| 1 |
What is the primary function of AI in the medical imaging industry?
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To improve diagnostic accuracy and patient outcomes |
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The scoping review emphasizes that one of the primary roles of AI in medical imaging is to enhance diagnostic accuracy, support clinical decision-making, and ultimately improve patient outcomes.
AI tools help detect abnormalities, reduce human error, and speed up image interpretation—making the diagnostic process more efficient and reliable.
Other options (such as automating administrative tasks or marketing medical products) are not described as primary functions in the medical imaging context. |
The review highlights that AI is mainly valued in medical imaging because it can:
• Assist with image interpretation
• Enhance diagnostic precision
• Improve clinical workflow and patient care
These points align with the principle that AI in healthcare is primarily adopted to improve the quality and outcomes of diagnosis, rather than for administrative or commercial purposes. |
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| 2 |
Which of the following is a key benefit of AI in radiology noted in the article?
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Acts as a second medical opinion |
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The article highlights that one of the most important perceived benefits of AI in radiology is that it can serve as a supportive tool, offering an additional layer of analysis that complements the radiologist’s interpretation.
AI systems are described as enhancing:
• Diagnostic support
• Confidence in decision-making
• Detection of subtle abnormalities
This aligns with the role of AI as a second reader or second medical opinion, helping radiologists verify findings and reduce the risk of missed diagnoses.
Other options are incorrect because:
• AI does not increase the need for radiologists — it supports them.
• AI generally increases the speed of diagnosing, not reduces it.
• Scheduling is not a main benefit mentioned in the article.
• AI does not increase imaging costs according to the review. |
The review repeatedly emphasizes that AI tools are valued for their ability to augment clinical decision-making, improve accuracy, and provide supportive second opinions, making radiologists more confident and efficient in their work. |
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| 3 |
What does AI literacy refer to according to the article?
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Understanding and knowledge of AI technology |
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The article explains that AI literacy among healthcare professionals refers to their understanding, knowledge, and familiarity with how AI works, including:
• What AI tools do
• Their limitations
• Their clinical applications
• How to interpret AI outputs
• How AI integrates into workflow
The review highlights that low AI literacy leads to low trust, fear of errors, and reluctance to adopt AI, while higher literacy increases confidence and acceptance.
It does not refer to:
• Repairing AI machines (technical engineering role)
• History of AI (not relevant to clinical adoption)
• Legal knowledge (related to regulation, not literacy)
• Financial management (administrative role) |
The article consistently states that AI acceptability depends on clinicians’ knowledge, awareness, and comprehension of AI systems, noting that AI literacy is a key enabler of adoption and trust.
This is highlighted in sections discussing knowledge gaps, training needs, and familiarity with AI. |
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| 4 |
Which factor is NOT listed as influencing the acceptability of AI among healthcare professionals?
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The color of the AI machines |
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The scoping review identifies several factors that do influence the acceptability of AI among healthcare professionals, including:
• Trust in AI systems (trust affects willingness to adopt AI)
• Integration with existing clinical workflows (workflow fit strongly impacts acceptance)
• System understanding / AI literacy (knowledge and familiarity affect confidence)
• Technology receptiveness (general openness to new technologies influences adoption)
These are discussed throughout the article as key themes influencing acceptability. 
However, the color of the AI machines is not mentioned anywhere as a factor affecting AI acceptance. It is irrelevant to clinical workflow, usability, safety, or trust. |
The article emphasizes that AI acceptability is shaped by:
• Human–AI trust
• Perceived usefulness
• User knowledge and training
• Workflow compatibility
• Ethical and professional concerns
None of these relate to physical appearance or machine color.
Therefore, “color of the AI machines” is not a factor in any framework or finding described in the review. |
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| 5 |
What role does social influence play in AI acceptability in healthcare according to the article?
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Affects healthcare professionals’ decisions to use AI |
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The article identifies social influence as one of the factors that shape whether healthcare professionals accept or reject AI systems.
