A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Bioengineering, Год журнала: 2024, Номер 11(3), С. 219 - 219

Опубликована: Фев. 25, 2024

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning deep to large model paradigms, stand poised significantly augment physicians in rendering more evidence-based decisions, thus presenting pioneering solution for clinical practice. Traditionally, amalgamation of diverse data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative facilitate comprehensive disease analysis, topic burgeoning interest among both researchers clinicians recent times. Hence, there exists pressing need synthesize latest strides multi-modal AI technologies realm diagnosis. In this paper, we narrow our focus five specific disorders (Alzheimer’s disease, breast cancer, depression, heart epilepsy), elucidating advanced endeavors their treatment through lens artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying but also underscores commonly utilized public datasets, intricacies feature engineering, prevalent classification models, envisaged challenges future endeavors. essence, research contribute advancement methodologies, furnishing invaluable insights decision making.

Язык: Английский

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Prompt Engineering Paradigms for Medical Applications: Scoping Review

Journal of Medical Internet Research, Год журнала: 2024, Номер 26, С. e60501 - e60501

Опубликована: Сен. 10, 2024

Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in medical domain due specialized terminology and technicity. Clinical natural processing applications must navigate complex ensure privacy compliance. engineering offers a novel approach by designing tailored guide exploiting clinically relevant information from texts. Despite promise, efficacy prompt remains be fully explored.

Язык: Английский

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Large Language Models in Mental Health Care: A Systematic Scoping Review (Preprint)

Опубликована: Июль 8, 2024

The integration of large language models (LLMs) in mental health care is an emerging field. There a need to systematically review the application outcomes and delineate advantages limitations clinical settings. This aims provide comprehensive overview use LLMs care, assessing their efficacy, challenges, potential for future applications. A systematic search was conducted across multiple databases including PubMed, Web Science, Google Scholar, arXiv, medRxiv, PsyArXiv November 2023. All forms original research, peer-reviewed or not, published disseminated between October 1, 2019, December 2, 2023, are included without restrictions if they used developed after T5 directly addressed research questions From initial pool 313 articles, 34 met inclusion criteria based on relevance LLM robustness reported outcomes. Diverse applications identified, diagnosis, therapy, patient engagement enhancement, etc. Key challenges include data availability reliability, nuanced handling states, effective evaluation methods. Despite successes accuracy accessibility improvement, gaps applicability ethical considerations were evident, pointing robust data, standardized evaluations, interdisciplinary collaboration. hold substantial promise enhancing care. For full be realized, emphasis must placed developing datasets, development frameworks, guidelines, collaborations address current limitations.

Язык: Английский

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AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications

Applied Sciences, Год журнала: 2024, Номер 14(13), С. 5889 - 5889

Опубликована: Июль 5, 2024

Mental health disorders are a leading cause of disability worldwide, and there is global shortage mental professionals. AI chatbots have emerged as potential solution, offering accessible scalable interventions. This study aimed to conduct scoping review evaluate the effectiveness feasibility in treating conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, PsycNet, well using AI-powered tools like Microsoft Copilot Consensus. Relevant studies on chatbot interventions for were selected based predefined inclusion exclusion criteria. Data extraction quality assessment performed independently by reviewers. The yielded 15 eligible covering various application areas, such support during COVID-19, specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, promotion, usability assessments. demonstrated benefits improving emotional well-being, addressing conditions, facilitating behavior change. However, challenges related usability, engagement, integration with existing healthcare systems identified. hold promise interventions, but widespread adoption hinges systems. Enhancing personalization context-specific adaptation key. Future research should focus large-scale trials, optimal human–AI integration, ethical social implications.

Язык: Английский

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Chain-of-Interaction: Enhancing Large Language Models for Psychiatric Behavior Understanding by Dyadic Contexts

2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), Год журнала: 2024, Номер unknown, С. 392 - 401

Опубликована: Июнь 3, 2024

Язык: Английский

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Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis

Journal of Healthcare Informatics Research, Год журнала: 2024, Номер 8(4), С. 658 - 711

Опубликована: Сен. 14, 2024

Язык: Английский

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The Role of Artificial Intelligence in Transforming Mental Healthcare: Bridging Gaps, Enhancing Accessibility, and Personalizing Interventions

Опубликована: Янв. 1, 2025

Язык: Английский

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PIE: A Personalized Information Embedded model for text-based depression detection

Information Processing & Management, Год журнала: 2024, Номер 61(6), С. 103830 - 103830

Опубликована: Июль 16, 2024

Язык: Английский

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Prompt Engineering Paradigms for Medical Applications: Scoping Review (Preprint)

Опубликована: Май 14, 2024

Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in medical domain due specialized terminology and technicity. Clinical natural processing applications must navigate complex ensure privacy compliance. engineering offers a novel approach by designing tailored guide exploiting clinically relevant information from texts. Despite promise, efficacy prompt remains be fully explored. The aim study review research efforts technical approaches as well provide an overview opportunities challenges clinical practice. Databases indexing fields medicine, computer science, informatics were queried order identify published papers. Since emerging field, preprint databases also considered. Multiple data extracted, such paradigm, involved LLMs, languages study, topic, baselines, several learning, design, architecture strategies specific engineering. We include studies that apply engineering–based methods domain, between 2022 2024, covering multiple paradigms learning (PL), tuning (PT), design (PD). included 114 recent studies. Among 3 paradigms, we have observed PD most prevalent (78 papers). In 12 papers, PD, PL, PT terms used interchangeably. While ChatGPT commonly LLM, identified 7 using this LLM sensitive set. Chain-of-thought, present 17 studies, emerges frequent technique. PL papers typically baseline evaluating prompt-based approaches, 61% (48/78) do not report any nonprompt-related baseline. Finally, individually examine each key engineering–specific reported across find many neglect explicitly mention them, posing challenge advancing research. addition reporting trends scientific landscape guidelines future help advance field. disclose tables figures summarizing available hope contributions will leverage these existing works better

Язык: Английский

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Comparative analysis of BERT-based and generative large language models for detecting suicidal ideation: a performance evaluation study

Cadernos de Saúde Pública, Год журнала: 2024, Номер 40(10)

Опубликована: Янв. 1, 2024

Artificial intelligence can detect suicidal ideation manifestations in texts. Studies demonstrate that BERT-based models achieve better performance text classification problems. Large language (LLMs) answer free-text queries without being specifically trained. This work aims to compare the of three variations BERT and LLMs (Google Bard, Microsoft Bing/GPT-4, OpenAI ChatGPT-3.5) for identifying from nonclinical texts written Brazilian Portuguese. A dataset labeled by psychologists consisted 2,691 sentences 1,097 with ideation, which 100 were selected testing. We applied data preprocessing techniques, hyperparameter optimization, hold-out cross-validation training testing models. When evaluating LLMs, we used zero-shot prompting engineering. Each test sentence was if it contained according chatbot's response. Bing/GPT-4 achieved best performance, 98% across all metrics. Fine-tuned outperformed other LLMs: BERTimbau-Large performed a 96% accuracy, followed BERTimbau-Base 94%, BERT-Multilingual 87%. Bard worst 62% whereas ChatGPT-3.5 81%. The high recall capacity suggests low misclassification rate at-risk patients, is crucial prevent missed interventions professionals. However, despite their potential supporting detection, these have not been validated patient monitoring clinical setting. Therefore, caution advised when using evaluated as tools assist healthcare professionals detecting ideation.

Язык: Английский

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