Artificial Intelligence in Neurosurgery, Neurology, and Neurotechnology (2)

1.

SURVEY PAPER PRESENTATION
Artificial Intelligence in Neurosurgery,
Neurology, and Neurotechnology
Applications, Challenges, and Future Directions
A comprehensive review of AI applications across neuroscience-related medical fields,
examining current capabilities, methodological approaches, and emerging clinical frontiers.
Presenter:
Feodor Tikhomirov
Student ID: 75057203
Introduction to AI
Date: April, 2026

2.

Importance of the topic
Life-Critical Consequences
Errors in neurosurgery and neurology carry extreme stakes: permanent disability or death.
The cost of diagnostic or surgical mistakes demands continuous improvement in
analytical accuracy and clinical decision-making precision.
Complex Data Overload
Clinicians face massive, heterogeneous datasets including MRI and CT imaging, EEG
signals, continuous monitoring data, molecular information, and clinical records that
exceed human capacity for rapid, comprehensive analysis using traditional methods
alone.
Speed and Ethics Imperatives
In acute neurological conditions, single days determine patient outcomes. AI acceleration
of analysis offers substantial clinical benefit, while simultaneously raising essential
questions about system trust, error responsibility, and regulatory frameworks for lifecritical decisions.

3.

Abstract of the paper
The aim of this survey is to review how AI is currently used in neurosurgery, neurology, and neurotechnology. The survey examines both clinical and
technological applications, comparing data types, AI methods, strengths, weaknesses, and future directions across peer-reviewed literature from 2022 to
2025.
Diagnostic Neurosurgery
Neuro-Oncology
Neurology & Clinical
Neurotechnology
Neuroscience
Imaging interpretation, anomaly
detection, tumor classification,
lesion segmentation
Tumor grading, molecular
diagnosis, radiomics-based
assessment
Stroke, epilepsy, Parkinson's
disease pattern recognition
Surgical Support
Technologies
Brain-computer interfaces,
neurosignal analysis, monitoring
Robotics, VR/AR for planning,
visualization, training
Survey Coverage: The review synthesizes research from 2020-2026, establishing foundations from key systematic reviews while capturing recent
methodological advances and clinical implementations across all five domains.

4.

Approaches Used in the Papers
Literature Foundation
Research Flow
The survey is based on recent research papers and review papers focused on AI applications in
neuroscience-related medicine. The reviewed papers examine AI in diagnostic imaging, tumor analysis,
clinical prediction, neurological disease recognition, brain signal processing, and surgical support.
Data Types
MRI/CT • EEG/fMRI • Clinical Records
Molecular Data
↓
AI Methods and Models
Machine Learning – structured clinical data
Deep Learning – complex, large datasets
AI Methods
ML • DL • CNN • Radiomics • Hybrid Models
Artificial Neural Networks – pattern
CNNs – MRI, CT imaging analysis
recognition
Radiomics/Radiogenomics – tumor analysis
↓
Hybrid Models – combined approaches
Clinical Applications
Diagnosis • Prediction • Decision Support
Surgery
Data Types Analyzed
MRI
CT
EEG
fMRI
MEG
Clinical Records
Molecular Data
Monitoring Data

5.

Key Findings
Diagnostic Neurosurgery
Overall Conclusion
AI helps detect abnormalities, classify tumors, segment lesions with anatomical precision, and support clinical
The literature shows that AI is already a
decisions. Systems demonstrate validated performance in predicting postoperative outcomes and disease progression
multifunctional clinical support tool with
from preoperative data.
demonstrated capabilities across all five
domains examined in this survey.
Neuro-Oncology
AI is used for tumor detection, grading, molecular diagnosis, and non-invasive assessment of tumor burden. Radiomicsbased methods enable assessment by integrating quantitative imaging features with clinical data, supporting treatment
planning without invasive biopsy.
Neurology & Clinical Neuroscience
AI supports earlier diagnosis and pattern recognition in stroke, epilepsy, Parkinson's disease, and related disorders.
Machine learning models identify subtle biomarkers that escape conventional clinical interpretation and predict
individual complication risks.
Neurotechnology & Surgical Support
AI helps analyze brain signals, support monitoring, and improve brain-computer interfaces. Connected with robotics,
VR, and AR for planning, visualization, training, and assistance during operations in Kazakhstan, USA, and Russia.
Accelerated analysis of complex data
Improved pattern recognition
Enhanced clinical decision support
Multi-modal data integration

6.

