| 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, especially deep learning algorithms, analyzes large volumes of medical images (X-ray, CT, MRI) faster and more consistently than humans. It can detect subtle abnormalities that may be missed by radiologists, leading to earlier and more accurate diagnosis, which improves patient outcomes. Meta-analyses and reviews have repeatedly confirmed that AI often performs at or above human expert level in diagnostic accuracy. |
Pattern recognition and machine learning theory underpins AI in medical imaging. AI models, especially deep neural networks, learn to detect patterns across imaging modalities that may be invisible to humans. Reviews supporting this include:
• Liu et al., 2021, Diagnostic accuracy of deep learning in medical imaging: a systematic review (PMC)
• Jiang et al., 2025, The Role of AI in Improving Diagnostic Accuracy (ScienceDirect) |
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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, particularly through computer-aided diagnosis (CAD) systems, can analyze medical images (X-ray, CT, MRI) to detect abnormalities that might be missed by human radiologists. This provides a “second opinion” that supports clinical decision-making, increases diagnostic confidence, and reduces the likelihood of missed findings. Reviews and systematic studies report that AI often performs at or above expert level in detecting subtle or complex pathologies. |
The underlying principle is that AI functions as a decision-support system using machine learning and pattern recognition to identify subtle imaging features that humans might miss; it systematically analyzes large volumes of data to reduce human error and inter-observer variability, thereby enhancing diagnostic accuracy and providing a reliable second opinion. This concept is supported by systematic reviews and meta-analyses such as Liu et al., 2021, Diagnostic accuracy of deep learning in medical imaging: a systematic review (PMC), and Jiang et al., 2025, The Role of AI in Improving Diagnostic Accuracy (ScienceDirect), which demonstrate AI’s ability to detect complex pathologies across modalities with performance comparable to expert radiologists. |
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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 and understanding of how AI works — including its basic principles, capabilities, limitations, and ethical or societal implications — so that a person can use or interact with AI in an informed and responsible way. It’s not just about being able to press buttons, but about knowing what’s happening behind the scenes, what AI can and cannot do, and what risks or benefits it brings. |
The conceptual framework for AI literacy defines it as a set of competencies including understanding, using, evaluating, and ethically interacting with AI systems — implying both technical knowledge and critical awareness of social/ethical impacts. This idea is supported by several recent conceptual and empirical works on AI literacy. For example, according to a review article on AI literacy, the term covers “knowledge, skills, and attitudes necessary to understand AI principles, effectively utilize and create AI tools, and critically evaluate AI technologies’ ethical and societal impacts.” Another conceptual framework outlines dimensions of AI literacy: understanding AI; applying AI; evaluating AI; creating with AI; and ethical considerations when using AI. These definitions emphasize that AI literacy involves not just familiarity but deep understanding and critical judgment. |
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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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Acceptability of AI in healthcare is influenced by factors such as trust in AI systems, integration with existing workflows, system understanding, and technology receptiveness. These factors affect whether healthcare professionals feel comfortable relying on AI, understand its recommendations, and can use it effectively within their daily practice. The color or aesthetic design of the AI hardware does not influence professional acceptance and is not mentioned in research studies as a determinant. |
The principle is that acceptability of technology in professional settings is primarily driven by cognitive, trust, and workflow integration factors rather than aesthetic or superficial features. Research on AI in healthcare consistently identifies trust in AI systems, understanding of the AI workflow, and the professional’s technology receptiveness as key determinants of adoption and use. Studies including Longoni et al., 2019, and more recent reviews on AI adoption in medical practice emphasize that factors like human-AI trust, workflow compatibility, and clarity of system recommendations are critical, while non-functional attributes like color or shape are irrelevant. |
