AI-Powered Clinical Decision Support in Neurology: Help or Hindrance?

Check out our extensive video library (see channel for our latest videos)

Abstract

This paper examines the application of artificial intelligence (AI) in neurology to assist doctors in making informed decisions about patient care. We discuss the benefits and drawbacks of using AI tools in brain-related medical conditions. Through reviewing recent studies and real-world examples, we found that AI can identify patterns in brain scans, predict seizures, and aid in diagnosing strokes with greater accuracy and efficiency than ever before. However, we also encountered significant issues, including instances where AI makes mistakes, privacy concerns, and doctors’ lack of trust in the technology. Our analysis reveals that while AI holds great potential to enhance patient care, its implementation requires careful consideration and ongoing monitoring. The key finding is that AI works best when it supports doctors rather than trying to replace them. We recommend establishing clear guidelines for the use of AI in neurology departments and providing enhanced training for healthcare workers.

Introduction

Neurology deals with some of the most complex medical conditions affecting the human brain and nervous system. From strokes that need immediate treatment to slowly progressing diseases like Alzheimer’s, neurologists face tough decisions every day. These decisions can mean the difference between a patient walking again or being permanently disabled, between catching a brain tumor early or missing the chance for successful treatment.

In recent years, AI has entered the field of neurology, promising to make these decisions easier and more accurate. Computer programs can now analyze brain scans, predict when seizures might happen, and even suggest treatment plans. Some hospitals already use these tools, while others remain skeptical.

This paper examines whether AI genuinely enhances neurologists’ ability to provide better care or if it introduces new issues that may ultimately harm patients. We’ll discuss real-world examples from hospitals around the world, review the latest research findings, and hear from doctors who utilize these tools daily. Our goal is to provide healthcare professionals, particularly physicians, with a clear understanding of where AI stands in neurology today and what to expect in the years to come.

What Is AI Clinical Decision Support?

Clinical decision support systems powered by AI are computer programs that assist doctors in making informed medical decisions. In neurology, these systems analyze patient information like brain scans, blood tests, medical history, and symptoms. The AI then provides suggestions about possible diagnoses, treatment options, or warns about potential problems.

Think of it like having a brilliant assistant who has read millions of medical papers and seen thousands of similar cases. This assistant never gets tired, doesn’t forget important details, and can spot patterns that humans might miss. However, unlike a human assistant, it cannot fully understand the context of a patient’s life or make informed judgment calls based on experience and intuition.

These systems work by using machine learning, a type of AI that learns from examples. Developers feed the computer thousands or millions of medical cases, teaching it to recognize patterns. For instance, they might display thousands of brain scans from stroke patients, along with information about which treatments were most effective. Over time, the AI learns to identify strokes and suggest appropriate treatments.

Current Applications in Neurology

Stroke Detection and Management

One of the most successful applications of AI in neurology is the identification of strokes. When someone has a stroke, every minute counts; brain cells die quickly without oxygen, and the faster doctors can start treatment, the better the outcome.

Several AI systems now help emergency departments spot strokes on CT scans within seconds. A company called Viz.ai developed software that analyzes brain scans and immediately alerts the stroke team if it detects a large vessel occlusion – a serious type of stroke where a major blood vessel in the brain is blocked. Studies show this system can cut the time to treatment by over 30 minutes, which can save lives and prevent disability.

Dr. Sarah Chen, a neurologist at Cleveland Medical Center, shares her experience: “Before we had the AI system, it could take 45 minutes or more to get a radiologist to review an urgent scan at 3 AM. Now, the AI flags potential strokes instantly, enabling us to begin preparing for treatment while awaiting the official diagnosis. Last week, it caught a stroke in a young patient that we might have missed because her symptoms were unusual.”

Epilepsy Prediction and Monitoring

People with epilepsy often live in fear of their next seizure. AI is changing this by predicting when seizures are likely to occur. Several research teams have developed algorithms that analyze brain wave patterns (EEG data) to predict seizures hours or even days in advance.

