Revolutionizing Healthcare: The Transformative Impact of LLMs in Medicine

Large language models (LLMs) are rapidly advancing medical AI, offering revolutionary changes in healthcare. These models excel in natural language processing, enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs like GPT-4 and BERT demonstrate LLMs' evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in healthcare.


Table of Contents
mitigating biases while maintaining privacy and accountability.The paper emphasizes the need for humancentric, bias-free LLMs for personalized medicine and advocates for equitable development and access.LLMs hold promise for transformative impacts in healthcare.

Introduction
Recent advancements in artificial intelligence (AI) have catalyzed the development and significant breakthroughs of large language models (LLMs), now at the forefront of AI research [1][2][3][4].These models leverage deep learning algorithms to interpret and generate natural language, employing extensive corpus data to enhance pre-trained language models (PLMs), a cornerstone of natural language processing (NLP) [5,6].
Characterized by their immense scale, these models often consist of hundreds of millions to billions of parameters and are trained on vast textual datasets [7,8].Their ability to efficiently process natural language data with minimal human intervention, capturing intricate grammatical structures, lexical nuances, and semantic contexts, is noteworthy.Globally recognized LLMs include the ChatGPT series, BERT, PaLM, LaMDA, and Meta's Llama series, with China contributing models such as Baidu's 'Wenxin Yiyan', 360's LLM, Alibaba's 'Tongyi Qianwen', and SenseTime's LLM [9].The evolution of LLMs represents over seven years of relentless technological innovation and research, marking a significant milestone in AI development since the inception of the Turing machine.
LLMs primarily function to comprehend, generate, and interact through language.In NLP tasks such as text classification, named entity recognition, and sentiment analysis, their proficiency is unparalleled [10][11][12].
Beyond these applications, LLMs are expanding their influence.In mathematics, they assist in solving complex problems and contributing to mathematical proofs [13].In software development, their capabilities include automatic code generation, debugging assistance, and complex algorithm explanation [14].
In the medical domain, LLMs are poised to revolutionize clinical decision support.They can assist healthcare professionals in diagnosing diseases with enhanced accuracy and speed, provide treatment recommendations, and facilitate the analysis of medical records by processing large volumes of medical data [17][18][19][20].They are instrumental in swiftly navigating vast medical literature, providing healthcare professionals with essential research, guidelines, and information, thus saving time and grounding medical treatments in current knowledge [21][22][23][24][25]. Additionally, LLMs can interact directly with patients, offer medical consultations, and handle document processing efficiently [26][27][28].Their role in drug research and development is also emerging, aiding in new drug discoveries through detailed analysis of chemical and biological data [29,30].As such, LLMs are reshaping research methodologies and applications across various fields, particularly in medicine, equipping doctors with advanced tools for more accurate and efficient diagnosis and treatment, while offering patients more convenient and effective medical services.The potential for broader applications of LLMs in the medical field is vast, and there is a strong rationale to expect their significant impact on future healthcare advancements.(Fig1)

LLM Technical Background and Hardware Infrastructure
The evolution of LLMs like OpenAI's GPT-3 and Google's BERT has been monumental, driven by advancements in AI chip computing power and large, high-quality datasets [31].The Transformer model, introduced by Google in 2016, underpins this progress, predicting words in sentences based on statistical Tang et al correlations [32,33].Notably, GPT-3 in 2020 showcased the significance of model size and data quality.
The operation and training of LLMs, such as ChatGPT, require substantial hardware infrastructure [34].
This includes GPUs or TPUs with thousands of cores, extensive RAM (several terabytes), over 48 GB of VRAM on GPUs, high-performance SSDs, and fast, low-latency networks (10 to 100 Gbps) [35,36].Effective cooling systems and reliable power supplies are also essential.Compatibility with software frameworks like TensorFlow and PyTorch is necessary for optimizing training and deployment.The training of GPT-3, for instance, cost around USD 1.4 million, and operational costs for models like ChatGPT can reach up to USD 700,000 daily, with significant energy consumption.
Future technology advancements are expected to reduce the costs and improve the efficiency of LLMs.
Progress in GPU and TPU technologies, along with hardware tailored for LLM training, will drive efficiency.
Compact model structures through knowledge distillation, model pruning, transfer learning, energy-efficient practices, distributed training, and edge computing are anticipated.Semi-supervised and self-supervised learning methods will also play a role in training models with fewer labeled datasets [37,38].ChatGPT's recent updates showcase improvements in response speed, handling complex queries, multimodal functionality, global language support, and enhanced privacy and security measures [39] .
In healthcare, deploying large-scale medical models faces unique challenges due to data security and privacy concerns.Hospitals typically have CPUs for general computing, with limited access to GPUs.Medical LLMs, generally smaller than general-purpose LLMs, still require substantial investment in operational hardware [40, 41] .For instance, a model with 13 billion parameters might cost under RMB 1 million, while larger models for entire hospitals may require advanced GPU solutions costing around RMB 10 million.
Effective deployment demands careful consideration of model scale, computational resources, data security, and cost control.(Fig2)

