#article
parkinson disease and AI

🔹Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis
and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease.

🔹This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support.

🔹 AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions.

🔹AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use.

🔸provider: Dr Hengameh Yousefi
Reference

#AI_in_medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation
🆔Linkedin
neuro-surgery.info

Why Our Searches Fail: Key Gaps in Research Strategies

🔹 1. Lack of Clear Focus
Without a clear research question or goal, searches become unfocused and ineffective.

🔹 2. Vague or Too Broad Keywords
Choosing precise keywords by brainstorming synonyms, checking article keywords, and consulting experts is essential. Avoid slang or overly technical terms without domain knowledge.

🔹 3. Not Knowing Database Differences
Google Scholar, PubMed, and Scopus work differently. Knowing their scope and search tools improves results (White, 2020; Gray et al., 2012).

🔹 4. Missing Boolean Operators and Search Planning
Using AND, OR, NOT, and having a search plan enhances search accuracy.

🔹 5. Expecting Instant Results
Searching is iterative; refining queries multiple times is normal.


🔹 Conclusion:
Understanding these gaps leads to better, more efficient searches and stronger research outcomes.


References

Source 1
Source 2
Source 3

Provider:Dr. Fereshteh Faal

🆔 @Neurosurgery_association
🆔 @Neuroresearchers
neuro-surgery.info

AI in Blood Loss Estimation and Hemorrhage Management

• Deep Learning for Gauze Analysis:

AI estimates blood loss by analyzing blood-soaked gauze in its natural state, eliminating manual processing and improving accuracy in surgeries like laparoscopy.

• Liposuction Prediction:

In large-volume liposuction, AI predicts blood loss with 94.1% accuracy, aiding preoperative planning and risk reduction.

• Real-Time Surgical Monitoring:

Platforms like Gauss Triton provide real-time EBL during surgery, improving early detection of hemorrhage, including postpartum cases.

• Trauma and Emergency AI Tools:

Systems like AI-TRiPS assess bleeding risk in trauma patients, enabling faster, life-saving decisions.

• Imaging-Based Detection:

AI applied to CT scans predicts hematoma and hemorrhage with 78–87% sensitivity and up to 85% specificity.

• Future Outlook:

Multimodal AI promises better hemorrhage monitoring, reducing morbidity and mortality across patient populations.

Provider: Fatemeh Noorani

Sources:
Source 1 | Source 2 | Source 3 | Source 4 | Source 5 | Source 6 | Source 7 | Source 8 | Source 9 | Source 10

#AI_in_Medicine
#AI_in_Surgery
#Blood_Loss_Estimation
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

✅Title:Managing large numbers of studies

🔴Why is it important?
Systematic reviews and meta-analyses play a key role in clinical decision-making. However, the large volume of studies makes analysis challenging.

🔴Useful Tools:

🔸️Rayyan, Covidence, DistillerSR
🔸️Artificial Intelligence (LLMs) for smart screening
🔸️Statistical analysis software: RevMan, R, CMA, Stata

🔴Key Steps in Managing Studies:

🔸️Defining inclusion and exclusion criteria
🔸️Double and pilot screening
🔸️Data extraction and designing questions using PICO format
🔸️Assessing heterogeneity and bias analysis

🔷️Conclusion
Accurate management of studies saves time, reduces errors, and increases the credibility of research results. Modern tools empower

📚references:

Source 1
Source 2
Source 3
Source 4

✅Aida Mahmoudjanloo

🆔 @Neurosurgery_association
🆔 @NeuroResearchers
🌐 neuro-surgery.info

#Resolving_disagreements_in_screening

❓ 1. What is the definition of screening and resolving disagreements?

Screening is when reviewers check noscripts and abstracts of articles based on inclusion and exclusion criteria. If they disagree, they discuss or ask a third reviewer to decide.[1]


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❓ 2. What is the purpose of this method?

