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Textbooks and PDFs are powerful tools, but they’re also rigid. Many learners skim, forget, or get overwhelmed by dense pages of text. Now imagine if those same materials could adapt to you. That’s what Google Research is building with Learn Your Way—a system that transforms PDFs and textbooks into interactive, adaptive lessons. From Static Reading to Adaptive Learning Upload a textbook or article, and “Learn Your Way” reshapes it into a dynamic learning experience. Instead of passively reading, you can: The result? Content feels less like a wall of words and more like a responsive tutor. The Evidence: Stronger Recall Google’s first efficacy study was striking: Why This Matters for Researchers Academics and professionals face the same problem as students: too much reading, too little time. Learn Your Way could transform: For early-career researchers, it could act as a study scaffold; for experienced academics, a tool to accelerate comprehension across new fields. Why This Matters for Individuals with Dyslexia Traditional textbooks are especially challenging for people with dyslexia, where dense text, long paragraphs, and lack of scaffolding can cause fatigue and frustration. Learn Your Way offers several benefits: This doesn’t replace structured literacy interventions, but it creates a more accessible environment for everyday studying, professional training, or even research reading. The Bigger Picture Learn Your Way moves education and research from “read and memorize” to “engage and adapt.” For: The Takeaway Education tools are evolving. Textbooks are no longer static—they’re starting to teach back. Whether you’re a student studying for exams, a researcher scanning through dozens of PDFs, or a learner with dyslexia navigating dense reading, Learn Your Way shows how adaptive AI can make knowledge not only more efficient but also more inclusive.

Artificial intelligence (AI) is no longer a futuristic idea—it is shaping the way professionals in every field approach their work. From engineers designing mining equipment to nurses writing care plans, AI is being tested against the real demands of professional practice. And now, researchers are asking a bold question: Can AI do your job? OpenAI’s latest study doesn’t give a simple yes or no. Instead, it paints a much more nuanced picture—AI is not yet a full replacement for human professionals, but it’s edging surprisingly close in some areas. For us as therapists, this raises both opportunities and challenges that are worth exploring. The Benchmark: Measuring AI Against Professionals To answer this question, OpenAI created a new framework called GDPval. Think of it as a “skills exam” for AI systems, but instead of testing algebra or trivia, the exam covered real-world professional tasks. The Results: Fast, Cheap, and Sometimes Surprisingly Good The study revealed a mix of strengths and weaknesses: When human experts compared AI outputs to human-created work, they still preferred the human versions overall. Yet, the combination of AI-generated drafts reviewed and refined by professionals turned out to be more efficient than either working alone. Why This Matters for Therapists So, what does this mean for us in speech therapy, psychology, occupational therapy, and related fields? AI is not going to replace therapists any time soon—but it is already shifting how we can work. Here are some examples of how this might apply in our daily practice: But here’s the critical caveat: AI’s work often looks polished on the surface but may contain subtle errors or missing details. Harvard Business Review recently described this problem as “workslop”—content that seems professional but is incomplete or incorrect. For therapists, passing along unchecked “workslop” could mean inaccurate advice to families, poorly designed therapy tasks, or even harm to clinical trust. This is where our professional expertise becomes more important than ever. The Therapist’s Role in the AI Era AI should be thought of as a bright but clumsy intern: That means our role doesn’t diminish—it evolves. Therapists who supervise, refine, and direct AI outputs will be able to reclaim more time for the heart of therapy: building relationships, delivering personalized interventions, and making evidence-based decisions. Instead of drowning in paperwork, we could spend more energy face-to-face with clients, coaching families, or innovating in therapy delivery. Looking Ahead Some AI experts predict that by 2026, AI may be able to match humans in most economically valuable tasks. While this sounds alarming, it doesn’t mean therapists will vanish from the workforce. Instead, it means that those who learn to integrate AI effectively will thrive—while those who resist may struggle to keep up. The takeaway for us is clear: Final Thought As therapists, our work is built on empathy, creativity, and nuanced understanding—qualities no AI can replicate. But AI can free us from repetitive tasks, give us faster access to resources, and help us innovate in service delivery. The future of therapy is not AI instead of us—it’s AI alongside us. And that collaboration, if used wisely, can give us more time, more tools, and ultimately, more impact for the people we serve.