A system developed at the University of Melbourne can predict seizures with approximately 70% accuracy up to one hour before they occur. Patients wear a small device that monitors their brain activity and sends alerts to their phone when a seizure risk is high. This gives them time to take medication, get to a safe place, or alert someone who can help.

Multiple Sclerosis Monitoring

Tracking the progression of multiple sclerosis (MS) requires comparing brain scans over time to spot new lesions. This process is time-consuming and prone to error, especially when changes are subtle. AI programs can now automatically compare scans and highlight even tiny changes that might indicate disease activity.

A study from Johns Hopkins found that AI detected 40% more MS lesions than human radiologists reviewing the same scans. This matters because catching disease activity early allows doctors to adjust treatments before permanent damage occurs.

Dementia Diagnosis

Diagnosing Alzheimer’s disease and other forms of dementia typically requires expensive scans and cognitive tests. AI is making this process faster and potentially more accessible. Researchers have developed algorithms that can detect early signs of dementia using simple tests, such as drawing a clock or analyzing speech patterns.

IBM Research created an AI system that analyzes writing samples to predict Alzheimer’s with 75% accuracy years before symptoms appear. While not yet ready for clinical use, such tools could eventually enable much earlier intervention, when treatments may be more effective.

Benefits of AI in Neurology

Speed and Efficiency

The most obvious benefit of AI is speed. What takes a human expert 30 minutes to analyze, AI can process in seconds. This speed is crucial in emergencies like stroke, where “time is brain.” However, it also helps with routine care by reducing wait times for scan results and enabling neurologists to see more patients.

Dr. Michael Torres, who runs a busy neurology practice in Houston, explains: “I used to spend hours every day reviewing EEG recordings, looking for abnormal patterns. Now, the AI highlights areas of concern, allowing me to focus my attention there. It’s like having a research assistant who pre-screens everything. I can see 30% more patients without working longer hours.”

Pattern Recognition

The human brain is adept at recognizing patterns, but AI can be even better, particularly when dealing with subtle or complex patterns across large datasets. AI doesn’t get tired after looking at the 50th scan of the day, and it doesn’t have bad days where it might miss something obvious.

In a landmark study published in Nature Medicine, an AI system outperformed neurologists in detecting brain aneurysms on imaging scans. The AI caught 95% of aneurysms compared to 84% for human doctors. When doctors used the AI as a second opinion, their detection rate improved to 92%.

Access to Expertise

Many parts of the world lack neurologists. In rural areas, patients might need to travel hundreds of miles to see a specialist. AI can bring expert-level analysis to these underserved areas. A general practitioner with access to AI tools can provide better neurological care than would otherwise be possible.

For example, in rural India, a project using AI to analyze retinal photographs (which can show signs of neurological disease) has screened thousands of patients who would never have seen a neurologist. The AI identifies those who need urgent referral to specialists in distant cities.

Continuous Learning

Unlike human doctors who can only learn from their own patients and whatever research they have time to read, AI systems can learn from millions of cases worldwide. They are updated continuously as new data becomes available, potentially remaining more current than busy clinicians.

Challenges and Limitations

The Black Box Problem

One of the most significant issues with AI in medicine is that doctors often struggle to understand how the AI reached its conclusions. Most modern AI systems are “black boxes” – they take in data and spit out answers without explaining their reasoning.

Dr. Jennifer Walsh, a neurologist at Stanford, describes her frustration: “The AI tells me this patient has an 85% chance of having a rare type of seizure disorder. But when I ask why, there’s no answer. As a doctor, I need to understand the reasoning to feel confident in the diagnosis, especially when it disagrees with my clinical judgment.”

This lack of transparency makes it hard for doctors to trust AI recommendations, particularly in complex or unusual cases. It also creates legal and ethical problems – if something goes wrong, who is responsible? The doctor who followed the AI’s advice or the company that made the AI?

Bias in AI Systems

AI systems learn from historical data, which means they can perpetuate and even amplify existing biases in healthcare. If an AI is trained mostly on brain scans from white patients, it might not work as well for Black or Asian patients whose brain anatomy can vary slightly.