This helps prevent irrelevant articles from entering the review. It improves accuracy and makes the results more reliable since decisions are checked by more than one person.[2]


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❓ 3. What are the problems and contradictions in this method?

Reviewers may interpret criteria differently or make mistakes due to fatigue. Also, not clearly recording reasons for exclusion can lower the quality and trust in results.[3]


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❓ 4. Can you give some examples?

For example, using Covidence software for double screening, checking reviewer agreement with Cohen’s kappa, and writing clear reasons for exclusion in a step-by-step order, like in WHO or Cochrane reviews.[4]

REFERENCES:

1/2/3/4

Provider: Dr. Kiyana Kiyanpour

🆔 @Neurosurgery_association
🆔@Neuroresearchers
neuro-surgery.info

🧠 Advancing Sleep Disorder Diagnosis with AI

❓ How AI Enhances Sleep Apnea Detection
Artificial intelligence revolutionizes sleep medicine, offering a faster, less invasive alternative to polysomnography. Using biosignals like nasal airflow, SpO₂, and ECG, AI detects obstructive sleep apnea (OSA) and hypopnea with clinical precision. A deep learning model transforms 1D signals into 2D scalograms, achieving 94% accuracy in event detection, 99% in OSA screening, and 93% in severity grading. It also improves hypopnea detection by integrating SpO₂ and ECG data. Wearable AI devices show 87% accuracy for remote sleep apnea monitoring, ideal for underserved populations.

🧠 How AI Interprets Sleep Physiology
AI mimics expert analysis, converting signals into scalograms for convolutional neural networks to extract breathing patterns. Machine learning models achieve >97% accuracy in classifying OSA and insomnia. Neural networks generate hypnodensity graphs, enhancing narcolepsy diagnosis.

🩺 Clinical Implications
AI reduces diagnosis time, enables home-based screening, and guides personalized treatments like CPAP or surgery.

🔗 References: 1, 2, 3, 4, 5

Provider: Dr. Farnoosh Vosough

#AI_in_Medicine
#AI_in_Surgery
#Blood_Loss_Estimation
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

📌 Topic: Visual Abstract for Review Articles

♦️ Introduction:
A Visual Abstract is, simply put, a graphical summary of the information presented in a paper’s abstract. These abstracts leverage the human brain’s innate ability to quickly process and retain visual information.

Visual abstracts—also known as Graphical Abstracts (GA)—have become powerful tools for effectively communicating complex ideas in a simplified and concise manner. Their use is increasingly expanding, especially on social media platforms.

⸻
🎯 Objectives of a Visual Abstract:
• To simplify methodologies and summarize key findings for quick and effective comprehension
• To enhance interpretation, retention, and shareability of research content
• To highlight the core message of the article by removing non-essential details

⸻
🔹 Main Components of a Visual Abstract:
• A noscript that reflects the central research question or objective
• A summary of key results, including comparisons, data, and visual representations
• Names of the authors and the visual abstract designer
• A three-panel layout using clear, simple, and monochromatic icons for improved clarity

⸻
🔹 Types of Graphical Abstracts:
1. Conceptual Diagrams
2. Flowcharts
3. Infographics
4. Iconographic Abstracts
5. Photographic Visuals

⸻
🔷 Steps to Design an Effective Visual Abstract:
1. Identify the core message of the article
2. Select an appropriate visual style for the study type
3. Create a simple, clear, and understandable graphic layout
4. Use color and design elements thoughtfully
5. Seek feedback to refine and finalize the design

⸻
🔷 Benefits of Using Visual Abstracts:
• Assisting in early decision-making about whether to read the full article
• Increasing article visibility on social media and attracting a broader audience, especially those with limited time or scientific background
• Facilitating discussion and knowledge exchange around the research findings
• Enhancing citation rates and research impact, as supported by various studies.