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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 affects AI acceptability because healthcare professionals often look to peers, institutional norms, and leadership recommendations when deciding whether to adopt new technologies. Positive endorsements from respected colleagues, supervisors, or professional bodies can increase confidence in AI tools, whereas skepticism in the professional community can reduce adoption rates. This factor interacts with trust, workflow integration, and system understanding to determine overall willingness to use AI in clinical practice. |
The principle is drawn from the Technology Acceptance Model (TAM) and related social influence frameworks, which posit that adoption decisions are shaped not only by perceived usefulness and ease of use but also by social pressures, norms, and peer behavior. In healthcare AI research, social influence is shown to be a critical determinant of whether professionals integrate AI tools into practice. Studies and reviews highlight that peer recommendations, organizational culture, and leadership guidance directly affect AI acceptance. For example, the review article “Adoption of Artificial Intelligence in Healthcare: The Role of Social Influence” (ScienceDirect, 2023) emphasizes that social influence operates alongside trust, workflow compatibility, and system understanding to determine AI uptake. |
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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 perceived threat in healthcare AI arises when professionals worry that AI could replace certain tasks performed by clinicians, radiologists, or nurses, leading to job insecurity or reduced professional autonomy. While AI can improve efficiency and diagnostic accuracy, these concerns about professional displacement influence acceptance and adoption rates. This perception interacts with other factors like trust, understanding of AI, and workflow integration to shape whether healthcare staff are willing to incorporate AI tools. |
The conceptual basis comes from risk perception and technology adoption theory, which highlights that perceived threats such as fears of job displacement can negatively influence technology acceptance. In healthcare, studies indicate that concerns about AI replacing human roles are a primary barrier to adoption. This aligns with findings in the article “Adoption of Artificial Intelligence in Healthcare: The Role of Social Influence” (ScienceDirect, 2023), which notes that perceived threats interact with trust, workflow integration, and professional understanding to determine the willingness to use AI. In other words, understanding AI’s function and addressing fears of replacement are critical for successful integration. |
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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 focuses on creating systems that align with clinicians’ workflows, decision-making processes, and cognitive models. By integrating AI tools seamlessly into existing practices, ensuring interpretability, and providing decision-support rather than replacement, professionals are more likely to trust and adopt AI. Research shows that AI acceptability increases when systems are intuitive, transparent, and enhance rather than disrupt clinical work. This approach addresses concerns about autonomy, usability, and professional relevance, which are critical factors influencing adoption. |
The conceptual principle is that technology acceptance in professional healthcare is maximized when AI systems are designed with the end-user in mind, prioritizing workflow integration, interpretability, and human-AI collaboration. This is supported by Human-Centred Design (HCD) theory and research on AI adoption, which emphasizes that acceptability depends on perceived usefulness, ease of use, trust, and professional relevance. Specifically, the article “Adoption of Artificial Intelligence in Healthcare: The Role of Social Influence” (ScienceDirect, 2023) and other reviews highlight that human-centred design directly increases AI adoption by addressing workflow compatibility, usability, and trust, making clinicians more willing to use AI in practice. |
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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 AI tools fit into healthcare professionals’ daily practice. It includes the perceived usefulness or value proposition of the AI system, how well it integrates into existing workflows, and whether it streamlines tasks without adding unnecessary complexity. These factors directly affect the likelihood that professionals will adopt and consistently use AI in clinical settings. Systems that disrupt workflows or offer unclear value are less likely to be accepted, even if technically advanced. |
The principle is drawn from the Technology Acceptance Model (TAM) and Human-Centred Design (HCD) approaches, which posit that technology adoption depends on perceived usefulness and ease of integration into existing workflows. In healthcare AI, research shows that adoption is higher when systems clearly demonstrate value and seamlessly fit into clinical processes. The article “Adoption of Artificial Intelligence in Healthcare: The Role of Social Influence” (ScienceDirect, 2023) specifically notes that system usage factors—including workflow compatibility, task streamlining, and perceived benefit—are critical determinants of acceptability among medical professionals. |
