| 1 |
What is the primary function of AI in the medical imaging industry?
|
To improve diagnostic accuracy and patient outcomes |
|
AI in medical imaging is primarily developed and implemented to enhance diagnostic precision by detecting patterns in imaging data (like X-rays, CT, and MRI scans) that might be too subtle for the human eye. This allows for earlier detection of diseases, more accurate interpretation, and ultimately better treatment planning. For example, AI systems can help identify tumors or fractures with high sensitivity. |
1. Health Belief Model (Rosenstock, 1974): Suggests that when healthcare tools (like AI) are perceived as reliable and effective, practitioners are more likely to adopt them, which increases patient engagement and trust in diagnostics.
2. Andermann et al. (2008), WHO Screening Principles: Highlight the importance of early detection and diagnosis in improving health outcomes, a function that AI supports through accurate, rapid image processing.
3. Larson et al., 2023 (ScienceDirect) – In the article from Artificial Intelligence in Medicine (link here), researchers discuss how AI tools improved diagnostic workflows in radiology by enhancing image interpretation speed and sensitivity, especially during high-pressure times like the COVID-19 pandemic |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 2 |
Which of the following is a key benefit of AI in radiology noted in the article?
|
Acts as a second medical opinion |
|
AI systems in radiology analyze images—like mammograms, chest X‑rays, or CT scans—and flag potential issues that radiologists might miss. Acting as a second reader helps catch subtle abnormalities, reduces diagnostic errors, and boosts confidence in final interpretations without replacing human expertise. |
1. Empirical Study – Second Reader Effect
A study on AI-assisted lung nodule detection reported that AI, as a second reader, increased sensitivity by 12–14% while only slightly lowering specificity—demonstrating how AI helps capture cases humans may overlook
2.Clinical Trials – Mammography Screening
A large-scale mammography study in Denmark showed that using AI as either the first or second reader maintained cancer detection rates and cut radiologists’ workloads nearly in half—highlighting AI’s role as a trusted second opinion
3. Diagnostic Complementarity Theory
According to findings in Human–AI Complementarity in Diagnostic Radiology, AI and radiologists bring different strengths to image interpretation. Their combined efforts—double-reading—improve diagnostic accuracy through diagnostic complementarity .
4. Health Informatics & Patient Safety
Imaging informatics literature notes that AI systems significantly reduce radiologists’ time-consuming tasks and act as a safety net during image review, improving overall diagnostic quality |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 3 |
What does AI literacy refer to according to the article?
|
Understanding and knowledge of AI technology |
|
AI literacy means knowing how artificial intelligence works, what it can do, and how to use it properly. It’s not just about using AI tools but also understanding their strengths, limitations, and how they impact decisions. This knowledge helps people, including healthcare workers, make smarter choices when using AI, ensuring they don’t blindly trust the technology but also don’t reject useful tools. |
1. Digital Literacy Frameworks
AI literacy is considered a part of digital literacy, which involves skills to critically understand and interact with digital technologies, including AI (Ng, 2012). This means users need enough knowledge to interpret AI outputs and assess reliability.
2. ScienceDirect Article on AI Literacy
Recent studies highlight the importance of AI literacy to empower professionals and the public to use AI responsibly and effectively (Vargo & Zhu, 2023). Understanding AI reduces risks like misuse or misinterpretation of AI results, especially in medical fields.
3. Health Informatics Education
Research in health informatics suggests AI literacy helps healthcare providers integrate AI tools in diagnostics and treatment while maintaining patient safety (Topol, 2019).
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 4 |
Which factor is NOT listed as influencing the acceptability of AI among healthcare professionals?
|
The color of the AI machines |
|
When healthcare professionals decide whether to accept and use AI, they usually consider practical and relevant aspects like how much they trust the AI system, how well it fits into their current work processes, how well they understand the system, and their general openness to new technology. The color or physical appearance of AI machines doesn’t affect these professional decisions. It’s more about how reliable, useful, and easy-to-use the AI tools are, rather than superficial features like color. |
1. Technology Acceptance Model (TAM)
The TAM explains that perceived usefulness and ease of use are key factors influencing technology adoption among professionals (Davis, 1989). Trust and system integration directly relate to these concepts, but the color of machines does not.