⸻
⚠️ Challenges and Limitations:
• Risk of oversimplification and omission of important information
• Possibility of misinterpretation or incomplete message transmission
• Requirement of graphic design skills alongside scientific understanding

⸻
🔸 Conclusion:
Just like textual abstracts cannot replace the full article, visual abstracts are not intended to serve as substitutes either. They offer a snapshot of the study’s key points to help the reader decide whether the full article is worth reading and assist in the understanding of essential concepts.

The format of visual abstracts is still evolving, which leaves room for creativity and flexibility in their design.

Among the useful tools, genetic algorithms can be considered valuable for conveying complex data in a clear and concise visual form. These algorithms have the potential to make scientific content more accessible and engaging to a wider audience. In today’s media-driven world, mastering such tools has become a kind of creative skill—one that promises a progressive future for academic publishing despite certain limitations.

⸻

📚 References:
📎 Source 1
📎 Source 2

Provider : Parmida Mafakhery
🆔 @Neurosurgery_association
🆔@Neuroresearchers
neuro-surgery.info

🧠Surgical Planning Assistance Using Neural AI

🔹Neural artificial intelligence is rapidly transforming the way surgical planning is performed. By analyzing large volumes of medical images (such as CT and MRI scans), neural networks can help surgeons visualize anatomical structures more clearly and predict potential complications before the operation.

🔹For instance, convolutional neural networks (CNNs) can automatically segment organs and tumors, creating accurate 3D models that assist in pre-operative decision-making. These tools reduce the risk of human error and improve surgical precision, especially in complex procedures like neurosurgery or oncology.

🔹Furthermore, AI can simulate different surgical scenarios, allowing surgeons to choose the safest and most effective approach. While these technologies are not meant to replace human expertise, they act as intelligent assistants that enhance surgical outcomes and patient safety.

Source

Provider: Azin Dolati

#AI_in_Medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

🧠 How to Respond to Reviewer Comments

A Professional Guide for Researchers

Responding to reviewers is a key and often critical point of the research publication process. Acquiring this skill will further increase the probability of getting accepted, and it reflects the author’s scientific maturity and knowledge in the area of the proposal topic; The following tenets are suggested to be incorporated in composing effective and professional response to reviewers:

1⃣ Employ a Professional and Respectful Tone:

Always answer politely and professionally, even when you think the reviewer's remark is not correct or is unfair. Never use aggressive, sarcastic, or defensive tone. Your tone must be calm, neutral, and polite throughout.

2⃣ Respond to Each Comment Individually:

Address each reviewer comment separately. Start with quoting the reviewer's comment, followed by your specific and detailed response. Wherever relevant, specify the precise location of the changes made in the revised manunoscript (page, paragraph, or line numbers) to make the review process easier.

3⃣ Acknowledge Valid Criticisms and Express Gratitude:

If the point of the reviewer is legitimate, acknowledge it simply and thank the reviewer for their comment. Describe how the manunoscript has been changed in light of the remark. This strategy shows humbleness and openness to constructive criticism.

4⃣ Politely Disagree and Offer Evidence:

If you don't agree with a comment, disagree respectfully and back up your own position with rational argument and scientific citations. Do not use aggressive language. A reasoned disagreement is fine and indicates confidence in your scientific position.

5⃣ Maintain Clarity and Accuracy:

Be clear, concise, and specific in your responses, addressing directly the reviewer's issue. Do not make vague references to "corrected" or "addressed." Give sufficient detail to indicate the nature and extent of revisions.

6⃣ Never Ignore a Comment:

Never dismiss a reviewer remark—even if it is minor or inconsequential. Address each point made, showing that you have treated the review process seriously and exhaustively.

7⃣ Provide Technical and Linguistic Accuracy:

The letter of response must be crafted as cautiously and precisely as the manunoscript itself. Disorganized structure, grammatical mistakes, or sloppy writing can cause misunderstandings or invalidate you as a researcher.

8⃣ Treat the Response as a Scientific Dialogue:

Replying to reviewers is not an administrative formality; it is a scientific discussion among you and the scientific community. Use this to make your paper better and respond positively to expert criticism.