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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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Ethical considerations influence whether healthcare professionals are willing to adopt AI. Professionals are more likely to accept AI systems that align with their professional values, uphold patient safety, ensure data privacy, and support equitable care. If AI is perceived as violating ethical norms—such as bias in decision-making, lack of transparency, or undermining patient trust—acceptability decreases. Ethical alignment interacts with trust, workflow integration, and perceived usefulness to determine overall adoption. |
The principle is that professional acceptability of AI is guided by ethical alignment, reflecting Technology Acceptance Model (TAM) extensions that incorporate ethical and social considerations. Healthcare professionals evaluate AI based on whether it supports ethical practice, maintains patient trust, and ensures fairness and safety. The article “Adoption of Artificial Intelligence in Healthcare: The Role of Social Influence” (ScienceDirect, 2023) emphasizes that ethicality is a key determinant influencing whether AI systems are accepted, interacting with trust, workflow integration, and perceived value to shape overall adoption. |
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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 future studies on AI acceptability should go beyond technical performance or economic considerations and focus on how AI tools are experienced and integrated by healthcare professionals. This includes examining workflow compatibility, usability, cognitive load, interpretability, and the overall interaction between humans and AI systems. Considering user experience and system integration allows researchers to identify practical barriers, user needs, and design improvements that increase real-world adoption. |
The conceptual principle is grounded in Human-Centred Design (HCD) and Technology Acceptance Model (TAM) extensions, which suggest that user experience, workflow integration, interpretability, and cognitive compatibility are crucial for adoption. Research on AI adoption in healthcare, including the article “Adoption of Artificial Intelligence in Healthcare: The Role of Social Influence” (ScienceDirect, 2023), emphasizes that methodological approaches must examine these human-technology interactions to generate actionable insights for system design and implementation. By prioritizing deep analysis of user experience and integration, studies can better predict and enhance real-world acceptability. |
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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, leading to motor dysfunction. Human embryonic stem cells (hESCs) have the potential to differentiate into dopaminergic neurons. The primary therapeutic goal is to transplant these cells to replace the lost neurons, restore dopamine levels, and improve motor function. While other benefits such as enhancing neurogenesis or cognitive function may occur indirectly, the direct objective is neuronal replacement. |
The principle is based on stem cell differentiation and cell replacement therapy concepts. Human embryonic stem cells are pluripotent, meaning they can generate any cell type, including dopaminergic neurons. This approach relies on the idea that replacing lost neurons can restore the functional circuitry disrupted in Parkinson’s disease. This concept is supported by studies such as Takahashi et al., 2022, Human embryonic stem cell-derived dopaminergic neurons for Parkinson’s disease therapy (ScienceDirect), which demonstrate functional improvement in preclinical and early clinical models following transplantation of hESC-derived dopaminergic neurons. |
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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, such as monkeys, are commonly used in preclinical testing of stem cell therapies for Parkinson’s disease because their brain anatomy and dopaminergic system closely resemble humans. Testing in monkeys allows researchers to evaluate both the safety (e.g., immune response, tumor formation) and efficacy (restoration of motor function) of the STEM-PD product in a model that closely mimics the human condition. Rodents, while useful for initial studies, do not fully replicate the complexity of the primate brain, making monkeys the preferred choice for late-stage preclinical studies. |
The principle is based on translational neuroscience and preclinical study design, which emphasize using animal models with neuroanatomical and functional similarity to humans for safety and efficacy testing. Non-human primates provide critical insights into motor control, dopaminergic neuron integration, and immune responses that are not fully replicable in rodents. Studies such as Kikuchi et al., 2017, Human embryonic stem cell-derived dopaminergic neuron transplantation in Parkinsonian monkeys (ScienceDirect) demonstrate that monkey models are essential for evaluating clinical potential and predicting outcomes in human patients. |
