Quantum Computing in Medicine

Medical Sciences, Год журнала: 2024, Номер 12(4), С. 67 - 67

Опубликована: Ноя. 17, 2024

Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible classical computers. This review paper provides detailed account of the current state QC, with particular focus on its applications within medicine. It explores fundamental concepts such as qubits, superposition, and entanglement, well evolution QC from theoretical foundations to practical advancements. The covers significant milestones where has intersected medical research, including breakthroughs drug discovery, molecular modeling, genomics, diagnostics. Additionally, key quantum techniques algorithms, machine learning (QML), quantum-enhanced imaging explained, highlighting their relevance healthcare. also addresses challenges field, hardware limitations, scalability, integration clinical environments. Looking forward, discusses potential quantum–classical hybrid systems emerging innovations hardware, suggesting how these advancements may accelerate adoption research practice. By synthesizing reliable knowledge presenting it through comprehensive lens, this serves valuable reference researchers interested transformative

Язык: Английский

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Proactivity of Chatbots, Task Types and User’s Characteristics When Interacting with Artificial Intelligence (AI) Chatbots

International Journal of Human-Computer Interaction, Год журнала: 2025, Номер unknown, С. 1 - 19

Опубликована: Янв. 8, 2025

The advancement of artificial intelligence technology has enabled chatbots to mimic human conversations the point being nearly indistinguishable from humans. This study investigates impact chatbot proactivity on user experience, considering task types and characteristics. Experiments were conducted using proactive non-proactive for business-related non-business-related tasks, evaluating characteristics (age, gender, AI experience level, education personality traits) (perceived usefulness, perceived ease use, satisfaction, trust). results showed that positively evaluated in terms trust, but no significant difference was found use compared chatbots. Additionally, traits (extraversion, agreeableness) significantly influenced experience. highlights critical role providing satisfying interaction experiences emphasizes importance designing

Язык: Английский

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Developing Effective Frameworks for Large Language Model–Based Medical Chatbots: Insights From Radiotherapy Education With ChatGPT

JMIR Cancer, Год журнала: 2025, Номер 11, С. e66633 - e66633

Опубликована: Фев. 18, 2025

This Viewpoint proposes a robust framework for developing medical chatbot dedicated to radiotherapy education, emphasizing accuracy, reliability, privacy, ethics, and future innovations. By analyzing existing research, the evaluates performance identifies challenges such as content bias, system integration. The findings highlight opportunities advancements in natural language processing, personalized learning, immersive technologies. When designed with focus on ethical standards large model–based chatbots could significantly impact education health care delivery, positioning them valuable tools developments globally.

Язык: Английский

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Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review

Algorithms, Год журнала: 2025, Номер 18(3), С. 156 - 156

Опубликована: Март 9, 2025

Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, enable personalized treatments. While QC holds the potential accelerate optimization, drug discovery, genomic analysis as hardware capabilities advance, current implementations remain limited compared classical in many practical applications. Meanwhile, ML has already demonstrated significant success medical imaging, predictive modeling, decision support. Their convergence, particularly through (QML), presents opportunities for future advancements processing high-dimensional healthcare data improving clinical outcomes. This review examines foundational concepts, key applications, challenges of these technologies healthcare, explores their synergy solving problems, outlines directions quantum-enhanced decision-making.

Язык: Английский

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Towards trustworthy AI-driven leukemia diagnosis: A hybrid Hierarchical Federated Learning and explainable AI framework

Informatics in Medicine Unlocked, Год журнала: 2025, Номер unknown, С. 101618 - 101618

Опубликована: Янв. 1, 2025

Язык: Английский

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From awareness to integration: Mobile applications as tools in radiology education

Journal of Radiation Research and Applied Sciences, Год журнала: 2025, Номер 18(2), С. 101353 - 101353

Опубликована: Фев. 13, 2025

Язык: Английский

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Interpreting text corpora from androids-related stories using large language models: “Machines like me” by Ian McEwan in generative AI

Humanities and Social Sciences Communications, Год журнала: 2025, Номер 12(1)

Опубликована: Март 6, 2025

Язык: Английский

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Assessing AI in Various Elements of Enhanced Recovery After Surgery (ERAS)-Guided Ankle Fracture Treatment: A Comparative Analysis with Expert Agreement