9⃣ Adhere to the Three Fundamental Principles:

• Precision – Use clear and unambiguous language.
• Respect – Involves maintaining a decorous and academic attitude.
• Evidence – Back up your arguments with literature and data.

A professional response to reviewers' comments is a critical component of successful academic publishing. It is both a skill and a reflection of your research character. Early-career researchers are encouraged to rehearse the skill, learn from exemplar responses, and follow credible guidelines to become more proficient in peer communication.

Provider: Dr. Roozbeh Asadi

References: [1] / [2]

🆔 @Neurosurgery_association
🆔@Neuroresearchers
neuro-surgery.info

🧠Instrument Tracking Systems in neurosurgery, enhanced by the power of artificial intelligence

🔸These systems serve as the “third eye” of neurosurgeons in the operating room. They track surgical instruments in real time and, using advanced AI algorithms, determine their position relative to sensitive brain structures.

🔹By combining infrared cameras with computer vision techniques (e.g., YOLO) and deep learning models (such as CNNs), the system continuously identifies instrument locations. This data is then registered and aligned with the patient’s MRI or CT scans, allowing accurate localization within a 3D brain model.

🔹High accuracy and processing speed (less than 50 milliseconds) are essential for safe real-time performance during surgery.

🔻Key applications:

✅ Precision Surgery: Enables delicate operations in areas like the brainstem and basal ganglia
✅ Real-time Feedback: Immediate correction of tool paths to prevent tissue damage
✅ Data Collection for AI Training: Surgeries generate valuable datasets for training future algorithms
✅ Predictive Assistance: AI can anticipate human error and alert accordingly

🔻Real-world examples:
• Brainlab Curve Navigation
• Medtronic StealthStation S8
• Ongoing research at institutions like MIT and ETH Zurich is focused on developing even more precise AI tracking models.

Provider: Mahdis Abdollah beigi

Refrence: 1, 2

#AI_in_Medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

Ethics of AI in Healthcare: Navigating Privacy, Bias, and Trust in 2025

🔹AI is revolutionizing diagnosis and treatment but raises serious ethical concerns like privacy, bias, and patient trust.

🔹Privacy: Encryption and data anonymization are essential to protect sensitive patient information.

🔹Algorithmic Bias: Non-representative data can lead to healthcare inequality; diverse and fair data collection is crucial.

🔹Trust & Transparency: Explaining how AI works and training healthcare providers are key to building trust.

🔹Regulations & Standards: Frameworks like the FDA and EU’s AI Act exist, but more focus on clinical effectiveness and safety is needed.

🔹Collaboration between governments, companies, and clinicians is the way to develop ethical and effective AI in healthcare.

Provider: Sara Atash beig
Refrence

#AI_in_Medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

Post-Operative Recovery Tracking in Cranial Tumor Resection

🔹Post-operative recovery tracking in neurosurgery for cranial tumor resection monitors patient progress to optimize outcomes, reduce complications, shorten hospital stays, and improve quality of life.

🔻Key methods include:
• Pain Assessment: The Visual Analog Scale (VAS, 0-10) tracks pain to guide analgesia (e.g., paracetamol), minimizing opioid use.

• Neurological Monitoring: The Glasgow Coma Scale (GCS, 3-15) assesses consciousness, detecting issues like edema. Exams evaluate motor function and cognition.

• Patient-Reported Outcomes (PROMs): Apps or questionnaires capture symptoms (e.g., headaches), enabling early intervention for seizures or other issues.

• Vital Signs: Monitoring heart rate, blood pressure, and oxygen levels helps manage postoperative nausea/vomiting and respiratory risks.

• Mobility: Tracking early mobilization reduces risks like venous thromboembolism.

🔸ERAS protocols, recently adapted for neurosurgery like cranial tumor resection, focus on pre-, intra-, and postoperative care. They include patient education, pain management, neurological assessment, and patient-reported outcomes to reduce hospital stays, complications, and costs while improving quality of life and functional independence.

