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Open research questions in Artificial Intelligence in Healthcare and Education

4,200 gap statements mined from Artificial Intelligence in Healthcare and Education papers in our 7.9M-paper local library, which holds 14,006 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The uneven performance of AI systems across high- and low-resource languages is a challenge. The lack of training in AI literacy and technological competences is a challenge to language access managers.

    AI in Language Access: Mapping Training Needs Through the Lens of Language Access Managers · 2026 · DOI

  • The lack of understanding of how the absence of AI disclosure varies with the functional depth of assistance.

    When AI Writes the Text: AI-Assistance Depth and Recorded Disclosure in University Application Essays · 2026 · DOI

  • The paper identifies a gap in traditional grounded theory, where manual coding is labor-intensive, time-consuming, and cognitively demanding. The study also highlights the limitations of computational approaches, such as LDA, which often produce decontextualized keyword clusters that lack semantic nuance and are difficult to interpret.

    LLM-enhanced computational grounded theory and the triadic dynamics of human-AI-society interaction · 2026 · DOI

  • The gap is the need for a uniform biomarker-supported reference standard for diagnostic accuracy studies. The current methods are limited by their reliance on trained personnel and time-intensive nature.

    Can speech-based AI transform cognitive impairment screening? · 2026 · DOI

  • The lack of accountability and oversight in AI-generated content is a significant gap in the current state of AI research. The paper identifies the need for careful human oversight and validation of AI-generated content. The authors highlight the risks of AI-enabled research misconduct, including the erosion of public trust and the waste of editorial and reviewer time.

    Artificial intelligence and large language models as a new threat to scientific truth – a call to action · 2026 · DOI

  • There is a lack of comprehensive comparisons of guideline-based clinical reasoning in the existing literature. Prior studies have predominantly examined the performance of LLMs in patient education or general clinical questions. There is a need to evaluate the performance of LLMs in interpreting evidence-based clinical guidelines for lumbar disc herniation with radiculopathy.

    Performance of large language models in interpreting evidence-based clinical guidelines for lumbar disc herniation with radiculopathy · 2026 · DOI

  • The paper identifies a gap in the current research process, where AI models can amplify scientific bias and erode accountability and ethical responsibility. The lack of rigorous standards for information sourcing and referencing can lead to an overrepresentation of positive findings, which can be further distorted by AI models.

    When AI agrees with us: large language models as amplifiers of scientific bias · 2026 · DOI

  • The lack of standardized metrics for evaluating AI agency in specialized surgical environments. The disconnect between probabilistic reasoning and a validated evidence base obstructs the clinical implementation of LLMs.

    Large language models in the vertical integration of spine surgery workflow: a scoping review · 2026 · DOI

  • The diagnostic reliability of emerging large language models for spinal pathologies remains understudied. There is a need to evaluate the performance of LLMs in detecting lumbar spondylolisthesis. The fidelity of labels in public access databases is a concern.

    Large language models for lumbar spondylolisthesis detection: a multi-center pilot comparative radiographic accuracy study · 2026 · DOI

  • The review highlights the need for more studies on the clinical benefit and safety of generative AI in chronic pain management. The paper notes that the current literature is limited by variable accuracy, readability, and limited external validation.

    Applications of generative artificial intelligence in chronic pain: a scoping review · 2026 · DOI

  • The lack of a comprehensive framework for responsible AI adoption in low-resource settings like Somalia. The need for a foundation-first approach that prioritizes reliable energy, inclusive connectivity, strong primary and community care, and accountable governance.

    Foundation first: a health-system readiness framework for responsible AI adoption in Somalia · 2026 · DOI

  • The challenge of reducing the gap between models is also highlighted. The study notes the challenge of ensuring that automation of triage tasks is safe and effective.

    Automating the triage of rheumatology outpatient referrals: a comparative evaluation of 23 large language models under simple and advanced prompting · 2026 · DOI

  • The study used a combination of convenience and snowball sampling, - The sample size was limited to 23…

    AI in Language Access: Mapping Training Needs Through the Lens of Language Access Managers · 2026 · DOI

  • Further research is needed to explore the applications of LLM-enhanced computational grounded theory, - The study suggests a need for clearer guidance on saturation assessment in large-scale computational workflows, - Future research could investigate the use of LLMs in other domains, such as healthcare…

    LLM-enhanced computational grounded theory and the triadic dynamics of human-AI-society interaction · 2026 · DOI

  • Infrastructure limitations, - environmental burdens, - equity and sovereignty concerns, - the need for robust impact evaluations and trained clinical…