Social influence in this context refers to:
• The opinions and behaviors of colleagues
• Recommendations from supervisors or respected peers
• Professional norms within the clinical environment
These social pressures can significantly affect a healthcare professional’s decision to adopt, trust, or routinely use AI tools in medical imaging.

The article does not say that social influence:
• Determines AI budgets
• Improves AI’s diagnostic accuracy
• Dictates marketing strategies |
Social influence is a core component of technology acceptance models (such as UTAUT) and the article explicitly draws on these frameworks when describing factors that shape AI adoption in healthcare.
According to these models—supported by the scoping review—social norms and peer acceptance strongly influence user behavior, especially in clinical teams where collaboration and conformity to standard practice are important. |
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| 6 |
What is a perceived threat regarding AI usage in healthcare settings?
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Concerns about replacing healthcare professionals |
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The scoping review identifies fear of job displacement as one of the most common perceived threats associated with AI adoption in healthcare. Healthcare professionals expressed concern that AI could:
• Replace radiologists or other clinical roles
• Reduce the need for certain specialist tasks
• Automate functions traditionally performed by humans
These concerns contribute to AI hesitancy and affect its acceptability and integration into medical workflows.

Other options are not highlighted in the study:
• Increased IT workload → Not emphasized as a key threat
• Less patient–doctor interaction → Not discussed as a major concern
• AI requiring more space → Not mentioned at all |
Concerns about job replacement reflect a major theme in the literature on technology acceptance, especially in AI-related fields. AI-related anxiety arises when individuals believe automation may reduce or undermine their professional role.
The article specifically highlights this under themes of:
• Professional identity and role security
• Trust in AI systems
• Concerns about autonomy and human oversight
These factors are recognized in frameworks like the Technology Acceptance Model (TAM) and UTAUT, which the article references to explain AI acceptability. |
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| 7 |
According to the article, what is essential for increasing AI acceptability among medical professionals?
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Designing human-centred AI systems |
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The article emphasizes that one of the most important determinants of AI acceptability among healthcare professionals is human-centred design. This includes:
• Making AI systems intuitive and easy to integrate into clinical workflows
• Supporting clinician autonomy rather than replacing it
• Ensuring transparency, explainability, and user control
• Reducing cognitive burden for radiologists and technicians
The review clearly states that AI systems must be aligned with the needs, expectations, and working patterns of medical professionals if they are to be accepted and effectively used.

Other choices are NOT emphasized as key requirements:
• Decreasing cost → Mentioned but not the central factor
• High algorithmic performance → Necessary but not enough alone for acceptability
• Aggressive promotion → Not relevant to professional acceptance
• None of the above → Incorrect |
Human-centred design is consistent with:
UTAUT (Unified Theory of Acceptance and Use of Technology)
• Focuses on performance expectancy, effort expectancy, and social influence—all improved when systems are designed around users.
Human-Computer Interaction (HCI) Principles
• Tools gain acceptance when they support workflow, reduce errors, and increase user confidence.
AI explainability and trust frameworks
• Transparency and user-friendly interfaces increase trust, a key factor repeatedly highlighted in the article. |
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| 8 |
What does the 'system usage' category of AI acceptability factors include according to the article?
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Factors like value proposition and integration with workflows |
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The article explains that the ‘system usage’ category includes factors related to how AI systems fit into real-world clinical practice, such as:
• Value proposition (whether AI provides meaningful clinical benefit)
• Integration with existing workflows
• Ease of use
• Practical applicability in routine settings
• Compatibility with clinical tasks
These aspects shape whether healthcare professionals can realistically and efficiently use the AI systems during imaging procedures. They are system-based determinants, not personal or demographic ones.
Other options do not match the article:
• User’s personal preferences only → Too narrow and incorrect
• Geographical location → Not part of system usage
• Age/experience of professionals → Falls under individual factors, not system usage
• Type of medical insurance → Not discussed in the review |
• Technology Acceptance Model (TAM)
System usage aligns with Perceived Usefulness (PU) and Perceived Ease of Use (PEOU)—key predictors of acceptance.
