| 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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AI technologies in medical imaging, such as machine learning and deep learning algorithms, are designed to analyze complex image data quickly and accurately.
These tools help detect abnormalities that might be missed by human eyes, reduce diagnostic errors, and assist in decision-making.
By improving the precision and speed of diagnosis, AI contributes directly to better patient outcomes, such as timely treatment and reduced complications.
While AI can support administrative tasks or research, its primary function in medical imaging is clinical enhancement. |
This is grounded in the Technology Acceptance Model (TAM), which explains how healthcare professionals adopt technologies that enhance performance and outcomes.
Additionally, concepts from Medical Informatics and Clinical Decision Support Systems (CDSS) highlight AI’s role in improving diagnostic processes and patient care. |
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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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AI systems in radiology are designed to analyze medical images and highlight potential abnormalities, serving as a decision-support tool for radiologists.
This allows AI to act as a second opinion, helping confirm findings or alerting the clinician to something they may have missed.
Rather than replacing radiologists, AI enhances accuracy, reduces oversight, and increases diagnostic confidence—ultimately improving patient safety and outcomes. |
This aligns with the concept of Clinical Decision Support Systems (CDSS), which are designed to aid healthcare professionals by providing evidence-based assistance during diagnosis.
It also draws from Augmented Intelligence theory, which emphasizes using AI to enhance—not replace—human expertise, especially in complex tasks like medical image interpretation. |
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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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AI literacy involves having the knowledge, awareness, and critical understanding of how artificial intelligence works and how it is applied in real-world settings.
In the context of the article, especially within healthcare and radiology, AI literacy helps professionals make informed decisions when interacting with AI tools, recognize their limitations, and use them ethically and effectively.
It does not involve repairing machines or legal/financial tasks, but rather focuses on the practical understanding and responsible use of AI systems. |
AI literacy is grounded in the Digital Literacy Framework, which includes the ability to access, understand, evaluate, and use digital technologies effectively and responsibly.
It also aligns with Technology Acceptance Model (TAM), which suggests that understanding a technology’s usefulness and ease of use influences its adoption and responsible use. |
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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 acceptability of AI among healthcare professionals depends on meaningful factors such as trust, ease of integration into clinical workflows, understanding of how AI works, and overall openness to new technology.
These elements influence how confident and willing professionals are to use AI tools in medical settings.
In contrast, the color of the AI machines is purely cosmetic and has no impact on performance, usability, or trust—making it irrelevant to the acceptability of AI in healthcare. |
This is based on the Technology Acceptance Model (TAM), which identifies key factors influencing technology adoption |
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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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Social influence refers to the impact that colleagues, supervisors, and professional norms have on an individual’s behavior. In healthcare, when respected peers or institutions adopt and support AI, it builds confidence and trust, encouraging others to do the same.
The article emphasizes that healthcare professionals are more likely to accept and use AI when they see it being positively received by those around them. This influence does not affect the technical accuracy of AI or financial budgeting, but it directly shapes user behavior and acceptance.
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This concept is grounded in the Theory of Planned Behavior (TPB) and Social Influence Theory, which highlight that individuals’ behaviors are strongly shaped by social norms, peer pressure, and perceived expectations from their professional community.
In technology adoption, these theories explain how social context affects willingness to use new tools like AI. |
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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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Many healthcare workers perceive AI as a potential threat because it may automate tasks traditionally done by humans, raising fears about job security and professional relevance.
This concern can create resistance to adopting AI, impacting its integration into healthcare settings.
While AI aims to assist rather than replace professionals, the fear of replacement remains a significant psychological barrier affecting user acceptance. |
This is related to the Technology Acceptance Model (TAM) and Job Security Theory, which suggest that fear of job loss or role displacement can negatively impact acceptance of new technologies.
Concerns about automation replacing human roles create resistance despite potential benefits. |
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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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Human-centred AI design prioritizes the needs, preferences, and workflows of healthcare professionals, making AI tools easier to use and more relevant in clinical settings.
This approach enhances usability, transparency, and trust, which are critical factors influencing acceptance. When users feel that AI systems support rather than hinder their work, they are more likely to adopt them.
Although high algorithmic performance and cost considerations matter, the design that respects human factors is key to successful implementation and acceptability. |
This is based on Human-Centered Design (HCD) principles, which emphasize designing technologies that fit users’ needs, capabilities, and contexts to improve acceptance and effectiveness.
It also relates to the Technology Acceptance Model (TAM), where perceived ease of use and usefulness are key determinants of technology adoption. |
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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 'system usage' category focuses on how the AI technology fits within the clinical environment, including its value proposition—the practical benefits it offers—and its integration with existing workflows.
If the AI system aligns well with healthcare professionals’ tasks and adds clear value, it is more likely to be accepted and used regularly.
This category does not emphasize personal preferences or demographic factors, nor unrelated external factors like location or insurance. |
This aligns with the Technology Acceptance Model (TAM), which highlights perceived usefulness and ease of integration as key factors influencing technology adoption.
It also relates to Sociotechnical Systems Theory, emphasizing the importance of fitting technology into existing social and organizational contexts for successful use. |
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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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Ethicality refers to the alignment of AI systems with healthcare professionals’ moral principles, professional standards, and patient-centered care values.
When AI technologies are perceived to support or uphold these values, professionals are more likely to trust and accept them.