How AI Works in This Field ёпта
Level 1
Level 2
Level 3
Data
AI Methods
Outcomes
MRI / CT
Machine Learning (ML)
Structural and functional brain imaging for
diagnosis and surgical planning
EEG / fMRI / MEG
Brain activity signals for monitoring and pattern
recognition
Diagnosis
Structured clinical data analysis, prediction
models
→
Deep Learning (DL)
Complex pattern recognition in large datasets
Early detection, tumor classification, anomaly
identification
→
Prediction
Complication risk, disease progression,
outcomes
CNN
Clinical Records
Patient history, symptoms, treatment
information
Convolutional neural networks for imaging
analysis
Radiomics
Molecular / Genetic Data
Tumor characterization and personalized
medicine
Quantitative imaging feature extraction for
oncology
Decision Support
Treatment planning, clinical recommendations
Surgical Assistance
Planning, navigation, robotics, VR/AR support
Monitoring / Rehabilitation
Hybrid Models
Monitoring Data
Continuous movement, cognition, functional
status
Combined approaches for multi-modal data
Continuous care, follow-up, recovery tracking

7.

Strengths and Limitations
Strengths
Limitations
Fast Analysis of Large Data
Lack of Standardization
AI processes massive datasets with superior speed and efficiency compared to
Clinical information and datasets differ from clinic to clinic, making it difficult to
human analysis, reducing time from hours to minutes.
use the same AI model across different institutions.
Improved Pattern Recognition
Need for External Validation
Machine learning identifies subtle biomarkers and patterns that escape
AI can make mistakes in complex imaging interpretation, requiring mandatory
conventional clinical interpretation in complex imaging.
physician validation before clinical decisions.
Prediction Capabilities
Possible Mistakes in Complex Data
Support for prediction of complications and disease progression enables
MRI and CT images can be difficult to interpret, and AI systems may produce
proactive treatment planning and risk management.
errors in very complex or unusual cases.
Multi-Modal Data Integration
Ethical and Privacy Issues
Ability to work with multiple data types—imaging, clinical records, brain signals,
Concerns about neurodata privacy, especially in brain-computer interfaces, and
molecular data—in a unified framework.
questions about responsibility for AI errors.
Decision Support
High Cost and Implementation Barriers
Structured assistance for complex clinical decision-making under time pressure,
Powerful technical infrastructure requirements and high costs limit accessibility
providing evidence-based recommendations.
outside major medical centers.

8.

Research Papers Used in the Survey Draft
1
3
5
Danilov et al. (2020)
2
Danilov et al. (2022)
Contribution: Identified the main research areas of AI in neurosurgery using systematic
Contribution: Focused specifically on AI applications in clinical neuro-oncology. Examined
review and topic modeling. Established foundational understanding of how AI is organized
tumor detection, grading, and molecular diagnosis using machine learning and radiomics
across neurosurgical applications.
approaches.
Hamam (2024)
4
Kazemzadeh et al. (2023)
Contribution: Reviewed the broader transformative impact of AI in neurosurgery and
Contribution: Examined robotics, virtual reality, and augmented reality in neurosurgery.
neurology. Provided comprehensive analysis of how AI is revolutionizing patient care
Documented successful implementations in Kazakhstan, USA, and Russia for surgical
across multiple domains.
assistance.
Shenderyuk-Zhidkov et al. (2026)
6
Mofatteh et al. (2025)
Contribution: Focused on ethical and regulatory issues in neurotechnology and AI.
Contribution: Systematic review of diagnostic neurosurgery applications. Analyzed AI
Addressed privacy protection, neurodata governance, and the need for international
performance in imaging interpretation, anomaly detection, and lesion segmentation.
standards.
7
Application of AI Technologies in Neurology (2024)
8
Artificial Intelligence in Neurosurgery (2023)
Contribution: Showed AI applications in stroke, epilepsy, Parkinson's disease, and broader
Contribution: Provided a broad overview of AI in neurosurgical applications. Covered
neurological practice. Demonstrated pattern recognition and early diagnosis capabilities.
multiple use cases and established baseline understanding of current capabilities.
Together, these papers support the survey by covering clinical applications, methodological approaches, limitations, and future ethical and regulatory challenges. They provide
a comprehensive foundation for understanding AI's current role and future potential in neuroscience-related medicine.

9.

Future Directions and Final Conclusion
Technical Directions
Final Conclusion
Better standardization of data across clinics
AI has strong potential in neurosurgery, neurology,
Stronger external validation frameworks
and neurotechnology, with demonstrated
More accurate imaging analysis algorithms
capabilities in diagnosis, prediction, and surgical
Better real-time surgical support systems
assistance.
However, its future success depends on safe,
Clinical Directions
validated, and human-centered implementation that
addresses current limitations.
Faster workflow and reduced documentation burden
More personalized treatment planning
AI should support doctors, not replace
Stronger rehabilitation and follow-up support
medical responsibility.
Wider hospital integration beyond specialized centers
Ethical and Regulatory Directions
Strengthened neurodata privacy protections
Feodor Tikhomirov
75057203 | Assignment 3 | 2026
Clearer regulation of AI in medical decision-making
Mandatory human supervision requirements
International standards and cooperation frameworks
Thank You