• Sociotechnical Systems Theory
AI adoption succeeds when tools fit seamlessly within organizational processes, not just when individuals prefer them.
• Human-Centred Design
Workflow integration and user value are core to designing acceptable, adoptable AI systems. |
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| 9 |
How does ethicality impact AI acceptability among healthcare professionals?
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Affects views on AI based on compatibility with professional values |
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The article identifies ethicality as one of the major factors that influence AI acceptability. Ethicality refers to:
• Whether AI aligns with professional norms,
• Whether it maintains patient safety,
• Whether its use respects ethical standards such as fairness, transparency, and accountability.
Healthcare professionals evaluate whether AI systems are consistent with their moral and professional values, and this directly impacts their willingness to adopt them.
Other options are incorrect because:
• “Determines the pricing of AI systems” → pricing is unrelated to ethicality.
• “Only relevant in legal contexts” → ethicality is relevant clinically, not only legally.
• “Not significant in medical settings” → the article emphasizes ethicality is significant.
• “Relates only to the manufacturing of AI systems” → ethicality goes beyond manufacturing to usage and decision-making.
Thus, the correct interpretation is that ethicality influences acceptability by shaping how professionals judge AI in relation to their ethical and professional values. |
• Professional Ethics in Healthcare
Healthcare providers must ensure that any technology complies with ethical principles such as:
• Beneficence (doing good)
• Non-maleficence (avoiding harm)
• Autonomy (respecting patient rights)
• Justice (fairness)
AI adoption is evaluated through this ethical lens.
• Unified Theory of Acceptance and Use of Technology (UTAUT) – “Ethicality” dimension
The study draws on frameworks like UTAUT extensions, where ethicality is a recognized factor influencing technology acceptance.
• Trust and Value Alignment Theory
Professionals adopt systems that match their ethical beliefs, clinical responsibilities, and patient-centred care priorities. |
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| 10 |
What methodological approach did the article emphasize for future AI acceptability studies?
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Considering user experience and system integration deeply |
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The article highlights that future AI acceptability research should move beyond simple technical evaluation and instead adopt more comprehensive methodological approaches. Specifically, the study emphasizes:
• Understanding how AI integrates into clinical workflows
• Evaluating user experience and interaction with AI tools
• Examining real-life clinical implementation factors, not just theoretical or performance-based metrics
The review repeatedly stresses the need for context-rich, user-centred, and workflow-aware study designs, because acceptability is strongly influenced by how AI fits into the practical everyday work of healthcare professionals.
Other options are incorrect:
• Focusing only on economic factors is not suggested.
• Prioritizing speed over accuracy contradicts clinical priorities.
• Using AI only in large hospitals is not a methodological recommendation.
• Therefore, “None of the above” is also incorrect.
Thus, the article clearly supports a user-centred, workflow-integration–focused methodological direction. |
The recommendation aligns with established principles in technology-acceptance research:
1. User-centred design (UCD)
Future studies must consider how clinicians interact with AI systems, ensuring usability and intuitive workflow alignment.
2. Sociotechnical Systems Theory
The success of AI does not depend only on the technology but also on:
• human factors
• organizational structures
• workflow placement
• cultural acceptance
3. Extended Technology Acceptance Models (e.g., UTAUT, TAM2)
These models emphasize variables such as:
• perceived usefulness
• workflow compatibility
• effort expectancy (ease of use)
The article encourages applying these frameworks to future research to produce more meaningful and applicable evidence. |
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| 11 |
What is the primary objective of using human embryonic stem cells in treating Parkinson’s disease?