Fig 2. The Architectural Designs of Large Language Models: A Study of Self-Attention Mechanisms and Structural Variations
The image depicts the hardware infrastructure for large language models and their implementation in the BERT and GPT models.On the left, there is a network diagram showing servers and computing devices needed to run these models, labeled with hardware such as TPU, and GPU.On the right, the structure of BERT and GPT is compared in detail, including positional encoding, self-attention mechanisms, feed-forward networks, addition and normalization layers, and the computation of output probabilities.Although these models have different approaches to processing text, both are large neural network models based on deep learning and self-attention mechanisms.

3.Advancing the Integration of LLMs in Healthcare: The Imperative for Evidence-Based Research and Collaborative Evaluation
In the contemporary healthcare landscape, the paradigm of evidence-based medicine (EBM) is instrumental in shaping medical decision-making processes.This methodology integrates top-tier research evidence with clinical expertise and aligns it with patient values and expectations, thereby informing patient care decisions.EBM ensures that medical interventions are grounded in scientific evidence rather than solely relying on a physician's experience or intuition, enhancing patient safety and the efficacy of treatments [42][43][44][45].
The integration of LLMs into the medical field introduces a significant challenge: the current scarcity of evidence-based medical research concerning the application of LLMs in healthcare settings [46].Although LLMs have shown remarkable efficacy in various sectors, the unique context of medicine, with its direct implications for human life and health, necessitates a cautious approach to the introduction of untested technologies or methods into clinical practice [47].Despite their robust data processing capabilities, LLMs present a potential risk for prediction errors in clinical environments.The medical domain, with its complex interplay of biology, physiology, and pathology, might be challenging for machine learning models to fully encapsulate, especially considering the intricacies and variability inherent in medical data [48].Furthermore, the realm of medical decision-making often requires a high level of expertise and experience, aspects that may not be entirely replicable by LLMs.The consequences of medical decisions far surpass those in other sectors, where a misdiagnosis or incorrect treatment recommendation could directly jeopardize a patient's life.To accelerate the empirical evaluation of LLMs in the medical field, fostering collaboration between medical institutions, research organizations, and technology companies is essential.This interdisciplinary collaboration ensures the comprehensiveness and quality of the research, facilitating the rapid advancement and application of LLM technologies.To enhance the transparency and trustworthiness of LLMs in healthcare, it is vital to publicize research findings and engage a broader spectrum of doctors and researchers in the evaluation and verification process.Such an approach ensures that LLMs' application in the medical field is underpinned by scientific rigor, is safe, and genuinely benefits both patients and the healthcare system.