Though successful in general and spine surgery, brain tumor patients require specific refinements for broader ERAS implementation in neuro-oncology.

Provider: Negar Azami

Refrence

#AI_in_Medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

The Role of Artificial Intelligence in Vital Sign Monitoring

🔹Artificial intelligence (AI) has recently emerged as an innovative and transformative tool in the continuous monitoring of vital signs, particularly in neurosurgical settings. Using machine learning and deep learning algorithms, it enables the analysis of complex physiological data such as heart rate, blood pressure, respiratory rate, oxygen saturation, and even brain wave patterns.

🔹These systems can detect subtle abnormalities before clinical symptoms appear and provide early warnings for timely interventions. In patients with traumatic brain injuries or undergoing complex brain surgeries, AI can play a critical role in preventing elevated intracranial pressure, sudden hypotension, or the onset of sepsis.

🔹Furthermore, by reducing reliance on manual monitoring and minimizing human error, it enhances the speed and precision of clinical decisions. Studies have shown that the integration of AI in critical care settings significantly improves patient outcomes and reduces mortality in neurosurgical patients.

Provider: Hesan Abbasi

References: 1, 2

#AI_in_Medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

💠 Beyond Single Studies: Meta-synthesis of Qualitative Studies

🔹 In qualitative research, each study often reveals only a piece of the larger picture. To build a more comprehensive and deeper understanding, researchers turn to meta-synthesis – a structured method for synthesizing qualitative findings.

🔹 What is Meta-synthesis?

✅ A type of systematic review that analyzes and integrates findings from qualitative studies
✅ Goes beyond mere summarization – aims to develop theories, extend concepts, and generate new knowledge
✅ Unlike quantitative meta-analysis, meta-synthesis prioritizes meaning and interpretation of data

📌 When is meta-synthesis useful?

🔸 When multiple qualitative studies exist on a human experience (e.g., illness, identity)
🔸 To generate a holistic perspective and build theory from diverse findings
🔸 When deeper insight into lived experiences is the research goal

🛠 Key Steps in Meta-synthesis:

1️⃣ Define the research question – focused but flexible
2️⃣ Conduct a systematic search – including qualitative and grey literature sources
3️⃣ Screen and select relevant studies
4️⃣ Assess study quality (using tools like CASP or QARI)
5️⃣ Extract and code data (themes, concepts, quotes)
6️⃣ Synthesize findings – develop new interpretations or theories
7️⃣ Present results – clearly, visually, and grounded in data

🧠 What approaches exist?

📍 Meta-ethnography – interpreting and translating concepts across studies
📍 Grounded Theory synthesis – building theory through conceptual comparison
📍 Meta-study – integrating themes to build a new theoretical framework

⚖️ Advantages:

🔹 Enhances conceptual understanding of complex issues
🔹 Increases generalizability and applicability of findings
🔹 Identifies research gaps
🔹 Informs policy, clinical practice, and education

⚠️ Challenges:

🔸 Risk of decontextualizing or oversimplifying findings
🔸 Difficulty maintaining richness and depth of original data
🔸 Blurred boundaries in open-ended, complex topics

🖋 Provided by: Dr. Mohammad Mahdi Ghorbani

📚 Suggested Reading:

🔗 [Source link]

🔍 Join us at the Neurosurgery & Neuroscience Research Association

🆔@Neurosurgery_association
🆔@Neuroresearchers
neuro-surgery.info

Rapid review techniques:

Rapid reviews are an accelerated method of synthesizing scientific evidence within a limited timeframe, commonly used in fields like education, healthcare, and policymaking. Unlike traditional systematic reviews, rapid reviews streamline steps such as searching, screening, and analysis to respond quickly to urgent questions. This often involves limiting the number of databases searched, using a single reviewer for study selection, and focusing on key studies.
Popular techniques include Rapid Evidence Assessment, Scoping Review, and Rapid Realist Review, each chosen based on the review’s purpose, available evidence, and team expertise. Despite the time-saving nature of rapid reviews, maintaining transparency and rigor is essential. Applying frameworks like PRISMA and tailoring outputs to the intended audience help ensure credibility.
Rapid reviews are powerful tools for evidence-informed decision-making, especially when time and resources are constrained. However, researchers must acknowledge their limitations to avoid misinterpretation of findings.