In a recent interview, Demis Hassabis, CEO of Google DeepMind, dismissed claims that today’s AI models possess “PhD-level intelligence.” His message was clear: while AI can sometimes match or outperform humans in narrow tasks, it is far from demonstrating general intelligence. Calling these models “PhD-intelligences,” he argues, is misleading and risks creating unrealistic expectations for what AI can do in fields like healthcare and research. Hassabis notes that models such as Gemini or GPT-style systems show “pockets of PhD-level performance” in areas like protein folding, medical imaging, or advanced problem-solving. However, these systems also fail at basic reasoning tasks, cannot learn continuously, and often make elementary mistakes that no human researcher would. According to Hassabis, true Artificial General Intelligence (AGI)—a system that can learn flexibly across domains—remains 5–10 years away. What This Means for Research and Healthcare AI’s current limitations don’t mean it has no place in our work. Instead, they point to how we should use it responsibly and strategically. Practical Takeaways: Example Applications by Discipline Field Current Benefits of AI Limitations / Risks Healthcare Research Protein structure prediction (e.g., AlphaFold); drug discovery pipelines; imaging diagnostics. Errors in generalization; opaque reasoning; bias in data. Therapy & Psychology Drafting therapy materials; generating behavior scenarios; transcribing sessions. Risk of over-reliance; errors in sensitive contexts. Special Education Differentiated content creation; progress tracking; accessible learning supports. Potentially inaccurate recommendations without context. Looking Ahead Even without AGI, today’s AI tools can dramatically accelerate workflows and augment human expertise. But the caution from Hassabis reminds us: AI is not a replacement for human intelligence—it is a partner in progress. As researchers and clinicians, our responsibility is two-fold: In our next editions, we’ll explore how to integrate AI into research more concretely, with examples from therapy and healthcare studies. References

Meta’s latest innovation—the Ray-Ban Display AI glasses paired with a neural EMG wristband—is creating waves well beyond the tech world. For years, smart glasses promised more than they delivered, but this time, the combination of a heads-up lens display, AI integration, and subtle gesture control suggests wearables are stepping into a new era. For therapists and patients, the potential is huge. At the core of the innovation is a full-color display inside the right lens, giving users discreet, real-time access to information without reaching for a phone. Paired with the Meta Neural Band, which detects wrist and finger movements via electromyography (EMG), the glasses allow hands-free control—ideal for users with mobility limitations or professionals needing quick interactions. Live captions, translations, messaging, and navigation can now appear directly in your line of sight. Key Features Therapists Should Know: Clinical Applications and Patient Benefits Therapy Area Potential Benefits Considerations Speech & Language Therapy Live captions for hearing-impaired clients; on-screen prompts for language tasks; gesture-based engagement Display size/readability; learning curve; potential distraction Occupational Therapy EMG control supports limited dexterity; interactive visual prompts for task sequencing Calibration required; less effective with tremors/severe motor impairments Psychomotor Therapy Movement guidance in real time; visual cues for coordination exercises Must keep exercises embodied, not screen-bound Psychology & Special Education Personalized reminders, translations, discreet coping prompts; increased independence Privacy, data security, risk of over-reliance on prompts What to Watch For The Funny Part: During the demo, the glasses completely malfunctioned—the captions started translating “Hello” into what looked like Morse code! But here’s the twist: even in chaos, the potential for therapy applications shone through. Imagine hands-free prompts during speech therapy, or gesture-controlled task sequences for occupational therapy—this is just the beginning. The bigger picture? These glasses mark a step toward assistive augmented reality. As battery life improves and features like lip-reading captions, real-time therapy overlays, and telehealth integration emerge, therapists could gain a whole new medium for intervention. Awareness now is key—understanding what these devices can and cannot do will help us prepare for the future. Stay tuned for our next edition, where we’ll dive deeper into practical ways to integrate wearables into therapy sessions.

