Large language models encode clinical knowledge

Nature, Journal Year: 2023, Volume and Issue: 620(7972), P. 172 - 180

Published: July 12, 2023

Abstract Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess knowledge of typically rely on automated evaluations based limited benchmarks. Here, address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries new dataset questions searched online, HealthSearchQA. We propose human evaluation framework model answers along multiple axes including factuality, comprehension, reasoning, possible harm bias. In addition, evaluate Pathways Language Model 1 (PaLM, 540-billion parameter LLM) its instruction-tuned variant, Flan-PaLM 2 MultiMedQA. Using combination prompting strategies, achieves state-of-the-art accuracy every MultiMedQA multiple-choice (MedQA 3 , MedMCQA 4 PubMedQA 5 Measuring Massive Multitask Understanding (MMLU) topics 6 ), 67.6% MedQA (US Medical Licensing Exam-style questions), surpassing prior state art by more than 17%. However, reveals key gaps. To resolve this, introduce instruction prompt tuning, parameter-efficient approach aligning LLMs domains using few exemplars. The resulting model, Med-PaLM, performs encouragingly, remains inferior clinicians. show that recall reasoning improve with scale suggesting potential utility in medicine. Our reveal limitations today’s models, reinforcing importance both frameworks method development creating safe, helpful applications.

Language: Английский

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Multimodal biomedical AI

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(9), P. 1773 - 1784

Published: Sept. 1, 2022

Language: Английский

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Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare

Biosensors, Journal Year: 2022, Volume and Issue: 12(8), P. 562 - 562

Published: July 25, 2022

Artificial intelligence (AI) is a modern approach based on computer science that develops programs and algorithms to make devices intelligent efficient for performing tasks usually require skilled human intelligence. AI involves various subsets, including machine learning (ML), deep (DL), conventional neural networks, fuzzy logic, speech recognition, with unique capabilities functionalities can improve the performances of medical sciences. Such systems simplify intervention in clinical diagnosis, imaging, decision-making ability. In same era, Internet Medical Things (IoMT) emerges as next-generation bio-analytical tool combines network-linked biomedical software application advancing health. this review, we discuss importance improving IoMT point-of-care (POC) used advanced healthcare sectors such cardiac measurement, cancer diabetes management. The role supporting robotic surgeries developed applications also discussed article. position functionality, detection accuracy, ability devices, evaluation associated risks assessment carefully critically review. This review encompasses technological engineering challenges prospects AI-based cloud-integrated personalized designing POC suitable healthcare.

Language: Английский

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Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 12765 - 12795

Published: Jan. 1, 2023

The rapid progress in digitalization and automation have led to an accelerated growth healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. Metaverse is emerging technology the digital space which has huge potential enabling realistic experiences patients as well medical practitioners. a confluence of multiple technologies such artificial intelligence, virtual reality, augmented internet devices, robotics, quantum computing, etc. through directions providing quality healthcare services can be explored. amalgamation these ensures immersive, intimate personalized patient care. It also provides adaptive intelligent solutions eliminates barriers between providers receivers. This article comprehensive review emphasizing on state art, adopt applications, related projects. issues adaptation applications identified plausible highlighted part future research directions.

Language: Английский

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Spatial profiling technologies illuminate the tumor microenvironment

Cancer Cell, Journal Year: 2023, Volume and Issue: 41(3), P. 404 - 420

Published: Feb. 16, 2023

Language: Английский

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Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning

Nature Biomedical Engineering, Journal Year: 2022, Volume and Issue: 6(12), P. 1399 - 1406

Published: Sept. 15, 2022

In tasks involving the interpretation of medical images, suitably trained machine-learning models often exceed performance experts. Yet such a high-level typically requires that be with relevant datasets have been painstakingly annotated by Here we show self-supervised model on chest X-ray images lack explicit annotations performs pathology-classification accuracies comparable to those radiologists. On an external validation dataset X-rays, outperformed fully supervised in detection three pathologies (out eight), and generalized were not explicitly for training, multiple image-interpretation from institutions.

Language: Английский

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Innovative Materials Science via Machine Learning

Advanced Functional Materials, Journal Year: 2021, Volume and Issue: 32(1)

Published: Oct. 4, 2021

Abstract Nowadays, the research on materials science is rapidly entering a phase of data‐driven age. Machine learning, one most powerful methods, have been being applied to discovery and performances prediction with undoubtedly tremendous application foreground. Herein, challenges current progress machine learning are summarized in science, design strategies classified highlighted, possible perspectives proposed for future development. It hoped this review can provide important scientific guidance innovating technology via future.

Language: Английский

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Addressing fairness in artificial intelligence for medical imaging

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Aug. 6, 2022

A plethora of work has shown that AI systems can systematically and unfairly be biased against certain populations in multiple scenarios. The field medical imaging, where are beginning to increasingly adopted, is no exception. Here we discuss the meaning fairness this area comment on potential sources biases, as well strategies available mitigate them. Finally, analyze current state field, identifying strengths highlighting areas vacancy, challenges opportunities lie ahead.

Language: Английский

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CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection

2021 IEEE/CVF International Conference on Computer Vision (ICCV), Journal Year: 2023, Volume and Issue: unknown, P. 21095 - 21107

Published: Oct. 1, 2023

An increasing number of public datasets have shown a marked impact on automated organ segmentation and tumor detection. However, due to the small size partially labeled problem each dataset, as well limited investigation diverse types tumors, resulting models are often segmenting specific organs/tumors ignore semantics anatomical structures, nor can they be extended novel domains. To address these issues, we propose CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) models. This CLIPbased label encoding captures relationships, enabling model learn structured feature segment 25 organs 6 tumors. The proposed is developed an assembly 14 datasets, using total 3,410 CT scans for training then evaluated 6,162 external 3 additional datasets. We rank first Medical Segmentation Decathlon (MSD) leaderboard achieve state-of-the-art results Beyond Cranial Vault (BTCV). Additionally, Model computationally more efficient (6× faster) compared with dataset-specific models, generalized better scansfrom varying sites, shows stronger transfer learning performance tasks.

Language: Английский

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Deep learning-based meta-classifier approach for COVID-19 classification using CT scan and chest X-ray images

Multimedia Systems, Journal Year: 2021, Volume and Issue: 28(4), P. 1401 - 1415

Published: July 6, 2021

Language: Английский

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