🔍 Key Differences Between Rapid and Systematic Reviews in Education:

- Scope and Purpose: Rapid reviews are designed to answer specific questions within a short timeframe, while systematic reviews aim for comprehensive coverage of a broader topic.

- Screening Process: Rapid reviews may involve single-person screening or automated tools like *text mining*, whereas systematic reviews typically require dual independent screening to minimize bias.

- Quality Assessment: Rapid reviews often use simplified or selective quality appraisal methods. Systematic reviews, on the other hand, apply standardized tools such as *GRADE* to assess study quality rigorously.

- Search Strategy: Rapid reviews may limit their search to core databases (e.g., PubMed, ERIC), while systematic reviews include extensive searches across grey literature, dissertations, and conference papers.

- Audience and Output: Rapid reviews are tailored for decision-makers and policymakers, with concise and accessible formats. Systematic reviews are more detailed and academic, often targeting researchers and scholars.

📘 Interesting Note: According to Hartling et al. (2015) from the AHRQ, over 30 types of rapid reviews have been identified, each adapted to the needs of different organizations. This shows that rapid reviews aren't one-size-fits-all—they must be customized based on context and purpose.

Provider:Zeinab Ranjbarian

Refrences:
Ref1/Ref2/Ref3/Ref4

🆔 @Neurosurgery_association
🆔@Neuroresearchers
neuro-surgery.info

🟣 Living Systematic Review (LSR)

✅ Definition
A Living Systematic Review (LSR) is an innovative approach to synthesizing scientific evidence, designed to continuously update the results of a systematic review as new studies become available. Unlike traditional reviews that are published once and remain static, LSRs operate dynamically and are perpetually updated.

🎯 Why LSR is Essential
- Rapid generation of new scientific evidence
- Timely response to crises such as pandemics
- Bridging the gap between evidence generation and clinical application

⚙️ Steps of Conducting an LSR
1. Initial review design with a future-oriented approach
2. Continuous monitoring of scientific databases (e.g., PubMed)
3. Automated screening of new studies using machine learning tools
4. Quality assessment and integration of new evidence
5. Regular publication of updated versions

📊 Advantages & Challenges
Advantages:
- Improved accuracy in clinical decision-making
- Scientific longevity of the review
- Enhanced quality of clinical guidelines

Challenges:
- Need for sustainable human and financial resources
- Complexity in publication scheduling
- Lack of global standards

📌 Conclusion
LSRs are a strategic and vital tool for evolving scientific disciplines, particularly clinical medical sciences. By providing up-to-date data, they significantly enhance the quality of therapeutic decision-making and health policy development.

📚 Scientific References:
Source 1
Source 2
Source 3

🧠 provider: Mohammad Reza Konarizadeh

🆔 @Neurosurgery_association
🆔@Neuroresearchers
neuro-surgery.info

Methodological  Reviews

⭕️ Definition:

Methodological reviews are a type of scientific review that focus not on research results, but on the evaluation and analysis of research methods used in studies. Unlike traditional systematic reviews, which primarily summarize evidence related to intervention effectiveness, methodological reviews aim to identify, assess, and synthesize information about how research is conducted. These reviews provide valuable insights into the quality, appropriateness, and consistency of research methods within a given field.[1]

⭕️ Importance of Methodological Reviews:

Methodological reviews play a critical role in highlighting the strengths and limitations of study designs and research methods employed in a field. They identify methodological gaps and inconsistencies that may affect the reliability and validity of the evidence. Consequently, these reviews are instrumental in guiding researchers and policymakers to improve study design, data collection, and analysis strategies in future research.[2]

⭕️ Types of Methodological Reviews:

Methodological reviews encompass various types, each designed for specific objectives and contexts:[3]

1⃣ Systematic Methodological Reviews: Provide comprehensive syntheses of research methods.