A sobering example comes from a study that found AI systems for detecting brain bleeds performed significantly worse on scans from Black patients. This happened because the training data included few Black patients, and the AI hadn’t learned to account for higher levels of melanin that can affect how brain tissue appears on certain scans.

Dr. Rosa Martinez, who researches AI bias, warns: “We’re seeing AI systems that work great in the hospitals where they were developed, but fail when deployed elsewhere. A system trained on data from a wealthy urban hospital might not work well in a rural clinic serving a different population.”

Over-reliance and Deskilling

As AI becomes more prevalent, there’s a real danger that doctors might lose critical diagnostic skills. If you always rely on AI to interpret EEGs or identify strokes on scans, what happens when the system fails or isn’t available?

Young doctors training today might never develop the pattern recognition skills that senior neurologists possess. Dr. Robert Kim, who trains neurology residents, worries about this trend: “I see residents who immediately want to know what the AI thinks before they’ve even looked at the scan themselves. They’re becoming dependent on the technology instead of using it as a tool.”

Technical Failures and Cybersecurity

Like all technology, AI systems can fail. Hospitals have reported instances where AI systems went offline during critical moments, leaving staff scrambling. There’s also the risk of cyberattacks. Imagine if hackers altered an AI system to misdiagnose strokes – patients could die before anyone noticed the problem.

In 2021, a major hospital system in Florida experienced a ransomware attack that disabled its AI-powered stroke detection system for three days. During this period, they had to revert to manual processes, resulting in delays in treatment for several patients.

Cost and Implementation Challenges

While AI promises to save money in the long run, the upfront costs can be substantial. Hospitals need to buy the software, upgrade their computer systems, train staff, and often hire new IT personnel. For smaller hospitals and clinics, these costs can be prohibitive.

There’s also the challenge of integrating AI systems with existing hospital technology. Many hospitals utilize electronic health record systems that are decades old and don’t integrate seamlessly with modern AI tools. Getting everything to work together can take months or years of effort.

Case Studies and Real-World Examples

Success Story: Mount Sinai’s Epilepsy Monitoring Unit

Mount Sinai Hospital in New York implemented an AI system for monitoring epilepsy patients in 2019. The system continuously analyzes EEG data from patients staying in the epilepsy monitoring unit, alerting staff to potential seizure activity.

Before AI, nurses had to constantly watch multiple EEG screens, a tedious job that led to fatigue and missed events. The AI system reduced false alarms by 75% while catching 98% of actual seizures. This allowed nurses to focus on patient care rather than staring at screens.

Patient satisfaction scores improved dramatically. Maria Rodriguez, whose daughter was monitored at Mount Sinai, shares: “The old system had so many false alarms that we couldn’t sleep. The nurses were always rushing in for nothing. With the new AI system, they only came when there was a real problem. We finally got some rest, and the nurses seemed less stressed too.”

Mixed Results: The VA’s Dementia Screening Program

The Veterans Administration launched an ambitious program in 2020 to use AI for early dementia screening. The goal was to identify veterans at risk for cognitive decline and intervene early with lifestyle changes and medications.

The results have been mixed. The AI successfully identified many veterans with early-stage dementia who might have gone undiagnosed for years. However, it also flagged many healthy veterans as at-risk, causing unnecessary anxiety and leading to expensive follow-up tests.

Dr. James Anderson, who oversees the program, reflects on the experience: “We learned that AI is great at raising red flags, but you need human judgment to interpret those flags in context. A veteran might score poorly on cognitive tests because of hearing loss or depression, not dementia. The AI couldn’t distinguish between these causes.”

Failure and Lessons Learned: London Stroke AI Trial

A major London hospital network invested millions in an AI system that promised to revolutionize stroke care. The system was supposed to analyze ambulance data and direct stroke patients to the most appropriate hospital based on their specific needs and each hospital’s current capacity.

The trial failed spectacularly. The AI made routing decisions based on incomplete data, sending patients to hospitals that lacked the necessary specialists or equipment. In one case, a patient was sent to a hospital 30 minutes away when a closer facility could have provided better care.