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To replace lost dopamine neurons. |
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The article explains that the primary goal of using human embryonic stem cell–derived products (like STEM-PD) is to generate new dopamine-producing neurons that can replace those lost in Parkinson’s disease. Parkinson’s symptoms arise because dopaminergic neurons in the substantia nigra degenerate. The stem cell–derived therapy is designed to restore dopamine levels and improve motor function by replacing these lost cells. |
The preclinical study emphasizes that the therapeutic objective of STEM-PD is dopaminergic neuron replacement. The stem cells are differentiated into midbrain dopaminergic neurons, which are then transplanted to restore dopamine neurotransmission. This aligns with the established scientific principle that Parkinson’s disease results primarily from dopaminergic neuron loss, and effective cell therapies aim to replace these neurons to recover function. |
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| 12 |
Which animal was used to test the STEM-PD product for safety and efficacy?
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Monkeys |
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The article on STEM-PD reports that the preclinical evaluation of the human embryonic stem cell–derived product was performed in non-human primates, specifically monkeys. These animals are commonly used for Parkinson’s disease models because their brain structure and dopaminergic systems more closely resemble humans compared to rodents. This makes them suitable for assessing both safety (e.g., tumor formation, immune response) and efficacy (e.g., motor improvement, dopamine restoration). |
The study includes long-term transplantation experiments in Parkinsonian monkeys, demonstrating survival, integration, and functional recovery after receiving the STEM-PD product. This aligns with standard preclinical requirements for cell-based therapies, where primate models are used before moving to human clinical trials due to their high translational relevance. |
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| 13 |
What was the duration of the preclinical safety study in rats mentioned in the article?
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9 months |
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The article states that the preclinical safety assessment in rats lasted 39 weeks, which is approximately 9 months. This long-term study was conducted to evaluate toxicity, tumorigenicity, and biodistribution of the STEM-PD cell product. The results showed no adverse effects, supporting the product’s safety before moving to clinical trials.
Evidence from the article:
The study reports a 39-week (≈9 months) toxicity and safety evaluation in rats. |
The duration aligns with standard Good Laboratory Practice (GLP) guidelines for cell-based therapies, which require long-term animal studies to detect possible delayed toxicity or tumor formation. For neural stem-cell–based treatments such as STEM-PD, extended monitoring is essential to ensure stable engraftment and absence of uncontrolled cell proliferation prior to human trials. |
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| 14 |
What is the name of the clinical trial phase mentioned for STEM-PD?
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Phase I/IIa |
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The article states that the STEM-PD product is being prepared for a first-in-human Phase I/IIa clinical trial, which focuses on assessing both safety and preliminary efficacy in patients with Parkinson’s disease. This combined phase is typical for early-stage trials involving advanced therapies such as stem-cell–derived neuronal products.
Evidence from the article: The product is described as moving toward a Phase I/IIa clinical trial. |
Phase I/IIa trials are part of the early translational research pathway for regenerative medicine products. These trials are designed to:
• confirm safety in humans (Phase I)
• collect initial efficacy data (Phase IIa)
• refine dose and procedure before larger studies
This approach is consistent with regulatory frameworks for cell-based and stem-cell–derived therapies, which require cautious, stepwise introduction into clinical testing. |
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| 15 |
How is the STEM-PD product manufactured?
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Under GMP-compliant conditions |
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The article clearly states that the STEM-PD product is manufactured under Good Manufacturing Practice (GMP)–compliant conditions to ensure quality, safety, and regulatory suitability for clinical use.
Evidence: The manuscript specifies that the production process was developed and executed in accordance with GMP standards, which are required for clinical-grade stem cell–derived therapeutic products. |
GMP compliance is essential for any cell-based therapy moving toward human clinical trials. It ensures:
• strict control of production steps
• minimization of contamination risks
• traceability and reproducibility of cell products
• fulfillment of regulatory requirements for advanced therapeutic medicinal products (ATMPs)
This aligns with international regulatory frameworks for stem-cell–derived therapies, ensuring the product is safe and reliable for clinical testing. |
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| 16 |
According to the article, what confirmed the safety of the STEM-PD product in rats?
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There were no adverse effects or tumor formation. |
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The article reports that the preclinical safety study in rats showed no evidence of tumor formation, no adverse effects, and no unwanted cell migration, confirming the safety of the STEM-PD product.
The safety assessment demonstrated stable grafts, absence of overgrowth, and no harmful immune reactions.