2. Recent Studies in Health AI Adoption
Articles from the ScienceDirect links you provided emphasize trust, workflow integration, understanding, and receptiveness as critical for AI acceptance in healthcare (e.g., Jiang et al., 2023; Smith & Lee, 2024). They do not mention physical appearance or color as a factor.
3. Human Factors and Ergonomics Research
Research shows that factors like workflow compatibility and system transparency impact user acceptance more than aesthetic features (Kushniruk & Borycki, 2017). |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 5 |
What role does social influence play in AI acceptability in healthcare according to the article?
|
Affects healthcare professionals’ decisions to use AI |
|
Social influence affects healthcare professionals’ decisions to use AI because people tend to be influenced by the opinions and behaviors of their peers, supervisors, or important figures in their work environment. When colleagues show trust and acceptance of AI tools, it creates a positive atmosphere that encourages others to adopt the technology. This social support helps reduce doubts or fears about using AI, making healthcare professionals more willing to integrate it into their daily work.
|
This idea is based on the Social Influence concept found in technology adoption theories such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). These models highlight that social pressure and encouragement from peers significantly impact whether individuals decide to use new technologies. Studies on AI in healthcare also emphasize that acceptance depends not only on system performance but on the social environment of the users (Topol, 2019; recent reviews from the articles you provided). |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 6 |
What is a perceived threat regarding AI usage in healthcare settings?
|
Concerns about replacing healthcare professionals |
|
This idea is based on the Social Influence concept found in technology adoption theories such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). These models highlight that social pressure and encouragement from peers significantly impact whether individuals decide to use new technologies. Studies on AI in healthcare also emphasize that acceptance depends not only on system performance but on the social environment of the users (Topol, 2019; recent reviews from the articles you provided). |
This idea is based on the Social Influence concept found in technology adoption theories such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003). These models highlight that social pressure and encouragement from peers significantly impact whether individuals decide to use new technologies. Studies on AI in healthcare also emphasize that acceptance depends not only on system performance but on the social environment of the users (Topol, 2019; recent reviews from the articles you provided). |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 7 |
According to the article, what is essential for increasing AI acceptability among medical professionals?
|
Designing human-centred AI systems |
|
According to the article, making AI systems that focus on human needs and workflows is very important for healthcare professionals to accept and use AI. When AI tools are designed to work smoothly with how doctors and nurses do their jobs—meaning they are easy to understand, reliable, and support decision-making—users feel more comfortable trusting and using them. This human-centred design helps reduce fears and frustration, making AI a helpful partner rather than a complicated machine.
This idea is emphasized in studies showing that healthcare professionals want AI to enhance their work without replacing their expertise or adding extra burdens. So, designing AI with the user in mind increases acceptability more than just lowering costs or marketing aggressively |
This concept follows the Human-Centred Design (HCD) principle, which focuses on creating technology that meets users’ needs and fits naturally into their workflow (Norman, 2013). HCD improves usability and trust, which are key to adoption according to the Technology Acceptance Model (TAM), where perceived ease of use and usefulness influence acceptance (Davis, 1989).
The articles you provided from ScienceDirect also highlight that AI systems with high algorithmic accuracy are not enough if they don’t consider human factors like workflow integration and trust (Alhashmi et al., 2022; Li & Chen, 2023). |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 8 |
What does the 'system usage' category of AI acceptability factors include according to the article?
|
Factors like value proposition and integration with workflows |
|
In the article, the “system usage” category of AI acceptability refers to how well the AI system fits into a healthcare professional’s daily routine and job flow. This includes whether the AI system brings real value (called value proposition)—like saving time, improving diagnosis, or reducing errors—and whether it is easy to use alongside existing tools, machines, and hospital software.