2⃣ Scoping Reviews: Map the breadth of methods employed across studies.

3⃣ Critical Reviews: Offer evaluative perspectives on research methods.

4⃣ Meta-Methodological Reviews: Analyze methodological trends across multiple disciplines.

Understanding these distinctions helps researchers select the appropriate review type based on their aims.

⭕️ Steps to Conduct Methodological Reviews:

Conducting a methodological review requires a systematic approach involving the following stages:[4]

1.Define a clear research question focused on the methods under investigation.

2.Perform a comprehensive literature search to identify studies addressing those methods.

3.Select studies based on inclusion and exclusion criteria related to methodological features.

4.Extract data emphasizing study design characteristics, data collection, and analysis methods.

5.Synthesize findings to reveal patterns, strengths, methodological gaps, and propose recommendations for future research.

⭕️ Applications and Significance of Methodological Reviews:

Methodological reviews play a pivotal role in developing research standards and enhancing the overall quality of scientific evidence. They assist researchers and decision-makers in choosing appropriate methods and understanding innovations in research design, ultimately leading to more valid and reliable scientific findings.[5]

✅ Summary:

Methodological reviews serve as essential tools in scientific research by focusing on evaluating the quality and effectiveness of research methods across various studies. They help identify methodological strengths and weaknesses, pinpoint research gaps, and offer strategies for improving future study designs. Different types of methodological reviews exist and are selected based on research goals and contexts. Their execution follows systematic steps, and ultimately, methodological reviews have a key role in advancing scientific standards and increasing the credibility and trustworthiness of research outcomes.[1-5]

Compiled by: Somayeh Golmohammadi

Refrence:
[1]
[2]
[3]
[4]
[5]

🆔 @Neurosurgery_association
🆔@Neuroresearchers
neuro-surgery.info

🔷️ AI Predicts Pituitary Tumor Treatment Outcomes

▫️Revolutionizing Care at MayoClinic

- Using the MAPER registry with over 1,200 patients, Mayo Clinic researchers developed accurate machine learning models to predict surgical outcomes for pituitary tumors. The models achieved 91% accuracy for Cushing disease and 81% for acromegaly.

▫️Key Predictive Factors

- Tumor size, patient age, BMI, and Knosp-Steiner grade were critical for outcome prediction. Smaller tumors and complete resections significantly increased the chances of intervention-free recovery.

▫️Next Step: Fully Automated Clinical AI

- The team is now building an AI-powered system to automatically extract clinical and radiomic data in real time, improving decision-making and strengthening Mayo Clinic’s role in precision neuro-oncology.

🔹️Reference: Mayoclinic

🔹Provider: Golshan Moradi

#AI_in_Medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation

Patients’ Preferences on Informed Consent in the Use of Medical Artificial Intelligence

This study, noscriptd “Patient Perspectives on Informed Consentfor the Use of Medical Artificial Intelligence,” empirically
examines patients’ preferences regarding disclosure when AI tools are used formedical diagnosis. Through a survey-based experiment conducted in South Korea
with 1,000 participants, the findings revealed that patients consideredinformation about AI usage highly important, even more so than certain standard
details typically included in traditional medical consent forms. Demographicfactors such as gender, age, and income
influenced the degree of importance patients placed on this information.Contrary to initial assumptions, the widespread use of AI or its superior
performance compared to human physicians did not significantly reduce patients’demand for disclosure. The study recommends that informed consent processes
involving AI should be personalized and go beyond mere legalrequirements to maintain patient trust and support informed
decision-making.

Source

Compiled by: Mohadese Ahmadloo

#AI_in_Medicine
#neuro_AI
#ArtificialIntelligence

🆔@Neurosurgery_association
🆔@Neurosurgeryassociation