Claude, by Anthropic, is one of the leading Large Language Models (LLMs). It has been evolving fast, and many updates are relevant for therapy, special education, psychology, and related fields. Here’s a summary of what’s new with Claude, plus ideas (and cautions) for how professionals like us can use it. Recent updates in Claude How these can help SLPs, OTs, Special Educators, Psychologists Here are some practical ways we might use Claude’s recent capabilities, plus what to be cautious about. Function / Task How Claude can support Things to watch / best practices Goal / IEP Planning Use Claude to draft or refine Individualized Education Program (IEP) goals, generate multiple options, suggest evidence-based strategies for goals in speech, fine motor, executive functioning, etc. Because of its improved context memory, Claude can remember student profile details across prompts to help maintain coherence. Always review drafts carefully; ensure the language matches legal/regulatory standards; verify that suggestions are appropriate for the individual child. Don’t rely on AI for diagnosis. Keep sensitive student info anonymized. Therapy Material Creation Generate therapy stimuli: e.g. social stories, visual supports, worksheets, scripts for practice, prompts for articulation or language, adapted texts. Longer context window means more ability to build complex lesson sets (e.g. a sequence of sessions) without re-uploading all the materials. Check for accuracy, cultural appropriateness, developmental level. Avoid overly generic content. Use human insight to adapt. Progress Monitoring & Data Analysis Claude can help pull together progress reports, analyze data (e.g. logs of student performance or assessment scores), spot trends, suggest modifications in therapy plans. With improved reasoning, it might help suggest when progress is stalled and propose alternative interventions. Be wary of over-interpreting AI suggestions. Ensure data quality. Maintain human responsibility for decisions. Supporting Learning & Generalization Use learning modes to help students think through tasks: rather than giving answers, Claude can scaffold reasoning, guide metacognitive strategies, support writing reflections. For older students, help them plan writing or projects with step-by-step reasoning. For psychologists, use it for psycho-educational support (e.g. helping students with ADHD plan tasks, break down executive functioning demands). Important: always ensure student is learning the process, not “cheating” or bypassing thinking. Monitor for bias or content that seems inappropriate. Confirm information (e.g. if medical or psychological content). Administrative / Documentation Efficiency Use Claude’s upgraded file tools to create formatted documents, progress notes, therapy plans, meeting summaries, parent-friendly reports. Memory and long context help keep consistent details so you don’t keep repeating basic background. Even here, you need to review for correctness. Also, check confidentiality and data protection policies. For example, do you have permission to include certain data? Ensure work complies with privacy laws. What to be cautious about & ethical considerations What to try soon References Anthropic. (2025, May 22). Introducing Claude 4. https://www.anthropic.com/news/claude-4 Anthropic Anthropic. (2025, August 12). Claude Sonnet 4 model now has a 1 million token context window. TechCrunch. TechCrunch Anthropic. (2025, August 11). Claude AI memory upgrade & incognito mode. The Verge. The Verge Anthropic. (n.d.). Claude for Education: Reimagining AI’s Role in K-12 Learning. Eduscape. Eduscape

What’s new in AI & research Another example is The AI Scientist-v2, which submitted fully AI-generated manuscripts to peer‐review workshops. Though human oversight was still needed in many parts, this is a milestone: an AI doing many steps that were traditionally human-only. arXiv There are also “virtual research assistants” being developed (e.g. at Oxford) that reduce workload by filtering promising leads in large datasets (like astronomical signals) so that scientists can focus their effort. Windows Central What this means (for us, in therapy & education & research) — “so what” What to watch next Here are some topics I’m planning to dive into in future issues: References Wei, J., Yang, Y., Zhang, X., Chen, Y., Zhuang, X., Gao, Y., Zhou, D., Ouyang, W., Dong, A., Cheng, Y., Sun, Y., Bai, L., Bowen, Z., Dong, N., You, C., Sun, L., Zheng, S., Ning, D., … & Zhou, D. (2025). From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery. arXiv. arXiv Yamada, Y., Lange, R. T., Lu, C., Hu, S., Lu, C., Foerster, J., Ha, D., & Clune, J. (2025). The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search (arXiv preprint). arXiv “AI is Revolutionizing University Research: Here’s How.” TechRadar. (2025, September).