For example, if an AI tool gives accurate results but is hard to use or doesn’t connect with the hospital’s records system, doctors may not want to use it. But if it’s well-designed and fits smoothly into their current way of working, they’re more likely to accept and trust it. |
This concept is supported by the Technology Acceptance Model (TAM) developed by Davis (1989), which says that people are more likely to use a technology if they find it: Useful (helps them do their job better) ,Easy to use (doesn’t make their work harder)
In the ScienceDirect article [Alhashmi et al., 2022], the “system usage” category also includes workflow integration, AI system reliability, and practical benefits—all crucial to acceptance in clinical settings. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 9 |
How does ethicality impact AI acceptability among healthcare professionals?
|
Affects views on AI based on compatibility with professional values |
|
Ethicality in AI means whether the technology matches with doctors’ and nurses’ values, like protecting patient privacy, doing no harm, and being honest and fair. If healthcare professionals believe that AI systems follow these ethical standards, they are more likely to trust and accept using them in their work. For example, if an AI system makes decisions that seem unfair or biased—or if it doesn’t explain its reasoning clearly—many professionals will hesitate to use it. But if the system is designed to respect ethics in healthcare, like informed consent, patient confidentiality, and fairness, it becomes more acceptable to use. |
This view is supported by the concept of “perceived ethicality” in health technology adoption, which is part of AI acceptability frameworks like the one reviewed in Alhashmi, S.F., et al. (2023). Factors influencing the acceptability of artificial intelligence in healthcare: A systematic review. Digital Health, https://doi.org/10.1016/j.dhjo.2022.100028
In that article, ethicality is described as an important dimension of AI acceptance. If AI systems conflict with medical values—like risking patient data or lacking accountability—it lowers healthcare professionals’ willingness to use them.
Also, the Unified Theory of Acceptance and Use of Technology (UTAUT) and extensions like UTAUT2 mention that ethical concerns and value compatibility can strongly affect a user’s behavioral intention to use new technology. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 10 |
What methodological approach did the article emphasize for future AI acceptability studies?
|
Considering user experience and system integration deeply |
|
The article emphasizes that to improve AI acceptability in healthcare, future studies must focus more on the user experience and how well AI systems fit into daily clinical workflows. This means researchers should study not just how smart or accurate the AI is, but also how easy it is for doctors and nurses to use, how to affects their work routines, and how smoothly it works with existing tools and hospital systems.
For example, even if an AI tool is highly accurate, if it is hard to use or disrupts a doctor’s workflow, it won’t be accepted. So, understanding the human-AI interaction is very important for designing systems that medical professionals will trust and use. |
lay on the statement of Alhashmi, S. F., et al. (2023). Factors influencing the acceptability of artificial intelligence in healthcare: A systematic review. Digital Health, https://doi.org/10.1016/j.dhjo.2022.100028
The study concludes that many past research efforts overlook the importance of user interaction and system integration, which are critical to real-world AI implementation. It recommends that future research and development should prioritize human-centered design and test how AI systems are actually used in practice—not just how they perform in theory.
This approach also connects with the Technology Acceptance Model (TAM), which says that perceived ease of use and perceived usefulness are two key factors that influence whether people accept and adopt a new technology. |
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 11 |
What is the primary objective of using human embryonic stem cells in treating Parkinson’s disease?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 12 |
Which animal was used to test the STEM-PD product for safety and efficacy?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 13 |
What was the duration of the preclinical safety study in rats mentioned in the article?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 14 |
What is the name of the clinical trial phase mentioned for STEM-PD?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 15 |
How is the STEM-PD product manufactured?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 16 |
According to the article, what confirmed the safety of the STEM-PD product in rats?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 17 |
What key finding was noted in the efficacy study of STEM-PD in rats?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 18 |
What specific markers were used to assess the purity of the STEM-PD batch?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 19 |
What role do growth factors like FGF8b and SHH play in the manufacturing process of STEM-PD?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|
| 20 |
What was a key outcome measured in the preclinical trials for efficacy in rats?
|
|
|
|
|
7 |
-.50
-.25
+.25
เต็ม
0
-35%
+30%
+35%
|