| 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 primary function of AI in medical imaging is to enhance diagnostic accuracy, which in turn leads to better clinical decisions and improved patient outcomes. AI systems—especially those based on deep learning and pattern recognition—can:
Detect diseases such as cancer, pneumonia, or fractures from X-rays, MRIs, or CT scans with high accuracy
Help radiologists identify abnormalities faster and more consistently
Reduce human error and inter-observer variability
Enable earlier detection, which is critical for treatment success and patient survival
Although AI also contributes to cost-saving, workflow optimization, and research, its core value in medical imaging lies in clinical accuracy and effectiveness. |
McKinsey & Company Report (2020)
> AI in imaging is expected to lead to earlier diagnosis, reduced diagnostic errors, and personalized treatment—all improving patient outcomes. |
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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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One of the key benefits of AI in radiology is that it functions as a second opinion to assist radiologists in interpreting medical images. This means AI:
Confirms or questions human diagnosis, helping reduce human error
Offers consistent, data-driven insights based on large datasets and prior patterns
Enhances diagnostic confidence by flagging areas of concern a human may miss
Supports early detection, especially in subtle or complex cases
Rather than replacing radiologists, AI tools are designed to augment their decision-making, acting as a clinical support system. |
European Society of Radiology (ESR) White Paper (2019)
> "AI acts as a second reader and can provide a safety net for radiologists, especially in high-volume settings." |
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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 refers to the understanding, awareness, and basic knowledge of how AI works, how it’s used, and what its implications are. This includes:
Knowing what AI can and cannot do
Understanding how AI makes decisions (e.g., using data, patterns, algorithms)
Being able to critically assess AI tools and their outputs
Recognizing potential biases or limitations
In healthcare, AI literacy helps clinicians, patients, and healthcare managers to:
Use AI tools responsibly and effectively
Interpret AI-based recommendations with caution
Build trust in AI-supported systems
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According to Long & Magerko (2020), AI Literacy: A Conceptual Framework:
> AI literacy is “the set of competencies that enable people to critically evaluate AI technologies, communicate and collaborate effectively with AI, and use AI applications ethically.” |
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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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Among the factors influencing healthcare professionals' acceptance of AI, the color of the AI machines is irrelevant and not supported by scientific research. In contrast, studies and reviews consistently highlight the following key influences:
Trust in AI systems – Professionals are more likely to use AI if they trust its accuracy and reliability.
Integration with existing workflows – If AI systems work seamlessly within current clinical routines, they’re more likely to be adopted.
System understanding – The better users understand how AI works, the more confident and willing they are to use it.
Technology receptiveness – Attitudes toward innovation and previous experiences with tech influence acceptance. |
Technology Acceptance Model (TAM):
Developed by Davis (1989), this widely used model explains that two key predictors of technology use are:
Perceived usefulness
Perceived ease of use
Trust, understanding, and compatibility are extensions of this model in modern healthcare AI applications. |
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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 how the opinions, behaviors, or expectations of colleagues, supervisors, professional groups, or institutions shape an individual’s decision to adopt or reject a new technology like AI. In healthcare, this plays a significant role in AI acceptability because:
Medical professionals often rely on peer validation and institutional norms when evaluating unfamiliar technologies
If respected colleagues or leaders support AI use, others are more likely to follow
Conversely, skepticism or resistance within the medical team can slow down adoption—even if the AI tool is effective |
Unified Theory of Acceptance and Use of Technology (UTAUT):
> This model identifies social influence as one of the four key constructs affecting technology adoption behavior.
It refers to “the degree to which an individual perceives that important others believe they should use the new system.” |
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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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A major perceived threat regarding AI usage in healthcare is the fear that AI will replace doctors, nurses, or other healthcare workers. This concern arises from:
AI’s increasing capability in diagnosis, image interpretation, and clinical decision support
Media and industry narratives promoting AI as "smarter" or faster than humans
Lack of clear communication about AI being a tool for augmentation, not replacement
Such concerns can reduce trust, lead to resistance, and slow down adoption, especially if professionals feel their roles or expertise are being devalued. |
Technology Threat Avoidance Theory (TTAT):
> This theory suggests that when individuals perceive a technology as a threat to their role, they may resist or avoid adopting it—even if it offers benefits. |
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| 7 |
According to the article, what is essential for increasing AI acceptability among medical professionals?