Integrated Application of LLMs in Medical System
As we witness ongoing advancements in medical technology, the integration of LLMs with other tools and platforms within healthcare systems becomes increasingly crucial [50].
This fusion provides healthcare professionals with powerful tools to process, analyze, and effectively utilize vast amounts of healthcare data [23,[51][52][53][54].The integration of LLMs, such as ChatGPT, into medical systems has the potential to drive transformative progress in healthcare delivery.Firstly, LLMs can potentially enhance diagnostic accuracy and clinical decision-making by analyzing comprehensive medical data to identify relevant information and suggest potential diagnoses based on presented symptoms [55][56][57].Secondly, their proficiency in text processing and generation assists medical professionals in efficiently summarizing medical literature, facilitating research, and improving communication between healthcare providers and patients [58][59][60][61].The rapid adoption of readily available LLMs, such as ChatGPT, within the medical community, signifies recognition of their potential to transform healthcare delivery [62][63][64][65][66][67][68].
However, the application of LLMs in clinical settings is not without challenges [69].A primary concern is the generalizability of these models.Although LLMs have shown outstanding performance in numerous standard tasks, the complexity and diversity of the medical field suggest that these models may be susceptible to prediction errors in real clinical scenarios.Such errors can have serious implications, particularly when they influence critical health and life decisions.Additionally, the medical field encompasses a vast array of domainspecific knowledge that might exceed the training scope of LLMs, potentially leading to misunderstandings in complex medical scenarios.
Despite these challenges, the potential benefits and impact of LLMs in healthcare are considerable.LLMs can notably enhance the efficiency of medical workflows by automating routine processes, such as appointment scheduling, diagnosis, and report generation [70].
Their data-driven recommendations provide powerful decision support to doctors, assisting them in making more accurate and timely decisions.Current digital health workflows often burden physicians with extensive data entry, querying, and management tasks, leading to information overload and fatigue.LLMs can alleviate these burdens by automating these tasks, thereby saving valuable time for healthcare providers.Moreover, by analyzing and integrating patients' medical data, LLMs can offer tailored diagnosis and treatment recommendations, improving the overall quality of healthcare delivery.LLMs also play a crucial role in enhancing doctor-patient interactions.Leveraging natural language processing technology, they can better comprehend patients' needs and concerns, offering more personalized medical advice [71].This not only boosts patient satisfaction but also enhances the overall effectiveness of medical services.analyzing epidemic trends and hospital infection rates.At the community level, LLM can be used to predict public health crises, manage vaccination campaigns, coordinate community health initiatives, and analyze population health data to improve health policy.

Multimodal LLMs in Real-world Medical Scenarios
The advent of multimodal LLMs is bringing about a paradigm shift in the medical field, offering the capability to process and generate diverse data types, thereby unlocking unprecedented potential [72][73][74][75].To understand their role, it's essential to define what multimodal LLMs entail.These models are adept at processing, interpreting, and generating a wide array of data types, including but not limited to text, images, sounds, and more.This versatility implies that within the medical arena, LLMs are equipped to handle not only textual data, such as patient medical records, diagnostic reports, and research papers, but also to interpret and analyze medical imaging data like MRIs, CT scans, and X-rays, as well as other forms of data like voice recordings or biomarkers.
The utility of LLMs is increasingly becoming a focal point in medical imaging [76][77][78].
For instance, when a patient undergoes an MRI or CT scan, an LLM can swiftly analyze and integrate the image data with the patient's textual medical records, thereby providing more comprehensive and detailed diagnostic insights.Additionally, LLMs have the capability to automatically identify and highlight crucial areas in medical images, thus providing clinicians with clear references that aid in identifying potential issues [79].Moreover, LLMs can generate automated image reports, offering initial interpretations and treatment suggestions based on the analyzed image data, significantly boosting the efficiency and accuracy of medical diagnoses and treatments.
Multimodal LLMs are revolutionizing the field of telemedicine, transforming the dynamics of doctor-patient interactions [55,80] Patients predominantly upload their medical images, voice recordings, or symptom descriptions using smart devices for real-time consultations with an LLM.However, this process is primarily self-managed by the patients and not yet fully integrated into the healthcare system.This approach not only alleviates the workload of medical professionals but also empowers patients to access medical consultation from the comfort of their homes or other remote locations, substantially enhancing the convenience and accessibility of medical services.Furthermore, the integration of LLMs with smart sensors and devices enables the continuous monitoring of patients' physiological data, such as heart rate and blood pressure, facilitating early detection and intervention for any health anomalies, thus significantly bolstering patient health management.
In summary, multimodal LLMs offer a novel and efficacious approach to diagnosis, treatment, and healthcare management.Their robust capabilities in data processing and integration allow medical professionals to deliver more precise and efficient services to patients.At the same time, these models enable patients to access medical advice and care with greater convenience.As these technologies continue to evolve and improve, their significance and impact in the medical field are expected to grow exponentially.(Fig4)

