
The expected time is 1 min – 2 minutes.
Section 1: Demographics
This section gathers basic demographic information to analyze responses based on different population
segments.
1. Age Group:
• 18-24
• 25-34
• 35-44
• 45-54
• 55-64
• 65+
2. Gender:
• Male
• Female
3. Education Level:
• Some High School
• High School Graduate
• Some College
• Bachelor's Degree
• Graduate Degree
4. Employment Status:
• Employed (full-time)
• Employed (part-time)
• Unemployed
• Student
• Retired
• Other
Section 2: Awareness of AI in Pharmacotherapy
ISSN: 03875547
Volume 47, Issue 05, May, 2024
8425
This section assesses the respondent's awareness of AI applications in healthcare, specifically in
pharmacotherapy.
5. Before this survey, were you aware of the use of AI technologies in personalized
medicine?
• Yes
• No
• Not sure
6. How would you rate your understanding of AI technologies in healthcare?
• Very good• Good
• Moderate
• Poor
• Very poor
7. Have you ever used any healthcare service or product that utilizes AI?
• Yes
• No
• Not sure
Section 3: Acceptance of AI in Pharmacotherapy
This section gauges the willingness of the public to accept AI-driven personalized pharmacotherapy.
8. Would you trust AI to assist in diagnosing health conditions?
• Completely trust
• Somewhat trust
• Neutral
• Somewhat distrust
• Completely distrust
9. How comfortable are you with AI technologies making recommendations about your
medication?
• Very comfortable
• Comfortable
• Neutral
• Uncomfortable
• Very uncomfortable
10. Would you be willing to share your personal health data with AI systems to receive
personalized medication recommendations?
• Definitely yes
• Probably yes
• Unsure
• Probably no
• Definitely no
Section 4: Perceptions and Concerns
Alhur, et.al, 2024 Teikyo Medical Journal
8426
This section identifies common perceptions and concerns regarding AI in pharmacotherapy.
11. What is your biggest concern about using AI in healthcare? (Select all that apply)
• Privacy and data security
• Accuracy of AI recommendations
• Loss of human oversight in healthcare decisions
• Potential for AI to make errors
• Other
12. Do you believe AI can improve the efficiency and effectiveness of healthcare services?
• Strongly agree
• Agree
• Neutral
• Disagree
• Strongly disagree
Section 5: Impact of Information on Acceptance
This section explores how additional information might influence public acceptance of AI in
pharmacotherapy.
13. Would receiving more information about how AI is used in healthcare increase your trust
in it?
• Yes, significantly
• Yes, somewhat
• No change
• No, would trust less
• Not sure
14. What type of information would make you more comfortable with the use of AI in
healthcare? (Select all that apply)
• Detailed explanations of how AI works
• Success stories/case studies
• Information on privacy and data protection measures
• Endorsements from trusted healthcare professionals
• Other