The investigation revealed several problems: the AI hadn’t been trained on local data, the ambulance crews weren’t adequately trained to input data, and there was no override mechanism for obviously wrong decisions. The trial was suspended after three months, but not before several patients experienced delayed treatment.

Comparing AI to Traditional Methods

Speed of Diagnosis

Traditional neurological diagnosis typically involves multiple steps, including an initial consultation, ordering tests, waiting for results, specialist interpretation, and follow-up appointments. This process can take weeks or months for non-emergency cases.

AI dramatically compresses this timeline. Brain scans can be analyzed instantly, patterns in symptoms can be recognized immediately, and risk assessments can be calculated in real-time. What once took weeks can now happen in minutes.

However, speed isn’t everything. Dr. Patricia Lee, a veteran neurologist, points out: “Sometimes the traditional slow process has value. When you take time to think about a case, talk to colleagues, and observe how symptoms evolve, you often catch things that a quick AI analysis might miss.”

Accuracy Rates

Studies comparing AI to human doctors show mixed results depending on the specific task. For well-defined problems, such as detecting strokes on CT scans or identifying seizure patterns on EEG, AI often matches or exceeds human accuracy. A meta-analysis of 50 studies found that AI systems had an average accuracy of 87% compared to 84% for neurologists.

However, for complex diagnoses that require the integration of multiple types of information—patient history, physical exam findings, lab results, and imaging—experienced neurologists still outperform AI. The human advantage is evident in unusual cases that don’t fit typical patterns.

Cost Effectiveness

The economics of AI versus traditional methods are complicated. AI systems require significant upfront investments, but can reduce long-term costs by:

Reducing diagnostic errors that lead to expensive complications
Decreasing the need for repeat tests
Allowing neurologists to see more patients
Catching diseases earlier, when treatment is cheaper

A health economics study from the University of Pennsylvania found that hospitals using AI for stroke detection saved an average of $2,500 per patient through faster treatment and better outcomes. However, these savings took three years to exceed the initial investment in the technology.

Challenges and Limitations in Greater Detail

Legal and Regulatory Issues

The legal landscape for AI in medicine remains murky. When an AI system makes an error, determining liability is complex. Is it the fault of the doctor who relied on the AI, the hospital that implemented it, or the company that developed it? Current malpractice law doesn’t clearly address these scenarios.

The FDA has approved several AI systems for use in neurology, but the regulatory process is struggling to keep pace with the rapid technological development. By the time a system receives approval, newer and potentially better versions are already available. This creates a dilemma: should hospitals use older, approved systems or newer, potentially superior but unapproved ones?

Data Privacy Concerns

AI systems require vast amounts of patient data to function effectively. This raises serious privacy concerns. Patient brain scans and medical records contain incredibly sensitive information. There’s growing worry about how this data is stored, who has access to it, and how it might be used beyond its intended purpose.

In 2022, a major AI company was found to be using patient data to develop commercial products without proper consent. While the company argued the data was anonymized, researchers showed that brain scans could be traced back to individual patients with surprising accuracy.

Resistance from Medical Professionals

Not all neurologists welcome AI with open arms. Many express concerns about job security, loss of professional autonomy, and the dehumanization of medicine. A survey by the American Academy of Neurology found that 40% of neurologists expressed concern that AI would negatively impact their profession.

Dr. Thomas Wright, a neurologist with 30 years of experience, voices common concerns: “Medicine is as much art as science. Every patient is unique, with their own story, fears, and goals. AI can’t have a conversation with a scared patient or notice the subtle worry in a spouse’s eyes. If we rely too heavily on machines, we risk losing the human connection that’s central to healing.”

The Update Problem

Medical knowledge evolves rapidly. New research constantly changes our understanding of neurological diseases and their treatment. AI systems need regular updates to remain current, but this process is complex and expensive.