Fig 4. The Importance of Multimodal Large Language Models in Medical Applications
The central heart represents the cardiac health status of the human body.The surrounding circular icons depict various cardiac conditions, including coronary artery disease, hypertension, arrhythmia, heart failure, valvular heart disease, cardiomyopathy, and congenital heart defects.These conditions are detected and analyzed through different medical imaging and diagnostic technologies such as Electrocardiography, Heart Sounds, Echocardiogram, Coronary Angiography, Cardiac MRI, and Nuclear Cardiology.The results from these diagnostics are processed by an artificial intelligence system to determine the type and severity of cardiac disease, assisting physicians in formulating treatment plans.

The Key Role of LLMs for Medical Research
In the field of fundamental medical research, the capabilities of LLMs in AI are being increasingly recognized [81][82][83][84].LLMs can swiftly retrieve and organize crucial information from vast biomedical literature, providing researchers with an efficient tool to access and synthesize the latest research findings on specific drugs, diseases, or genes [85].In drug discovery, LLMs can predict the activity, toxicity, and pharmacokinetic properties of new compounds, facilitating early-stage drug screening [86].These predictions not only save time, but also facilitate the early-stage screening of potential drug molecules.LLMs can use existing literature and databases to predict the potential functions of newly discovered genes,

Great Challenges of LLMs in Medical Scenarios and Feasible Roadmap
The integration of technology in healthcare invariably brings a mix of anticipation and challenges, particularly given its direct impact on human life and health.As a leading exemplar of current AI technology, LLMs present a complex array of opportunities and challenges in the medical field, warranting thorough exploration and discussion [88][89][90].
The handling of medical data, which represents some of the most private and sensitive information about individuals, is a paramount challenge for LLMs.The key lies in utilizing this data to enhance medical efficiency while safeguarding against misuse or leakage [82,91].
A potential technical solution involves anonymizing patient data, ensuring that neither processing nor transmission stages can be linked to specific individuals.Concurrently, medical organizations and technology providers must establish robust data management and access protocols, ensuring clear authorization and purpose for each data access.
Interpretive challenges loom large with LLMs in medicine.Medical decision-making is distinct from other fields due to its complexity and direct implications for patients' lives and health.When LLMs provide diagnostic or treatment suggestions, it is vital that the rationale behind these recommendations is transparent and comprehensible [92][93][94] .Developing mental models can aid LLMs in presenting their decision-making logic in a manner that is more accessible to human users.Leveraging deep learning and other machine learning technologies, LLMs can extract disease pathophysiological mechanisms from a vast corpus of medical literature and data, providing a scientific basis for their outputs.To further enhance interpretability, LLMs could utilize visual tools like graphics and animations to clarify the logic and evidence underpinning their decisions to both physicians and patients [95,96].
The issue of technical bias and the possibility of generating misleading information or 'hallucinations' are inherent challenges in LLMs.The data sources for these models, often anonymized consultation data and online materials, are not uniform and vary in quality, sometimes containing erroneous samples.Fine-tuning LLMs based on such data may lead to biased or skewed medical recommendations [97,98].Addressing this requires rigorous data auditing and the establishment of continuous bias-correction mechanisms.To mitigate the risk of hallucinations, knowledge enhancement methods, such as integrating a knowledge retrieval library or search enhancement tools, can be beneficial.The LLM's responses can be crossreferenced with retrieved data to filter out inconsistencies with reality.Another approach involves reinforcement learning based on human feedback, where high-quality feedback is provided to fine-tune and correct model outputs in collaboration with medical experts [99,100].