When it comes to brain health, timing isn’t just important—it’s everything. A recent breakthrough in England demonstrates just how transformative artificial intelligence can be when speed and accuracy mean the difference between life-long disability and meaningful recovery. The NHS has introduced an AI-powered tool across all 107 stroke centres in England that can analyze CT scans in under a minute. By instantly identifying the type and severity of a stroke, doctors can make treatment decisions faster and more confidently. The results are remarkable: treatment time dropped from an average of 140 minutes to 79 minutes, and the proportion of patients recovering with little or no disability nearly tripled—from 16% to 48% (The Guardian, 2025). Why Therapists Should Pay Attention While most of us don’t work in emergency rooms, the lesson here applies powerfully to our field: the earlier the intervention, the better the outcome. Just as “time is brain” in stroke care, time is potential in developmental therapy. For children with speech delays, autism spectrum disorder (ASD), ADHD, or dyslexia, early intervention is proven to reshape developmental trajectories. Research consistently shows that children who receive targeted therapy early demonstrate stronger communication, social, and learning outcomes compared to those who start later. In swallowing therapy, catching a feeding issue before it escalates can prevent hospitalizations and improve nutritional health. AI’s success in stroke care reminds us of two things: Drawing Parallels for Therapy Imagine an AI assistant that quickly analyzes a child’s speech sample and highlights phonological processes or syntactic errors in minutes—leaving the therapist more time for direct intervention. Or a system that alerts you when a client’s attention patterns, logged across sessions, suggest the need for a strategy change. Like the NHS stroke tool, these systems wouldn’t “do therapy” for us—but they could give us insights faster, allowing us to act at the moment it matters most. Ethical Integration: Guardrails We Need The NHS model also teaches us about safe integration: AI works with clinicians, not instead of them. For therapy, this means: Takeaway Toolkit: “Timely AI Use in Therapy” Here are four reflective questions to guide safe, effective use of AI in your practice: Final Thoughts The NHS story is inspiring—not just because of its immediate life-saving impact, but because it paints a picture of how AI and clinicians can work together. For us in therapy, the lesson is clear: when interventions happen sooner, lives change more profoundly. With AI as a partner, not a substitute, we may be able to bring timely support to even more clients who need it.

AI is advancing faster than regulation can keep up, and mental health is now at the heart of this debate. In August 2025, Illinois became the third U.S. state (after Utah and Nevada) to ban the use of AI in therapy decision-making. The law prohibits licensed therapists from using AI for diagnosis, treatment planning, or direct client communication. Companies are also barred from marketing “AI therapy” services that bypass licensed professionals (Washington Post, 2025; NY Post, 2025). This move reflects growing concerns about “AI psychosis,” misinformation, and the lack of accountability when vulnerable people turn to chatbots for therapy. Why This Matters for Therapists Everywhere Even if you don’t practice in Illinois, the ripple effects are significant. Regulations often start locally before spreading nationally—or globally. It raises key questions for all of us: What’s Still Allowed Importantly, the Illinois law doesn’t ban AI altogether. Therapists may still use AI for: What’s explicitly prohibited is letting AI act as the therapist. This distinction reinforces what many of us already believe: AI can support our work—but empathy, relational attunement, and clinical reasoning cannot be automated. Therapist Responsibility: Transparency and Boundaries With or without regulation, therapists should: The Bigger Picture: Advocacy and Ethics While some view bans as overly restrictive, they reflect real concerns about client safety and misinformation. Rather than rejecting AI outright, therapists can play an advocacy role—helping shape policies that strike a balance between innovation and protection. We can imagine a future where regulators, clinicians, and developers collaborate to define “safe zones” for AI use in therapy. For example, AI could continue to support therapists with data organization, early screening cues, and progress tracking—while humans remain the ultimate decision-makers. Takeaway Roadmap: “Using AI Without Crossing the Line” Here’s a simple three-step check-in for ethical AI use: Final Thoughts The Illinois ban isn’t about shutting down technology—it’s about drawing clearer boundaries to protect vulnerable clients. For therapists, the message is simple: AI can be a tool, but never the therapist. As the legal landscape evolves, staying proactive, transparent, and ethical will ensure we keep both innovation and humanity at the heart of our practice.