There’s also the risk of “catastrophic forgetting” – when an AI system is updated with new information, it might lose previously learned knowledge. One hospital reported that after updating their seizure detection AI with new data, it became worse at detecting the types of seizures it previously handled well.

Applications and Use Cases

Emergency Departments

Emergency departments have become the front line for AI adoption in neurology. When patients arrive with symptoms like sudden weakness, confusion, or severe headaches, every second counts. AI helps emergency doctors who aren’t neurology specialists make critical decisions.

The typical workflow now involves AI analyzing brain scans as soon as they’re completed. If the AI detects signs of stroke, aneurysm, or bleeding, it immediately alerts the stroke team. This parallel processing means treatment can begin even before a radiologist officially reads the scan.

Dr. Kevin Park, an emergency physician, describes the impact: “I’m not a neurologist, but with AI support, I can confidently initiate stroke protocols. Last month, the AI caught a subtle brain bleed in a patient I was about to discharge with a migraine diagnosis. That catch probably saved her life.”

Intensive Care Units

In neurological ICUs, patients require constant monitoring. AI systems now track multiple parameters simultaneously—such as brain pressure, oxygen levels, and electrical activity—and predict complications before they become critical.

One particularly valuable application is detecting delayed cerebral ischemia in patients with subarachnoid hemorrhage. This complication can occur days after the initial bleeding and is often missed until permanent damage occurs. AI systems monitoring transcranial Doppler ultrasound data can predict these events up to 24 hours in advance with 80% accuracy.

Outpatient Clinics

In neurology clinics, AI assists with routine tasks that consume valuable time and resources. Systems can pre-screen patient questionnaires, analyze videos of movement disorders, and track disease progression over time.

For Parkinson’s disease, AI analyzes smartphone data—how patients type, their voice patterns, and movement characteristics—to monitor disease progression between visits. This provides neurologists with objective data about how patients function in daily life, not just during brief clinic visits.

Rehabilitation Centers

Stroke and brain injury rehabilitation is getting an AI boost. Systems analyze patient movements during therapy exercises, providing real-time feedback and automatically adjusting difficulty levels. This personalized approach helps patients progress faster than traditional one-size-fits-all protocols.

Virtual reality systems powered by AI create engaging rehabilitation exercises tailored to each patient’s deficits and interests. A patient who loves gardening might perform arm exercises by virtually planting flowers, while the AI system monitors their movements and adjusts the challenge level accordingly.

Research Applications

AI is accelerating neurological research by identifying patterns in large datasets that would take human researchers years to find. Machine learning algorithms analyze genetic data, brain imaging, and clinical records from thousands of patients to identify new disease subtypes and potential treatment targets.

One breakthrough came when AI analyzing brain scans from Alzheimer’s patients identified five distinct subtypes of the disease, each with different progression patterns and treatment responses. This discovery is leading to more personalized treatment approaches.

The Human Element

Despite all the technology, the successful implementation of AI in neurology depends on human factors. The best outcomes occur when AI augments human expertise rather than attempting to replace it.

Nurse practitioner Lisa Thompson shares her perspective: “AI doesn’t reduce my workload – it changes it. Instead of spending time on routine pattern recognition, I can focus on patient education, coordinating care, and providing emotional support. The AI handles the data; I handle the human being.”

Training is crucial. Healthcare workers need to understand both what AI can and cannot do. They need to maintain their clinical skills while learning to effectively use AI tools. This requires ongoing education and a shift in how we train new neurologists.

Dr. Amanda Foster, who runs a neurology residency program, has redesigned her curriculum: “We still teach traditional diagnostic skills, but now we also teach AI literacy. Residents learn to critically evaluate AI recommendations, understand the technology’s limitations, and integrate AI insights with clinical judgment.”

Ethical Considerations

The use of AI in neurology raises profound ethical questions. When AI recommends withholding treatment from a patient it deems unlikely to recover, who makes the final decision? How do we ensure AI systems respect patient autonomy and cultural values?

There’s also the question of equity. If AI systems provide better neurological care, but only wealthy hospitals can afford them, does this worsen healthcare disparities? Some argue that AI could democratize access to expert neurological care, while others worry it will create a two-tier system.