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Decreasing the cost of AI systems |
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According to the article, the key to increasing AI acceptability among medical professionals is to design human-centred AI (HCAI) systems. This means AI should be:
Aligned with clinicians’ real-world needs and workflows
Transparent, explainable, and easy to use
Supportive of ethical values, trust, and shared decision-making
Designed with end-user input, so clinicians feel confident and empowered when using it
Even if an AI system has high algorithmic performance, it will not be widely accepted if it disrupts clinical work, lacks clarity, or feels foreign to users. |
Human-Centred AI (HCAI) Framework:
Ben Shneiderman (2020): HCAI focuses on keeping humans in control, emphasizing reliability, safety, and trustworthiness.
The goal is to ensure AI augments human capabilities, not replaces them. |
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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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In the article, the 'system usage' category refers to practical, operational aspects that influence whether healthcare professionals are willing and able to adopt AI. These include:
Value proposition: How beneficial the AI system is perceived to be (e.g., does it save time, improve diagnostics, enhance efficiency?)
Workflow integration: How smoothly the AI system fits into existing clinical routines and processes
Ease of use: Whether the system is intuitive and minimizes disruption
Compatibility: Whether the system aligns with current tools, platforms, and staff capabilities
These factors are crucial for real-world implementation and are more impactful than demographic or geographic characteristics. |
Technology Acceptance Model (TAM) and UTAUT
These models highlight that “Perceived Usefulness” and “Effort Expectancy” are major drivers of technology adoption.
If a system integrates well into existing workflows, it reduces cognitive load and resistance to 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 in the context of AI acceptability refers to how well the AI system aligns with healthcare professionals' core values, such as:
Do no harm (non-maleficence)
Beneficence (promoting patient well-being)
Autonomy (respecting patients' rights and informed consent)
Justice (fair access and non-discrimination)
If an AI system is perceived as ethically sound, clinicians are more likely to trust and adopt it. Conversely, if it violates ethical norms—e.g., introduces bias, reduces patient-centeredness, or lacks transparency—it may be rejected, regardless of technical performance. |
Morley et al. (2020), The Lancet Digital Health
> Call for AI systems to be developed with explicit ethical frameworks to ensure clinician and patient confidence. |
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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 emphasizes that for future studies on AI acceptability in healthcare, researchers must go beyond technical performance and examine how AI fits into the real-world experiences of users, including:
Healthcare professionals' interaction with AI systems
How easily AI integrates into existing clinical workflows
The emotional, cognitive, and trust-related factors influencing daily use
How AI systems affect communication, workload, and patient care processes
This user-centered and integration-focused approach helps ensure that AI tools are both effective and adoptable in actual practice, not just theoretically powerful. |
Topol, E. (2019), Deep Medicine
> Argues that user-centered AI is essential for “deepening the patient-clinician relationship,” not disrupting it. |
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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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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 hallmark motor symptoms such as tremors, rigidity, and bradykinesia.
The primary objective of using human embryonic stem cells (hESCs) in treating Parkinson’s disease is to differentiate these stem cells into dopamine-producing neurons and replace the lost neurons. This approach aims to restore dopamine levels and improve motor function. |
Barker et al. (2017), Nature Reviews Neurology
> Demonstrated that hESC-derived dopamine neurons can survive transplantation and integrate into host brain tissue, restoring motor function in animal models. |
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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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Non-human primates like monkeys are commonly used in preclinical testing for neurological therapies such as STEM-PD because:
Their brain anatomy and physiology are closely similar to humans, especially in the dopamine system affected by Parkinson’s disease.
Testing in monkeys provides more relevant data on safety, efficacy, and potential side effects before moving to human trials.
Rodents, while useful, do not replicate all aspects of human neurodegenerative diseases as precisely as primates. |
Preclinical Testing Principles:
Safety and efficacy testing in animal models with high translational relevance is essential before clinical trials.
Monkeys are often used in neuroscience research due to their similarity to human neural structures. |
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| 13 |
What was the duration of the preclinical safety study in rats mentioned in the article?
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12 months |
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The preclinical safety study in rats lasted 12 months to ensure long-term assessment of the STEM-PD product's safety profile. This duration allows researchers to:
Monitor potential delayed adverse effects
Evaluate the durability and stability of the transplanted stem cells
Assess any tumorigenicity or immune reactions over a longer period
Such extended observation is critical for therapies involving stem cells because of their potential for uncontrolled growth or late-onset side effects. |
Preclinical Toxicology Guidelines:
Regulatory agencies (e.g., FDA, EMA) recommend long-term animal studies (often 6-12 months or more) for cell-based therapies before 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 clinical trial for STEM-PD is described as a Phase I/IIa trial, which typically means:
Phase I: Focuses on assessing safety and tolerability in a small group of participants.