When AI Steps In—and When It Steps Over the Line In recent months, AI chatbots like ChatGPT have surged in popularity as a source of mental health support, largely due to accessibility, affordability, and the promise of immediate responses. While these tools can offer meaningful assistance, troubling incidents have highlighted the limitations of AI and reinforced that it is not a replacement for trained mental health professionals. Real Cases That Raised Alarms Some recent events have drawn urgent attention to the risks of unsupervised AI in mental health. In one case, a 16-year-old tragically died by suicide after extensive interactions with ChatGPT. Reports suggest that the chatbot failed to direct him toward professional help and may have inadvertently reinforced harmful behavior. Similarly, a man in Connecticut allegedly committed a murder-suicide after ChatGPT appeared to amplify delusional beliefs regarding his mother. Psychiatrists have described instances of “AI psychosis,” where prolonged interaction with AI chatbots contributed to delusional or psychosis-like symptoms among vulnerable adults. These cases are stark reminders that AI, while capable of simulating empathy, lacks the nuanced understanding, ethical judgment, and crisis awareness inherent to human-led mental health care. The Benefits—and the Balance Despite these serious concerns, AI support tools can provide meaningful benefits. Chatbots can offer low-cost, immediate support for individuals experiencing mild distress or who face barriers to traditional therapy, such as financial constraints, geographic limitations, or social stigma. Trials of AI-driven tools indicate modest reductions in symptoms of depression and anxiety for mild-to-moderate cases, showing that AI can serve as a valuable adjunct rather than a replacement. Clinicians have also found AI useful for administrative and psychoeducational tasks, allowing them to dedicate more time to person-centered care. Yet, these advantages are contingent upon thoughtful use, clear boundaries, and professional oversight. Risks and Ethical Considerations AI’s limitations are clear. Emotional overattachment to chatbots may reinforce harmful beliefs, while privacy concerns and a lack of confidentiality create systemic risks. Critically, AI may mismanage crises, provide inaccurate or “hallucinated” advice, and fail to detect nonverbal cues and complex emotional signals. Without ethical safeguards, these tools can exacerbate vulnerability instead of alleviating it. Legislative action in several states has begun addressing these risks by restricting AI therapy use without licensed professional oversight. Proposed regulations emphasize the need for human supervision, accurate marketing, and clearly defined boundaries between administrative support and therapeutic guidance. Developers and AI engineers play a crucial role as well. They can design safer systems by integrating crisis detection protocols, employing human-in-the-loop review models, and avoiding anthropomorphic language that may create undue emotional dependence. Therapists, too, have a key role in guiding clients to use AI responsibly, integrating outputs as prompts for discussion rather than definitive advice, and advocating for ethical AI development aligned with clinical practice. Summary: AI as a Tool, Not a Replacement AI chatbots have potential to expand access and provide interim support, particularly for underserved populations. However, recent tragedies illustrate the risks of unsupervised use. Thoughtful regulation, clinician involvement, ethical design, and public education are essential to ensure AI supplements, rather than replaces, human therapeutic care. By using AI responsibly, we can enhance access to mental health resources while preserving the core human connection that is central to effective therapy. References

Neurodegenerative disorders, such as Alzheimer’s and Parkinson’s disease, are becoming an increasing challenge worldwide, particularly as populations age. Early detection is crucial; the sooner these conditions are identified, the greater the potential for effective intervention. Artificial intelligence (AI) is rapidly emerging as a transformative ally for clinicians—not to replace their expertise, but to enhance decision-making, efficiency, and patient-centered care. A Growing Field: AI in Neurodegenerative Research Research in AI applications for neurodegenerative disorders has grown exponentially over the past decade. A bibliometric review analyzing over 1,400 publications from 2000 to early 2025 found a significant surge in studies since 2017, driven by advances in deep learning, neural networks, and multimodal data integration. The United States and China lead in research output, while the UK produces studies with the highest citation impact (Zhang et al., 2025). This growth underscores that AI is not a distant innovation—it is actively reshaping research and clinical practice today. Early Detection: Uncovering Subtle Signals One of AI’s most promising contributions is in the early identification of neurodegenerative disorders, often before traditional clinical signs become apparent. