Articles Flashcards
(25 cards)
What can AI technologies potentially detect regarding mental health?
Mental disorders through behavior and language
AI technologies offer benefits for early intervention.
What are the barriers to the application of AI in mental health diagnostics?
Persistent stigma and risk of false positives
These factors hinder the effective use of AI technologies.
What is crucial to protect patient autonomy in AI mental health applications?
Ensuring transparency and addressing potential biases
This is essential for ethical AI use.
What is the impact of online personal data accessibility on AI mental health applications?
It necessitates regulations to prevent misuse and combat discrimination
Misuse of AI-derived mental health information is a concern.
What does the traditional view state about psychiatric illness?
It is inherently private
Individuals need to share thoughts and feelings for assessment.
What are observable indicators of mental illness?
- Non-verbal communication: Appearance/behaviour
- Verbal communication: Speech expressing feelings/emotions
These indicators require further assessment for a formal diagnosis.
What does social media reveal about individuals relevant to mental health assessment?
Behaviors, speech patterns, moods, and intimate feelings
Private data is now publicly available.
What are the potential advantages of using AI in mental health?
- Increased access to care
- Prevention and early intervention
- Enhanced clinical practice
These advantages can lead to better diagnosis, monitoring, and prognosis.
What is courtesy stigma?
When stigma extends to those around the mentally ill, leading to social isolation
This stigma affects not only individuals with mental disorders but also their families and friends.
What does ARMS stand for in the context of mental health?
At-Risk Mental State
ARMS is an early detection paradigm criticized for a high false-positive rate.
What ethical dilemmas does AI present in mental health?
- Beneficence: Early detection offers potential intervention
- Nonmaleficence: Diagnostic labels can increase self-stigma
- Autonomy: Paternalistic nature of diagnosing without addressing stigma
These dilemmas highlight the balance between benefits and risks.
What are black box models in AI?
Machine learning models whose operations are not completely observable
They may carry hidden biases.
What is the challenge of explainability in AI?
Understanding the reasons behind a model’s output
It is crucial for ensuring trust in AI decisions.
What is defensive medical practice?
When physicians agree with a machine/model to avoid liability due to lack of understanding
This can lead to misinterpretation of AI recommendations.
What are the main issues with ClinicalTrials.gov?
- Inconsistent definitions
- Inconsistent intervention labelling
- Lack of results posting
These issues undermine the site’s reliability.
What does the Declaration of Helsinki lack clarity on?
Who is responsible for ensuring post-trial access in low/middle-income countries
This vagueness puts vulnerable patients at risk.
What is neuroarchitecture?
Embracing the needs of individuals with neurologic and cognitive challenges
It highlights the relationality between environment and mental health.
What are some adverse effects of environmental factors on health?
- Extreme climate change
- Contaminants/pollution
- Pesticides/glyphosate
- Mining/fracking
These factors can negatively impact brain health.
What role does traditional ecological knowledge play in research?
It situates research in the context of people, spirituality, and wellness
It balances community desires with the risk of increased stigma.
What are common marketing claims of consumer EEG devices?
- Enhancing concentration
- Relaxation
- Meditation
- Sleep improvement
Some claims also include treating ADHD and chronic pain.
What is neurofeedback?
When individuals attempt to self-regulate their brainwaves to alter behavior
It is a key concept in consumer EEG applications.
What are the potential validation approaches for consumer EEG?
- Simultaneous recording with research-grade EEG
- Comparing recordings at different times
- Running standard EEG protocols
These approaches help assess the validity of consumer EEG devices.
What ethical concerns arise from diagnostic claims of consumer EEG?
Misleading consumers about brain signals correlating with mental states
This violates the principle of nonmaleficence.
What is the main recommendation for addressing racial gaps in neuroimaging research?
Acknowledge who did not meet exclusion criteria and why
This can help improve inclusivity in research.