    Sustainable artificial intelligence in global health: balancing promise, equity, and environmental realities in low- and middle-income countries · 2026 · DOI

  • The evidence base for clinical AI in low- and middle-income countries remains strikingly thin, - the lack of robust impact evaluations exposes health systems and patients to significant risks, - the absence of trained clinical staff can prevent effective human-in-the-loop…

    Sustainable artificial intelligence in global health: balancing promise, equity, and environmental realities in low- and middle-income countries · 2026 · DOI

  • The study did not involve human participants or patient data, - The research was conducted entirely using published educational material and simulated responses from artificial intelligence models, - The study used a limited number of clinical…

    AI for clinical neurology: Comparing five large language models in neurological diagnosis and lesion localization · 2026 · DOI

  • Investigating the performance of large language models in clinical workflows, - Evaluating the effectiveness of large language models in diagnosing and localizing lesions, - Comparing the performance of different large language models in various neurological…

    AI for clinical neurology: Comparing five large language models in neurological diagnosis and lesion localization · 2026 · DOI

  • The response to AI-enabled research/publishing misconduct must be two-pronged and involve both author consequences and upstream changes in research culture, - Institutions must address institutional research aims where key performance indicators reward volume over research…

    Artificial intelligence and large language models as a new threat to scientific truth – a call to action · 2026 · DOI

  • The evaluation relied on subjective scoring of accuracy and completeness, - The resulting inter-rater reliability was low, - The evaluation protocol lacked a formal pre-study rater calibration or training…

    Comparison of responses from google and large language models to the top frequently asked questions on lumbar spinal stenosis: an evaluation of accuracy and completeness · 2026 · DOI

  • Future hypotheses regarding the utility of LLMs for laypersons, - Further evaluation of LLMs in patient education and digital health information…

    Comparison of responses from google and large language models to the top frequently asked questions on lumbar spinal stenosis: an evaluation of accuracy and completeness · 2026 · DOI

  • Evaluations remain limited by unimodal text-only evaluations, - Current LLMs lack the capacity of autonomous critical appraisal, - Data contamination causes LLMs to oscillate between outdated protocols and recovery bias, - Longitudinal variability is introduced by the use of broad model identifiers without documenting specific version-locked API…

    Large language models in the vertical integration of spine surgery workflow: a scoping review · 2026 · DOI

  • The study used a single VinDr dataset which may limit external generalizability of findings - The study used the free versions of ChatGPT-4o and ChatGPT-5 - The study had a limited sample size of 200…

    Large language models for lumbar spondylolisthesis detection: a multi-center pilot comparative radiographic accuracy study · 2026 · DOI

  • Epistemic uncertainties of AI systems, - Lack of experiential and intersubjective participation in interpersonal…

    Toward an epistemic grounding of clinical reasoning: artificial intelligence and psychological diagnosis · 2026 · DOI

  • Further study on the integration of AI into clinical reasoning, - Investigation of the epistemic status of psychiatric…

    Toward an epistemic grounding of clinical reasoning: artificial intelligence and psychological diagnosis · 2026 · DOI

Most-cited papers in Artificial Intelligence in Healthcare and Education

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This page shows what the Artificial Intelligence in Healthcare and Education literature already flags as unresolved. To narrow it to your specific question, search the Research Gap Finder: the search is free with a free account and lists the papers closest to your topic first. Unlocking that topic (50 credits, charged once) fills the comparison table from our 7.9M-paper local library and writes the gaps from its rows.

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Open questions raised by papers listed on this page include: (1) The lack of training in AI literacy and technological competences is a challenge to language access managers (“AI in Language Access: Mapping Training Needs Through the Lens of Language…”, 2026); (2) The lack of understanding of how the absence of AI disclosure varies with the functional depth of assistance (“When AI Writes the Text: AI-Assistance Depth and Recorded Disclosure in…”, 2026); (3) The lack of accountability and oversight in AI-generated content is a significant gap in the current state of AI research (“Artificial intelligence and large language models as a new threat to scientific…”, 2026); (4) There is a lack of comprehensive comparisons of guideline-based clinical reasoning in the existing literature (“Performance of large language models in interpreting evidence-based clinical…”, 2026); (5) The lack of rigorous standards for information sourcing and referencing can lead to an overrepresentation of positive findings, which can be further distorted by AI models (“When AI agrees with us: large language models as amplifiers of scientific bias”, 2026). 4,200 gap statements have been mined from Artificial Intelligence in Healthcare and Education papers in our 7.9M-paper local library, which holds 14,006 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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