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) has demonstrated that deep learning applied to MRI scans and other biomarkers can identify Alzheimer’s disease with more than 95% accuracy and detect mild cognitive impairment with over 82% accuracy (Alzheimer’s Disease Neuroimaging Initiative, 2025). Further, narrative reviews suggest that multi-modal and longitudinal AI models outperform single-modality approaches, offering powerful prognostic insights. While these tools are promising, integrating them into clinical practice and improving interpretability remains a critical focus for researchers (Rudroff et al., 2024). AI is also being applied in novel non-invasive ways. For instance, ophthalmic imaging powered by AI can detect retinal nerve fiber layer thinning, a biomarker for Parkinson’s disease, with diagnostic accuracy reaching an AUC of 0.918 (Tukur et al., 2025). Integrating genetic, imaging, and clinical data through AI has the potential to reshape detection and management, enabling clinicians to intervene earlier and more accurately (Mikić et al., 2025). Beyond Detection: Supporting Clinicians and Enhancing Care AI’s value extends beyond diagnostics. Administrative tasks, particularly documentation, contribute significantly to clinician burnout, reducing time for patient interaction. AI is addressing this by streamlining workflows. For example, a study led by Mass General Brigham found that ambient AI documentation systems reduced physician burnout by 21.2% while increasing documentation-related well-being by 30.7% within a few months (Mass General Brigham-led study, 2025). Similarly, AI scribes at the Permanente Medical Group saved nearly 15,800 hours of documentation in one year, allowing clinicians to focus more on patient care (Permanente Medical Group, 2025). Cleveland Clinic reported that AI reduced average documentation time by two minutes per patient visit, improving interactions without sacrificing accuracy (Cleveland Clinic, 2025). These examples highlight a central principle: AI does not replace human care but enhances it, freeing mental energy for the relational and empathetic aspects of therapy. Does AI Slow Us Down? Some experts caution that overreliance on AI might erode diagnostic skills or reduce transparency in clinical decision-making (Patel, 2025). Yet, neuroscience offers a useful analogy: as the brain adapts to disease, it reorganizes into fewer but more efficient neural networks. AI functions similarly by handling repetitive tasks, allowing clinicians to conserve cognitive resources for critical reasoning, empathy, and therapeutic connection. Importantly, oversight by trained professionals ensures AI serves as a tool rather than a replacement. Integrating AI Thoughtfully and Ethically For AI to fulfill its promise responsibly, certain standards must be maintained. Tools should be validated across diverse patient populations to ensure fairness and generalizability (Zhang et al., 2025). Clinicians must be involved in tool development and receive training to interpret AI outputs accurately (Rudroff et al., 2024). Additionally, protecting patient privacy, mitigating bias, and maintaining clinician autonomy are essential to foster trust and ethical integration. When these safeguards are in place, AI becomes an amplifier of human expertise rather than a substitute, supporting clinicians to deliver more precise, efficient, and compassionate care. Conclusion AI is increasingly shaping the landscape of neurodegenerative care—from early detection and predictive modeling to reducing administrative burdens. Its goal is not to replace clinicians but to empower them to detect disease earlier, work more efficiently, and maintain a human-centered approach to care. By thoughtfully integrating AI into clinical practice, we can preserve the most important aspect of therapy: the connection between clinician and patient. References Alzheimer’s Disease Neuroimaging Initiative. (2025). Diagnosis and prediction of Alzheimer’s from neuroimaging using deep learning. Wikipedia. https://en.wikipedia.org/wiki/Alzheimer%27s_Disease_Neuroimaging_Initiative Cleveland Clinic. (2025, August). Less typing, more talking: AI reshapes clinical workflow at Cleveland Clinic. Cleveland Clinic Consult QD. https://consultqd.clevelandclinic.org/less-typing-more-talking-how-ambient-ai-is-reshaping-clinical-workflow-at-cleveland-clinic Mass General Brigham-led study. (2025, August 21). Ambient documentation technologies reduce physician burnout and restore ‘joy’ in medicine. Mass General Brigham Press Release. https://www.massgeneralbrigham.org/…burnout Mikić, M., et al. (2025). Public hesitancy for AI-based detection of neurodegenerative disorders. Scientific Reports. https://www.nature.com/articles/s41598-025-11917-8 Patel, A. (2025). The case for slowing down clinical AI deployment. Chief Healthcare Executive. https://www.chiefhealthcareexecutive.com/…deployment-viewpoint Permanente Medical Group. (2025, June). AI scribes save 15,000 hours—and restore the human side of medicine. AMA News Wire. https://www.ama-assn.org/…medicine Rudroff, T., Rainio, O., & Klén, R. (2024). AI for the prediction of early stages of Alzheimer’s disease from neuroimaging biomarkers—A narrative review of a growing field. arXiv. https://arxiv.org/abs/2406.17822 Tukur, H. N., et al. (2025). AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases. International Journal of Emergency Medicine, 18, Article 90. https://intjem.biomedcentral.com/articles/10.1186/s12245-025-00870-y Zhang, Y., Yu, L., Lv, Y., Yang, T., & Guo, Q. (2025). Artificial intelligence in neurodegenerative diseases research: A bibliometric analysis since 2000. Frontiers in Neurology. https://doi.org/10.3389/fneur.2025.1607924