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Related Concept Videos

Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Natural Language Processing and Large Language Models.

Gordon C Wong1, Kevin C Chung1

  • 1Section of Plastic Surgery, Department of Surgery, Michigan Medicine, The University of Michigan Health System, 1500 East Medical Center Drive, 2130 Taubman Center, SPC 5340, Ann Arbor, MI 48109-5340, USA.

Hand Clinics
|November 8, 2025
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Summary
This summary is machine-generated.

Natural language processing (NLP) and large language models (LLMs) can transform hand surgery by improving efficiency and patient care. Addressing current challenges will unlock significant advancements in the field.

Keywords:
ApplicationArtificial intelligenceChallengesLarge language modelNatural language processing

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Surgical Innovation

Background:

  • Natural language processing systems (NLPs) and large language models (LLMs) offer significant potential for advancing hand surgery.
  • These AI technologies can streamline clinical workflows, improve patient care, and enhance research capabilities within the specialty.

Purpose of the Study:

  • To explore the potential applications of NLPs and LLMs in hand surgery.
  • To identify the benefits and challenges associated with implementing these technologies in clinical practice and research.

Main Methods:

  • Review of current literature on NLP and LLM applications in healthcare.
  • Analysis of the specific capabilities of NLPs in processing unstructured clinical data.
  • Evaluation of LLM contributions to patient education, clinical decision support, and administrative task automation.

Main Results:

  • NLPs can enhance research efficiency, quality improvement, and patient safety by analyzing unstructured clinical data.
  • LLMs can improve patient education, provide real-time clinical decision support, and reduce administrative burdens through automated documentation.
  • Key challenges remain in fully realizing the benefits of these technologies in hand surgery.

Conclusions:

  • NLPs and LLMs hold transformative potential for hand surgery, impacting workflows, patient care, and research.
  • Overcoming implementation challenges is crucial for leveraging these AI advancements.
  • Successful integration promises significant progress in hand surgery and overall healthcare.