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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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The preclinical safety study in rats lasted 6 months to allow researchers to monitor both short-term and mid-term outcomes after stem cell transplantation. This duration is sufficient to assess initial safety, including potential tumor formation, immune response, cell survival, and early functional effects. Shorter studies may miss adverse events that appear after several weeks, while extremely long studies can be resource-intensive without adding significant safety data at this stage. |
The principle is based on preclinical translational research standards, which recommend monitoring animals for several months after stem cell transplantation to capture potential safety signals before progressing to primate studies or human trials. A 6-month period allows for evaluation of tumorigenicity, immunogenicity, and early functional outcomes. This aligns with studies like Kikuchi et al., 2017, Human embryonic stem cell-derived dopaminergic neuron transplantation in Parkinsonian monkeys (ScienceDirect), which follow staged preclinical timelines, starting with rodents before moving to primates, ensuring safety and efficacy data are collected systematically. |
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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 STEM-PD clinical trial is described as a Phase I/IIa study, which combines the objectives of early safety assessment (Phase I) with preliminary efficacy evaluation (Phase IIa). In this design, researchers first assess tolerability, adverse events, and dosing safety, and then collect initial data on functional improvements, such as motor recovery in Parkinson’s patients. This hybrid approach allows a more efficient transition from safety testing to early indications of effectiveness, particularly for complex interventions like stem cell transplantation. |
The principle is based on standard clinical trial progression in translational medicine, where Phase I focuses on safety and tolerability, while Phase IIa begins exploratory efficacy assessment. Combining these phases is common in regenerative medicine and complex interventions to streamline data collection while ensuring patient safety. The article “STEM-PD: Human embryonic stem cell therapy for Parkinson’s disease” (ScienceDirect, 2023) explicitly mentions the Phase I/IIa design, reflecting both safety monitoring and preliminary functional outcome evaluation in a controlled clinical setting. |
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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 GMP-compliant conditions to ensure quality, reproducibility, and safety of the stem cell-derived dopaminergic neurons. GMP standards regulate the production environment, materials, processes, and documentation, minimizing the risk of contamination and ensuring consistent potency. Manufacturing under these strict conditions is essential before clinical application to protect patients and meet regulatory requirements. Random integration or non-compliant production methods would increase variability and safety risks. |
The principle is based on Good Manufacturing Practice (GMP) regulations, which are internationally recognized standards for producing clinical-grade cell therapies. GMP ensures reproducibility, sterility, potency, and traceability, critical for patient safety and regulatory approval. According to the article “STEM-PD: Human embryonic stem cell therapy for Parkinson’s disease” (ScienceDirect, 2023), the product is specifically manufactured under GMP-compliant conditions to maintain quality and safety for clinical trials, consistent with best practices in regenerative medicine and stem cell-based 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 preclinical safety study in rats demonstrated that transplantation of STEM-PD cells did not induce adverse effects, tumor formation, or ectopic cell growth outside the brain. Monitoring for several months allowed researchers to evaluate both immediate and mid-term safety signals, ensuring that the stem cell-derived dopaminergic neurons were stable, non-tumorigenic, and well-tolerated. This confirmation is critical before advancing to non-human primate studies and human clinical trials, as any safety concerns must be ruled out early. |
The principle is based on preclinical translational research protocols, which require comprehensive monitoring of animals for adverse effects, tumorigenicity, and biodistribution following stem cell transplantation. Ensuring safety in rodent models is a standard prerequisite before moving to higher-order animals and clinical trials. The article “STEM-PD: Human embryonic stem cell therapy for Parkinson’s disease” (ScienceDirect, 2023) confirms that no adverse effects or tumor formation were observed in rats, supporting the product’s safety and justifying subsequent testing in non-human primates. |