Informed consent becomes complicated when AI is involved in care decisions. Do patients have the right to refuse AI analysis of their data? Should they be told when AI influenced their diagnosis or treatment plan? These questions don’t have easy answers.

Future Directions

The future of AI in neurology presents both exciting and challenging prospects. Emerging technologies promise even greater capabilities:

Brain-Computer Interfaces: AI-powered devices that directly interface with the nervous system could restore function to patients with paralysis and provide new treatment options for neurological diseases.

Predictive Analytics: Future AI systems may be able to predict neurological diseases years or decades before symptoms appear, enabling preventive interventions that we can only dream of today.

Personalized Medicine: AI could tailor treatments to individual patients based on their genetic profile, lifestyle, and specific disease characteristics.

Digital Twins: Virtual models of individual patient brains could enable doctors to test treatments virtually before applying them in real-world settings.

However, realizing this potential requires addressing current limitations. We need AI systems that can explain their reasoning, eliminate bias, and integrate seamlessly with clinical workflows. We need regulations that ensure safety without stifling innovation. Most importantly, we need to maintain the human elements of compassion and judgment that define good medical care.

Conclusion

AI-powered clinical decision support in neurology is neither purely helpful nor a hindrance – it’s a powerful tool that can improve patient care when used wisely. The evidence demonstrates clear benefits in specific applications, such as stroke detection, seizure prediction, and image analysis. AI can facilitate neurological care that is faster, more accurate, and more accessible.

But significant challenges remain. The black box nature of AI decisions, potential for bias, risk of deskilling, and complex implementation issues can’t be ignored. Real-world experiences show both dramatic successes and notable failures.

The path forward requires balanced integration of AI with human expertise. AI should augment neurologists’ capabilities, not replace their judgment. Success depends on proper training, careful implementation, continuous monitoring, and maintaining focus on patient-centered care.

For healthcare professionals considering AI adoption, the key is to start small with well-validated applications, invest in training, and maintain healthy skepticism. AI is a tool, not a magic solution. Like any tool, its value depends on how skillfully it’s used.

The future of neurology will undoubtedly include AI, but it will be shaped by how well we address current limitations and maintain the human elements that make medicine a healing art as well as a science.

Key Takeaways

AI excels at specific tasks, with pattern recognition in imaging, EEG analysis, and risk prediction showing the most promise, often achieving accuracy that matches or exceeds that of human experts.
Speed is a significant advantage – AI can analyze data in seconds that would take humans hours, crucial for time-sensitive conditions like stroke.
Integration challenges are real – Technical, financial, and workflow obstacles make implementation more complicated than vendors often suggest.
The black box problem persists: most AI systems can’t explain their reasoning, making it difficult for doctors to fully trust their recommendations.
Bias is a serious concern – AI systems can perpetuate and amplify existing healthcare disparities if not carefully developed and monitored.
Human expertise remains essential; the best outcomes occur when AI augments rather than replaces clinical judgment.
Training is crucial – healthcare workers need new skills to effectively utilize AI while maintaining their traditional diagnostic abilities.
Regulatory frameworks lag – Current laws and regulations haven’t kept pace with the evolving capabilities and risks of AI.
Cost-benefit analysis is complex. While AI can save money in the long term, upfront costs and ongoing maintenance are substantial.
Patient care must stay central – Technology should enhance the doctor-patient relationship, not replace it.

Frequently Asked Questions:

FAQ Section

Q: Will AI replace neurologists?

A: No, AI is designed to assist neurologists, not replace them. While AI excels at specific tasks, such as pattern recognition, it lacks the clinical judgment, empathy, and complex reasoning that human doctors provide. The future involves neurologists collaborating with AI tools to deliver more effective care.

Q: How accurate is AI in diagnosing neurological conditions?

A: Accuracy varies by condition and specific AI system. For well-defined tasks, such as detecting strokes on CT scans, AI often achieves 90-95% accuracy. For complex diagnoses that require the integration of multiple data types, experienced neurologists still outperform AI. The best results are achieved by combining AI analysis with human expertise.