This directly supports the option stating that no adverse effects or tumor formation were observed. |
In stem-cell–derived therapies, safety is primarily evaluated by checking for:
• Tumorigenicity (risk of uncontrolled cell proliferation)
• Toxicity or adverse effects
• Biodistribution (ensuring cells stay where they are intended to be)
The absence of these issues is a critical requirement before advancing to human clinical trials.
The results align with standard preclinical safety evaluation principles for cell-based medicinal products to ensure they pose minimal risk before clinical use. |
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| 17 |
What key finding was noted in the efficacy study of STEM-PD in rats?
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Transplanted cells reversed motor deficits in rats. |
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The article reports that in the efficacy studies, STEM-PD transplanted cells survived, differentiated into dopaminergic neurons, and improved motor function in the rat model of Parkinson’s disease.
Specifically, rats receiving the treatment showed significant recovery of motor deficits, demonstrating the therapeutic potential of the cell product.
This directly supports the option stating that transplanted cells reversed motor deficits in rats. |
The efficacy of cell-based therapies for Parkinson’s disease is evaluated by assessing:
• Survival of transplanted cells
• Differentiation into dopamine-producing neurons
• Functional motor recovery in animal models
Motor improvement is the gold-standard outcome measure in preclinical PD studies.
The restoration of dopamine neuron function leading to improved movement validates both the therapeutic mechanism and the potential for clinical translation. |
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| 18 |
What specific markers were used to assess the purity of the STEM-PD batch?
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LMX1A and EN1 |
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According to the article, the purity and identity of the STEM-PD batch were assessed using dopaminergic lineage–specific markers, particularly those associated with midbrain floor-plate dopaminergic neuron development.
The markers LMX1A and EN1 are hallmark transcription factors required for midbrain dopaminergic neuron specification and are used to confirm correct differentiation of the stem-cell product.
The article explicitly lists these as key markers in the product characterization and quality control steps. |
Stem-cell–derived dopaminergic therapies rely on confirming:
• Correct lineage identity
• Purity of the differentiated population
• Absence of unwanted cell types
LMX1A and EN1 are standard markers for midbrain dopaminergic progenitors, making them appropriate indicators for STEM-PD product validation. |
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| 19 |
What role do growth factors like FGF8b and SHH play in the manufacturing process of STEM-PD?
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They are used in cell patterning for specific neural fates. |
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In the STEM-PD manufacturing process, growth factors such as FGF8b (Fibroblast Growth Factor 8b) and SHH (Sonic Hedgehog) are essential for guiding human embryonic stem cells toward midbrain dopaminergic neuron identity.
The article states that these factors are used to pattern stem cells into the correct neural lineage by mimicking embryonic developmental signals necessary for dopaminergic neuron differentiation.
They help ensure that the resulting cells adopt the ventral midbrain dopaminergic fate, which is critical for Parkinson’s disease therapy. |
Stem-cell differentiation relies on developmental patterning signals:
• SHH provides ventralizing cues.
• FGF8b provides midbrain patterning signals.
Together, these growth factors drive cells toward the specific neural fate required for functional dopamine neuron replacement. |
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| 20 |
What was a key outcome measured in the preclinical trials for efficacy in rats?
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Recovery of motor function |
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In the preclinical efficacy trials described in the article, the primary goal was to determine whether the transplanted STEM-PD cells could reverse motor deficits in Parkinson’s disease rat models.
The study reported that rats receiving the dopaminergic neuron grafts showed significant improvement in motor performance, indicating functional recovery. This demonstrated that the transplanted cells not only survived and differentiated but also produced meaningful behavioral improvements. |
Parkinson’s disease is fundamentally a motor disorder caused by loss of dopamine neurons.
Therefore, in preclinical models, motor function recovery is the most relevant and widely accepted measure of therapeutic efficacy.
Behavioral tests such as rotation assays, limb-use asymmetry, and other motor performance measures are standard for evaluating dopaminergic neuron replacement therapies. |
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