Phase IIa: Explores early signs of efficacy and further safety data in a slightly larger cohort.
Combining Phase I and IIa allows researchers to efficiently transition from safety to initial effectiveness testing, which is common in early-stage trials of innovative therapies like stem cell treatments. |
Clinical Trial Phases Overview:
Phase I: First-in-human trials, mainly safety-focused.
Phase IIa: Early efficacy and dose-finding within a limited patient group. |
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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 STEM-PD product is manufactured under Good Manufacturing Practice (GMP)-compliant conditions to ensure:
Quality, safety, and consistency of the stem cell product
Compliance with regulatory standards required for clinical-grade therapies
Minimization of contamination and variability, which is crucial for patient safety in cell therapies
Manufacturing under GMP is a standard requirement for any product intended for human clinical use. |
Good Manufacturing Practice (GMP):
GMP guidelines regulate production processes to ensure products meet quality standards.
Critical for advanced therapy medicinal products (ATMPs) like stem cell therapies. |
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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 safety of the STEM-PD product in rats was confirmed because:
The preclinical safety study observed no adverse effects, such as immune reactions or toxicities.
Importantly, no tumor formation was detected, which is a critical safety concern in stem cell therapies due to their potential for uncontrolled growth.
The biodistribution was confined, indicating the cells did not migrate outside the targeted brain area.
These findings demonstrate the product’s biocompatibility and safety for further clinical development. |
Safety Assessment in Stem Cell Therapies:
Tumorigenicity is a major risk, so absence of tumors is a key indicator of safety.
Localized biodistribution reduces risks of ectopic tissue formation or systemic effects. |
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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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In the efficacy study of STEM-PD in rats, the transplanted dopamine neurons derived from human embryonic stem cells successfully survived, integrated, and functioned to:
Restore dopamine levels
Improve motor function impaired by Parkinson’s disease-like symptoms
This demonstrates the potential therapeutic effect of the stem cell product in reversing key disease manifestations. |
Stem Cell Therapy Mechanism:
Replacement of lost dopamine neurons restores the neural circuitry controlling movement.
Functional integration of transplanted cells is essential for reversing symptoms. |
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| 18 |
What specific markers were used to assess the purity of the STEM-PD batch?
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GIRK2 and ALDH1A1 |
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The markers GIRK2 and ALDH1A1 are specific proteins used to identify and confirm the presence of mature dopaminergic neurons, particularly the A9 subtype of dopamine neurons most affected in Parkinson’s disease.
Assessing these markers in the STEM-PD batch ensures:
Purity and quality of the differentiated cells
That the cell product contains the correct neuronal subtype for effective therapy
Minimization of undifferentiated or off-target cells that could reduce safety or efficacy. |
Marker Significance:
GIRK2 (G protein-coupled inwardly rectifying potassium channel 2) and ALDH1A1 (Aldehyde dehydrogenase 1 family member A1) are well-established markers for A9 dopaminergic neurons, which are crucial for motor control.
Other markers like OCT4 and NANOG indicate pluripotent stem cells, not mature neurons, so they are not used for purity in this context. |
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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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Growth factors such as FGF8b (Fibroblast Growth Factor 8b) and SHH (Sonic Hedgehog) play critical roles in guiding stem cells to differentiate into specific neural cell types, especially midbrain dopaminergic neurons relevant to Parkinson’s disease.
These factors mimic developmental signals in the embryonic brain that instruct cells on their positional identity and fate.
They help pattern the cells, ensuring the stem cells become the desired subtype of neurons needed for therapy.
Without these cues, stem cells would not efficiently or reliably differentiate into the targeted dopamine neurons. |
Lee et al. (2010), Nature Protocols
> Detailed protocols use SHH and FGF8b to pattern stem cells toward a ventral midbrain fate. |
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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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The key outcome measured in the preclinical trials for STEM-PD in rats was the recovery of motor function. This is because:
Parkinson’s disease primarily impairs movement control due to the loss of dopamine-producing neurons in the substantia nigra.
Therefore, in animal models of Parkinson’s disease (like rats with induced dopamine neuron damage), successful treatment is assessed by how well motor deficits are reversed.
After transplantation of the STEM-PD product (dopaminergic neurons), researchers observed improvements in movement, which confirmed functional efficacy. |
Scientific Principle:
Dopaminergic neurons are essential for motor control.
The loss of these neurons leads to symptoms such as tremors, rigidity, and slowed movement.
Recovery of these functions post-treatment demonstrates that the transplanted cells are working properly. |
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