Background

According to traditional Chinese medicine (TCM) theory, acupoints are specifically chosen sites of acupuncture manipulation, and also the basis for studying the mechanism of acupuncture. Stimulating different acupoints on the body surface could provide various therapeutic benefits. However, what is the acupoint? This question is not clear.



Review Summary

We focuse on examining the function of acupoints from different perspectives, including the local and the systemic effects of stimulating acupoints. For example, acupoints may release certain substances or incur some changes, which could adjust the function of organs, maintain homeostasis. Furthermore, the therapeutic effects of verum acupoints versus sham acupoints were discussed. However, due to insufficience in evidence and in current methodologies, research into mechanisms of acupuncture is still incomplete.



Conclusion

This review might explain, to some extent, what an acupoint is. Further research into the identity of acupoints is warranted, and multidisciplinary methods using novel technologies may yield significant advances over existing knowledge.

/pdf/what-is-the-acupoint-a-preliminary-review-of-acupoints-1nwcb8snnt.pdf

What is the Acupoint? A preliminary review of Acupoints

Intelligent diagnosis processes rely on Artificial Intelligence (AI) techniques to provide possible diagnoses by analyzing patient data and medical information. To make accurate and quick diagnoses, it is possible to use AI tools to efficiently analyze huge amounts of data and find patterns that a clinician might miss. In recent years, new large language models (LLMs), such as ChatGPT and Google BARD, have shown remarkable capabilities in several domains, including intelligent diagnostics. This research aims to compare the performances of ChatGPT and traditional machine learning models for making diagnoses of low- and medium- risk diseases only based on their symptoms. On the basis of our study, we defined four research questions: RQ1) What are the benefits and limitations of using ChatGPT in intelligent diagnosis? RQ2) How do traditional machine learning approaches compare to ChatGPT for intelligent diagnosis? RQ3) How does ChatGPT compare with other LLMs and domain-specific natural language processing models in the intelligent diagnosis tasks?, and RQ4) What are the implications of the predictive models and ChatGPT for healthcare, and how can they be used to support people?. To answer these RQs, we first evaluate the performances of different engines of ChatGPT, also introducing a new prompt engineering methodology specifically tailored for achieving accurate diagnostic outcomes. Moreover, we compare these results with those achieved by different predictive models trained for intelligent diagnosis tasks, i.e., Google BARD, and two domain-specific NLP models. Finally, we propose a new interactive bot available for users that relies on the best-performing models evaluated in the previous steps. The experiments have been conducted using two medical datasets for disease prediction consisting of more than 100 symptoms associated with several diagnoses. 

Can ChatGPT provide intelligent diagnoses? A comparative study between predictive models and ChatGPT to define a new medical diagnostic bot

Biases in medical artificial intelligence (AI) arise and compound throughout the AI lifecycle. These biases can have significant clinical consequences, especially in applications that involve clinical decision-making. Left unaddressed, biased medical AI can lead to substandard clinical decisions and the perpetuation and exacerbation of longstanding healthcare disparities. We discuss potential biases that can arise at different stages in the AI development pipeline and how they can affect AI algorithms and clinical decision-making. Bias can occur in data features and labels, model development and evaluation, deployment, and publication. Insufficient sample sizes for certain patient groups can result in suboptimal performance, algorithm underestimation, and clinically unmeaningful predictions. Missing patient findings can also produce biased model behavior, including capturable but nonrandomly missing data, such as diagnosis codes, and data that is not usually or not easily captured, such as social determinants of health. Expertly annotated labels used to train supervised learning models may reflect implicit cognitive biases or substandard care practices. Overreliance on performance metrics during model development may obscure bias and diminish a model's clinical utility. When applied to data outside the training cohort, model performance can deteriorate from previous validation and can do so differentially across subgroups. How end users interact with deployed solutions can introduce bias. Finally, where models are developed and published, and by whom, impacts the trajectories and priorities of future medical AI development. Solutions to mitigate bias must be implemented with care, which include the collection of large and diverse data sets, statistical debiasing methods, thorough model evaluation, emphasis on model interpretability, and standardized bias reporting and transparency requirements. Prior to real-world implementation in clinical settings, rigorous validation through clinical trials is critical to demonstrate unbiased application. Addressing biases across model development stages is crucial for ensuring all patients benefit equitably from the future of medical AI. 