If AI raises ethical concerns—such as patient privacy, fairness, or accountability—it can lead to skepticism and resistance.
Therefore, ethical compatibility is a crucial factor in shaping healthcare providers’ attitudes toward AI adoption. |
This relates to Ethical Decision-Making Theory and the Technology Acceptance Model (TAM) extension that incorporates ethical compatibility as a determinant of technology adoption.
It emphasizes that users are more likely to accept technologies aligned with their ethical beliefs and professional norms. |
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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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acceptability among healthcare professionals depends not only on technical performance but also on how well the AI system fits into existing workflows and meets users’ needs. Focusing on user experience ensures that healthcare professionals find the AI intuitive, trustworthy, and helpful. System integration is crucial because if AI tools disrupt clinical processes or are difficult to use, professionals are less likely to adopt them. Therefore, future studies should emphasize these factors to improve acceptance and effective implementation of AI in healthcare settings.
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The key theoretical framework supporting this answer is the Technology Acceptance Model (TAM), which highlights that perceived usefulness and perceived ease of use significantly influence users’ acceptance of new technologies. Additionally, Socio-Technical Systems Theory emphasizes that successful technology adoption requires alignment between technical systems and social/organizational contexts. These frameworks suggest that deep consideration of user experience and system integration is essential for AI acceptability among healthcare professionals. |
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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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because Parkinson’s disease is characterized by the progressive loss of dopamine-producing neurons in the substantia nigra region of the brain. This loss leads to the motor symptoms typical of the disease. Human embryonic stem cells have the ability to differentiate into various cell types, including dopamine neurons. By transplanting these stem cells, the goal is to restore the depleted dopamine neuron population, thereby improving motor function and alleviating symptoms of Parkinson’s disease. |
Lindvall, O., & Kokaia, Z. (2010). Stem cells in human neurodegenerative disorders — time for clinical translation? The Journal of Clinical Investigation, 120(1), 29–40.
Barker, R. A., Parmar, M., Studer, L., & Takahashi, J. (2017). Human Trials of Stem Cell–Derived Dopamine Neurons for Parkinson’s Disease: Dawn of a New Era. Cell Stem Cell, 21(5), 569–573. |
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| 12 |
Which animal was used to test the STEM-PD product for safety and efficacy?
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Rats |
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Rats are commonly used in preclinical studies due to their well-characterized nervous system and suitability for modeling Parkinson’s disease. The article specifically mentioned rats as the model organism to evaluate both safety and efficacy of the STEM-PD product before moving to clinical trials. |
Rodent models are widely accepted in neurodegenerative research for initial testing of cell therapies because they allow controlled observation of biological effects and toxicology.
Lindvall & Kokaia (2010). The Journal of Clinical Investigation
Barker et al. (2017). Cell Stem Cell
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| 13 |
What was the duration of the preclinical safety study in rats mentioned in the article?
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6 months |
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A 6-month safety evaluation period is standard to assess long-term adverse effects such as tumorigenicity and immune response after stem cell transplantation in animal models. |
Preclinical safety protocols typically recommend at least 6 months follow-up in rodent models to ensure cell therapy safety before human trials.
FDA Guidance for Industry (2013), Preclinical Assessment of Investigational Cellular Therapies
Lindvall & Kokaia (2010) |
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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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Phase I/IIa trials combine early safety (Phase I) and preliminary efficacy (Phase IIa) assessments in a small group of patients, common for novel cell-based therapies. |
This combined phase is designed to efficiently assess safety and initial clinical benefit in small patient cohorts.
References: Kimmelman (2014), ClinicalTrials.gov standards.
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| 15 |
How is the STEM-PD product manufactured?
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Under GMP-compliant conditions |
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Good Manufacturing Practice (GMP) ensures consistent product quality, safety, and regulatory compliance necessary for clinical-grade cell therapies. |
GMP is a regulatory requirement to minimize contamination and variability in therapeutic cell production.
References: EMA/FDA guidelines, Barker et al. (2017).
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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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Safety was confirmed by absence of tumorigenicity, no unexpected biodistribution, and lack of immune rejection or toxicity. |
Tumor formation is a key safety concern in stem cell therapy; absence indicates safe clinical translation potential.
References: Lindvall & Kokaia (2010), FDA guidelines. |
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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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Improvement in motor function demonstrates functional integration and dopamine production by transplanted cells. |
Motor recovery is the main functional endpoint in Parkinson’s disease animal models.
References: Barker et al. (2017), Lindvall & Kokaia (2010). |
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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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LMX1A and EN1 are transcription factors specific to midbrain dopaminergic neuron progenitors, indicating correct cell lineage. |
Use of lineage-specific markers ensures differentiation fidelity and purity of stem cell products.
References: Arenas et al. (2015), Barker et al. (2017). |
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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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FGF8b and SHH mimic developmental signals to guide stem cells toward midbrain dopaminergic neuron lineage. |
Developmental biology principles applied to stem cell differentiation protocols ensure correct cell type specification.
References: Arenas et al. (2015), Studer (2012). |
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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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Recovery of motor skills indicates successful therapeutic effect in Parkinson’s disease models. |
Functional behavioral tests are standard for evaluating efficacy of cell therapy in animal models.
References: Barker et al. (2017), Lindvall & Kokaia (2010).
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