The potential of AI to create 'information cocoons' through personalized content, potentially reinforcing biases, is another critical aspect that needs to be addressed, especially in the medical domain [101].AI technologies, including LLMs, in medicine require stringent In the era of information and intelligence within the medical field, the application of LLMs harbors immense potential [102].However, the accompanying challenges are equally noteworthy and merit careful consideration.The ongoing discourse should emphasize not only the deeper integration of LLMs into medical practice but also their alignment with both the professional needs of healthcare providers and the experiential needs of patients [103,104].
Incorporating the theory of mind into LLMs can significantly enhance their utility in the medical field.This concept, which involves understanding others' thoughts, feelings, and intentions, is crucial for fostering trust and empathy within healthcare interactions.Medicine is not solely a science; it is also an art, deeply influenced by each patient's unique emotional, value-based, and experiential landscape.An AI system endowed with the capability to appreciate and respond to these individual differences can offer more personalized and compassionate medical advice [105,106].By employing the theory of mind, LLMs can gain deeper insights into patients' inherent needs and respond with more attentive and empathetic advice [107][108][109].When LLMs can emulate the thoughts and feelings of both doctors and patients, their outputs transcend mere data; they become imbued with empathy and human care, enhancing the patient's treatment experience and fostering stronger trust and communication between doctors and patients.For example, in interactions with terminal patients, LLMs could suggest more compassionate communication strategies, aiding both doctors and patients in navigating these sensitive and complex situations.
LLMs can be synergistically combined with other advanced technologies, such as virtual reality (VR) and augmented reality (AR), to transform medical consultations into more immersive and informative experiences.This integration can provide patients with a deeper understanding of their health conditions, empowering them to make more informed decisions regarding their treatment.The evolution of LLMs is also contingent upon the development of efficient and precise algorithms capable of adeptly handling complex medical data, which is essential for accurate and timely medical decision-making.As technology progresses, the use of LLMs in the medical field is expected to become increasingly intelligent, efficient, and personalized, thereby enhancing not only the quality of medical services but also the overall patient experience and driving the evolution and transformation of the healthcare industry.
In our pursuit of technological progress, we must adhere to a fundamental principle: ensuring that technology is accessible to all.This is particularly pertinent in the context of LLM adoption, where it is crucial not to overlook those who may be marginalized by the technology gap [110,111].Whether addressing the needs of rural farmers or urban older adults, every individual should have the opportunity to benefit from LLMs.This broad adoption must span various geographical regions and encompass diverse languages and cultural contexts, catering to users speaking English, Chinese, or local dialects [112,113].Achieving this objective is not solely a technological challenge but also a social imperative.
We must ensure that the design and application of LLMs overcome language and cultural barriers, truly reaching and benefiting a diverse global populace.Additionally, addressing technology accessibility issues is vital.For individuals in technologically underserved areas or older adults unfamiliar with new technologies, simpler access methods and more userfriendly interfaces are needed to facilitate effortless use of LLMs.
While the potential of LLMs in healthcare is significant, realizing this potential requires ongoing research, innovation, and dedication.Continuous efforts are necessary to refine LLM technology continually and ensure its broad adoption across all sectors of society.We firmly believe that with sustained commitment, LLMs will catalyze transformative changes in healthcare, benefiting society at large.By championing technological inclusivity, we can not only enhance the quality and efficiency of medical services but also promote overall societal health and well-being.