Q: What happens if the AI makes a mistake?

A: Currently, the attending physician remains responsible for all clinical decisions, even when following AI recommendations. This is why doctors must understand the limitations of AI and maintain their diagnostic skills. Legal frameworks are still evolving to address questions of liability.

Q: Can patients refuse AI involvement in their care?

A: This varies by institution and jurisdiction. Some hospitals allow patients to opt out of AI analysis, while others consider it part of standard care. Patients should discuss their preferences with their healthcare providers.

Q: How do I know if my hospital uses AI?

A: Ask your healthcare provider directly. Many hospitals are transparent about their use of AI tools. You have the right to understand what technologies are involved in your care.

Q: Is my data safe when used by AI systems?

A: Medical AI systems must comply with privacy laws like HIPAA. However, concerns exist about data security and potential misuse. Ask your healthcare provider about their data protection policies and how your information is used and protected.

Q: Does AI work equally well for all patients?

A: Unfortunately, no. Many AI systems exhibit bias due to the data on which they were trained. Systems trained primarily on data from one demographic group may perform worse for others. This is an active area of research and improvement.

Q: How much does AI add to healthcare costs?

A: Initial implementation is expensive, but studies suggest AI can reduce overall healthcare costs through earlier diagnosis, fewer errors, and more efficient care. Individual patient costs depend on insurance coverage and specific applications.

Q: Should I trust an AI diagnosis?

A: AI diagnoses should be considered alongside clinical evaluation, not in isolation. Think of AI as providing a second opinion that your doctor considers along with other information. Always discuss any concerns with your healthcare provider.

Q: What’s the future of AI in neurology?

A: The future likely includes more sophisticated AI tools that can explain their reasoning, personalized treatments based on individual patient data, and earlier disease detection. However, human doctors will remain central to providing compassionate, contextual care.

References:

Adeoye, O., Nystrom, K. V., Yavagal, D. R., et al. (2019). Recommendations for the establishment of stroke systems of care: A 2019 update. Stroke, 50(7), e187-e210.

Benjamens, S., Dhunnoo, P., & Mesko, B. (2020). The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. NPJ Digital Medicine, 3(1), 1-8.

Chilamkurthy, S., Ghosh, R., Tanamala, S., et al. (2018). Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study. The Lancet, 392(10162), 2388-2396.

Esteva, A., Robicquet, A., Ramsundar, B., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.

Hashimoto, D. A., Witkowski, E., Gao, L., Meireles, O., & Rosman, G. (2020). Artificial intelligence in anesthesiology: Current techniques, clinical applications, and limitations. Anesthesiology, 132(2), 379-394.

Johnson, K. W., Torres Soto, J., Glicksberg, B. S., et al. (2018). Artificial intelligence in cardiology. Journal of the American College of Cardiology, 71(23), 2668-2679.

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key Challenges for Delivering Clinical Impact with Artificial Intelligence BMC Medicine, 17(1), 1-9.

Liu, X., Faes, L., Kale, A. U., et al. (2019). A comparison of deep learning performance against healthcare professionals in detecting diseases from medical imaging: A systematic review and meta-analysis. The Lancet Digital Health, 1(6), e271-e297.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.

Senders, J. T., Staples, P. C., Karhade, A. V., et al. (2018). Machine learning and neurosurgical outcome prediction: A systematic review. World Neurosurgery, 109, 476-486.

Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.

Van Leeuwen, K. G., Schalekamp, S., Rutten, M. J., van Ginneken, B., & de Rooij, M. (2021). Artificial intelligence in radiology: 100 commercially available products and their scientific evidence. European Radiology, 31(6), 3797-3804.

Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731.

Zhu, G., Jiang, B., Tong, L., Xie, Y., Zaharchuk, G., & Wintermark, M. (2019). Applications of deep learning to neuroimaging techniques. Frontiers in Neurology, 10, 869.

[Internal Medicine -Home]