Bias in medical AI: Implications for clinical decision-making

The efficacy of acupressure therapy in managing urodynamic stress incontinence has not been fully investigated. This study evaluates the effects of a validated acupressure protocol for treating women with urodynamic stress incontinence. A randomized, single-blinded, placebo-controlled trial with a before-and-after study design was conducted. Eighty-one women aged between 18 and 60 were recruited at a urogynecology clinic in an acute hospital in Hong Kong. Participants were randomly assigned to an intervention (acupressure) group, a sham group, or a usual care (control) group. A validated acupressure protocol was employed in the intervention group. The intervention comprised three weekly sessions of 30 minutes each for a total of thirty sessions. Outcome measures included pelvic floor muscle strength, number of episodes of urine leakage over four days, self-reported severity of urine leakage, and responses on the Chinese version of the King's Health Questionnaire. No adverse effects were reported. Five percent of the participants dropped out of the study. Intention-to-treat analysis was conducted, the results indicated significant differences in pelvic floor muscle strength across the three groups (H =7.05, p =0.03) and in self-reported severity of urine leakage between the intervention and control groups (Z = -2.48, p = 0.01). Significant improvement in all domains of the Chinese version of the King's Health Questionnaire was evident in the intervention group. Acupressure is a simple and non-invasive intervention that appears to have positive physiological and psychological effects on women with urodynamic stress incontinence. The findings support acupressure as an intervention option for managing urodynamic stress incontinence in women.

Effect of Acupressure in Treating Urodynamic Stress Incontinence: A Randomized Controlled Trial

To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases.Narrative literature review.Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues.While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated. 

/pdf/ocular-images-based-artificial-intelligence-on-systemic-6iq8st9i.pdf

Ocular images-based artificial intelligence on systemic diseases

Background/aims This study evaluates the performance of the Airdoc retinal artificial intelligence system (ARAS) for detecting multiple fundus diseases in real-world scenarios in primary healthcare settings and investigates the fundus disease spectrum based on ARAS. Methods This real-world, multicentre, cross-sectional study was conducted in Shanghai and Xinjiang, China. Six primary healthcare settings were included in this study. Colour fundus photographs were taken and graded by ARAS and retinal specialists. The performance of ARAS is described by its accuracy, sensitivity, specificity and positive and negative predictive values. The spectrum of fundus diseases in primary healthcare settings has also been investigated. Results A total of 4795 participants were included. The median age was 57.0 (IQR 39.0–66.0) years, and 3175 (66.2%) participants were female. The accuracy, speciﬁcity and negative predictive value of ARAS for detecting normal fundus and 14 retinal abnormalities were high, whereas the sensitivity and positive predictive value varied in detecting different abnormalities. The proportion of retinal drusen, pathological myopia and glaucomatous optic neuropathy was significantly higher in Shanghai than in Xinjiang. Moreover, the percentages of referable diabetic retinopathy, retinal vein occlusion and macular oedema in middle-aged and elderly people in Xinjiang were significantly higher than in Shanghai. Conclusion This study demonstrated the dependability of ARAS for detecting multiple retinal diseases in primary healthcare settings. Implementing the AI-assisted fundus disease screening system in primary healthcare settings might be beneficial in reducing regional disparities in medical resources. However, the ARAS algorithm must be improved to achieve better performance. Trial registration number NCT04592068.

/pdf/application-of-artificial-intelligence-system-for-screening-1ba2w1wp.pdf

Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases

Clinical application of the point baihui.

Anti-Glaucoma Effects of Timolol and Bimatoprost in Novel Ocular Hypertension Model in Rats

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Application of artificial intelligence system for screening multiple fundus diseases in Chinese primary healthcare settings: a real-world, multicentre and cross-sectional study of 4795 cases

Clinical application of the point baihui.

Anti-Glaucoma Effects of Timolol and Bimatoprost in Novel Ocular Hypertension Model in Rats