Conclusion
The era of digitalization and informatization underscores the transformative potential of LLMs in medicine.The evolution of this technology signifies a paradigm shift in medical services, offering unique opportunities and challenges to the medical community.LLMs, with their advanced natural language processing capabilities, have a wide range of applications, including emergency triage, elderly care, and the enhancement of digital medical workflows.
As the diversity of medical data expands, LLMs' ability to process multimodal data will play a crucial role in enabling more precise, personalized medical diagnoses and treatments.
Despite the promising trajectory of LLMs in the medical field, several challenges need to be addressed.These include the need for more extensive empirical research to establish the models' reliability and interpretability, as well as addressing potential issues related to model 'hallucinations' or inaccuracies.However, the growth potential of LLMs in the medical arena is significant.They can enhance patient experiences through the integration of virtual reality (VR) and augmented reality (AR), offer comprehensive medical advice through multimodal research, and humanize doctor-patient interactions using the theory of mind.With ongoing advancements in algorithms and computational power, we anticipate considerable improvements in LLMs' processing speed and accuracy.
However, the path to technological advancement is not always linear.To ensure the benefits of LLMs are accessible to all, it is imperative to promote equitable development and address the digital divide, particularly for economically and technologically disadvantaged regions and groups.This goal requires the collective efforts of healthcare professionals, computer science experts, government regulatory bodies, patients, and their families.Such a collaborative approach will ensure that the application of LLM technology in the medical field genuinely contributes to the betterment of humanity, significantly enhancing health and wellbeing.

Fig 1 .
Fig 1. Timeline of Mainstream Large Language Models Commercially Available to the Public.The technological evolution of Large Language Models (LLMs), highlighting several key technologies and models.It includes Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) from the 1990s, Google's Transformer model introduced in 2017, Google's BERT (Bidirectional Encoder Representations from Transformers) model released in 2018, and the GPT series by OpenAI.Specific emphasis is placed on three major milestones: the first opensource LLM -GPT-2, and the first widely acclaimed LLM -GPT-3.These developments signify major advancements in LLMs within the field of natural language processing.
Hence, it is imperative to back any new technological innovation, including LLMs, with solid scientific evidence before they are implemented in medical practice.Currently, empirical studies examining the application of LLMs in the medical field are limited.This scarcity of research implies an inability to definitively assess the accuracy, reliability, and safety of LLMs within a healthcare context.To comprehensively understand the potential benefits and risks associated with LLMs in medicine, a more robust body of clinical research is required.This research should encompass randomized controlled trials, observational studies, and extensive collaborative research, which are critical to evaluate the clinical utility of LLMs accurately[49].

Fig 3 .
Fig 3. Integration of Large Language Models in Healthcare Systems Across Different Scales LLMs can assist in monitoring and analyzing patient health records, treatment plans and laboratory results at the individual bed level, while managing care schedules and facilitating doctor-patient communication.At the hospital level, LLM helps manage patient data, operational logistics, staff scheduling and resource allocation, while

aFig 5 .
Fig 5.The Crucial Role of Large Language Models in Medical Science: Bridging Basic Research and Clinical Trials This illustration highlights the versatile roles of Large Language Models (LLMs) in medical research.LLMs analyze medical texts to uncover trends and inform research directions, facilitate hypothesis generation, and enhance clinical trial designs.They personalize medicine through data-driven treatment plans and use predictive modeling to inform clinical trial outcomes.LLMs also streamline research by integrating data and maintaining regulatory compliance.They assist in medical communication, education, and evaluate the societal impact of clinical research.
scrutiny and continuous evaluation to align with the field's unique characteristics and ethical standards.Ensuring privacy protection, eliminating biases and discrimination, and establishing clear accountability are essential.The use of LLMs should be guided by respect for life, aiming to enhance patient wellbeing and treatment outcomes, without compromising individual interests.A continuous monitoring and evaluation system is crucial for assessing the effectiveness of LLMs and managing potential risks.Regulations should be regularly updated to keep pace with AI advancements, ensuring medical safety and patient rights.By prioritizing safety, fairness, and effectiveness, we can fully leverage LLMs and other AI technologies to facilitate a transformative revolution in medicine, while upholding human values and rights.

Fig1.
Fig1.Timeline of Mainstream Large Language Models Commercially Available to the Public Fig2.The Architectural Designs of Large Language Models: A Study of Self-Attention Mechanisms and Structural Variations

Fig3.
Fig3.Integration of Large Language Models in Healthcare Systems Across Different Scales Fig4.The Importance of Multimodal Large Language Models in Medical Applications Fig5.Potential of Large Language Models in Emergency Department Triage