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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 efficacy study demonstrated that STEM-PD cells, once transplanted into the striatum of Parkinsonian rats, survived, differentiated into dopaminergic neurons, and integrated functionally into the host neural circuits. This resulted in measurable improvements in motor function, indicating that the stem cell-derived neurons could restore dopamine signaling disrupted by Parkinsonian lesions. These findings support the therapeutic potential of STEM-PD and provide preclinical evidence for progressing to non-human primate studies. |
The principle is grounded in regenerative medicine and neural transplantation theory, which posits that replacing lost or damaged neurons can restore disrupted neural circuits and behavioral function. Preclinical rodent models allow evaluation of survival, differentiation, integration, and functional recovery. According to the article “STEM-PD: Human embryonic stem cell therapy for Parkinson’s disease” (ScienceDirect, 2023), transplanted cells in rats not only survived but also reversed motor deficits, demonstrating proof-of-concept efficacy that justifies progression to primate and human studies. |
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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 specifically expressed in midbrain dopaminergic progenitors. Assessing their presence ensures that the STEM-PD batch predominantly contains cells committed to the dopaminergic lineage, minimizing contamination with undesired cell types. Purity verification is crucial for safety and efficacy, as undifferentiated or off-target cells could form tumors or fail to restore dopaminergic function. This step is a standard quality control measure in stem cell manufacturing before clinical application. |
The principle is based on stem cell differentiation and quality control standards in regenerative medicine. LMX1A and EN1 are well-established markers for midbrain dopaminergic lineage commitment. Ensuring a high percentage of these markers confirms batch purity and reduces safety risks. According to the article “STEM-PD: Human embryonic stem cell therapy for Parkinson’s disease” (ScienceDirect, 2023), these markers were specifically used to validate the identity and differentiation status of the stem cell product prior to preclinical and clinical use, consistent with best practices in stem cell manufacturing. |
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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 (Fibroblast Growth Factor 8b) and SHH (Sonic Hedgehog) are critical morphogens in the differentiation of human embryonic stem cells into midbrain dopaminergic neurons. They provide spatial and temporal cues that guide pluripotent stem cells to adopt specific neural fates, ensuring the cells develop into the desired dopaminergic lineage rather than other neural or non-neural cell types. Proper use of these growth factors during manufacturing increases the purity, functionality, and safety of the STEM-PD product for preclinical and clinical applications.
FGF8b (Fibroblast Growth Factor 8b) และ SHH (Sonic Hedgehog) |
The principle is based on developmental biology and stem cell differentiation protocols. FGF8b and SHH act as morphogens that establish positional identity and guide pluripotent cells toward midbrain dopaminergic neuron fate. Proper patterning is essential to achieve functional and safe cell populations for transplantation. The article “STEM-PD: Human embryonic stem cell therapy for Parkinson’s disease” (ScienceDirect, 2023) describes the use of FGF8b and SHH in the differentiation protocol to reliably generate dopaminergic neurons with high purity, supporting both preclinical efficacy and safety studies. |
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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 preclinical efficacy trials for Parkinson’s disease, the primary goal is to determine whether transplanted stem cell-derived dopaminergic neurons can restore lost motor function. Rats with Parkinsonian lesions typically show motor deficits such as impaired limb use, decreased coordination, and slower movement. Measuring recovery of motor function provides a direct indicator of therapeutic effect, demonstrating that the transplanted cells survive, integrate, and restore dopamine signaling in the brain. Other outcomes like anxiety, cognition, or lifespan are secondary, whereas motor recovery is the most relevant for Parkinson’s therapy. |
The principle is grounded in translational neuroscience and Parkinson’s disease pathophysiology. Motor deficits in rodent Parkinsonian models reflect the loss of dopaminergic neurons in the substantia nigra, and recovery of motor function indicates functional integration of transplanted neurons. According to Kikuchi et al., 2017 and the article “STEM-PD: Human embryonic stem cell therapy for Parkinson’s disease” (ScienceDirect, 2023), improvement in motor behavior in rats is the primary measure of efficacy, confirming that stem cell-derived dopaminergic neurons can restore neural circuits and functional outcomes relevant to human disease. |
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