Journal of Multidisciplinary Healthcare, Год журнала: 2025, Номер Volume 18, С. 1629 - 1638

Опубликована: Март 1, 2025

This study aimed to assess and compare the performance of ChatGPT iFlytek Spark, two AI-powered large language models (LLMs), in generating clinical recommendations aligned with expert consensus on Enhanced Recovery After Surgery (ERAS)-guided ankle fracture treatment. aims determine applicability reliability AI supporting ERAS protocols for optimized patient outcomes. A qualitative comparative analysis was conducted using 35 structured questions derived from Expert Consensus Optimizing Ankle Fracture Treatment Protocols under Principles. Questions covered preoperative preparation, intraoperative management, postoperative pain control rehabilitation, complication management. Responses Spark were independently evaluated by experienced trauma orthopedic specialists based relevance, consistency consensus, depth reasoning. demonstrated higher alignment (29/35 questions, 82.9%), particularly comprehensive perioperative recommendations, detailed medical rationales, treatment plans. However, discrepancies noted blood pressure management antiemetic selection. 22/35 (62.9%), but responses often more generalized, less clinically detailed, occasionally inconsistent best practices. Agreement between observed 23/35 (65.7%), generally exhibiting greater specificity, timeliness, precision its recommendations. LLMs, ChatGPT, show promise decision-making ERAS-guided While provided accurate contextually relevant responses, inconsistencies highlight need further refinement, validation, integration. Spark's lower conformity suggests potential differences training data underlying algorithms, underscoring variability AI-generated advice. To optimize AI's role care, future research should focus enhancing guidelines, improving model transparency, integrating physician oversight ensure safe effective applications.

Язык: Английский

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Ethical and Privacy Considerations in AI-Driven Language Learning

LatIA, Год журнала: 2025, Номер 3, С. 328 - 328

Опубликована: Март 29, 2025

Artificial intelligence (AI) has revolutionized language learning by enabling personalized and adaptive education; however, these advancements also raise ethical privacy concerns, including algorithmic bias, data security risks, a lack of transparency in AI-driven decision-making. This study examines challenges, focusing on fairness, linguistic diversity, the balance between automated human instruction, with goal proposing guidelines for responsible adoption AI education. Through literature review comparative analysis, risks AI-powered tools were explored, assessing bias detection algorithms, frameworks, privacy-preserving techniques to identify best practices. The findings indicate that tend exhibit biases disadvantage underrepresented groups, raising concerns about fairness while exposing due inadequate measures. Implementing frameworks incorporate fairness-aware explainable models, robust protection mechanisms enhances user trust security. Therefore, addressing issues is essential ensuring integration education, where hybrid approach combining instruction emerges as most solution. Lastly, future research should focus regulatory compliance models strengthen ethics

Язык: Английский

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Copilot in service: Exploring the potential of the large language model-based chatbots for fostering evaluation culture in preventing and countering violent extremism

Open Research Europe, Год журнала: 2025, Номер 5, С. 65 - 65

Опубликована: Апрель 17, 2025

Background The rapid advancement in artificial intelligence (AI) technology has introduced the large language model (LLM)-based assistants or chatbots. To fully unlock potential of this for preventing and countering violent extremism (P/CVE) field, more research is needed. This paper examines feasibility using chatbots as recommender systems to respond practitioners’ needs evaluation, increase their knowledge about key evaluation aspects, provide practical guidance professional support process. At same time, provides an overview limitations that such solution entails. Methods explore performance LLM-based we chose a publicly available AI assistant called Copilot example. We conducted qualitative analysis its responses 50 pre-designed prompts various types. study was driven by questions established accuracy reliability, relevance integrity, well readability comprehensiveness responses. derived aspects evidence-based along with from results H2020 INDEED project. Results Our findings indicate demonstrated significant proficiency addressing issues related P/CVE. Most generated were factually accurate, relevant, structurally sound, i.e. sufficient kick-start deepen internal practise. biases data security inherent should be carefully explored practitioners. Conclusions underscored both fostering culture While can effectively generate accessible, informative encouraging recommendations, it still requires oversight manage coordinate process, address field-specific needs. future focus on rigorous user-centred assessment P/CVE use based multidisciplinary efforts.

Язык: Английский

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