[Artificial intelligence and its applications in medicine I: introductory background to AI and robotics]

Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery

PURPOSE

This study aims predicting the stress level based on the ergonomic (kinematic) and physiological (electrodermal activity-EDA, blood pressure and body temperature) parameters of the surgeon from their records collected in the previously immediate situation of a minimally invasive robotic surgery activity.

METHODS

For this purpose, data related to the surgeon's ergonomic and physiological parameters were collected during twenty-six robotic-assisted surgical sessions completed by eleven surgeons with different experience levels. Once the dataset was generated, two preprocessing techniques were applied (scaled and normalized), these two datasets were divided into two subsets: with 80% of data for training and cross-validation, and 20% of data for test. Three predictive techniques (multiple linear regression-MLR, support vector machine-SVM and multilayer perceptron-MLP) were applied on training dataset to generate predictive models. Finally, these models were validated on cross-validation and test datasets. After each session, surgeons were asked to complete a survey of their feeling of stress. These data were compared with those obtained using predictive models.

RESULTS

The results showed that MLR combined with the scaled preprocessing achieved the highest R2 coefficient and the lowest error for each parameter analyzed. Additionally, the results for the surgeons' surveys were highly correlated to the results obtained by the predictive models (R2 = 0.8253).

CONCLUSIONS

The linear models proposed in this study were successfully validated on cross-validation and test datasets. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon's health during robotic surgery.

Content Validation of the Self-Medication Scale and Trust in Online Resources: Deepening Digital Access to Health

(1) Background: The rise of online health resources and AI has reshaped the diagnosis and treatment of disease, altering the relationship between patients and healthcare professionals and encouraging self-medication. However, questionnaires validated in the literature on self-medication seem to lack questions on the possible causes that induce such behaviors, including items that explore trust toward websites and artificial intelligence. The aim of this study is to design and validate the content of a questionnaire designed to assess self-medication in health professionals, delving into the underlying etiologies, the pharmacological categories involved and the degree of confidence placed in clinical assessments derived from AI tools. (2) Methods: Validation study of the content of an instrument consisting of two phases: 1. The content validation phase involved evaluation by a selected group of health experts, who, using a Likert-type scale, analyzed the clarity, coherence and relevance of the items. 2. Pilot study of health professionals who have indicated the practice of self-administration of medications. (3) Results: In the first phase of the study, the experts considered most of the questionnaire items to be clear, representative and consistent with the construct to be measured. In its second phase, the preliminary results of our pilot study suggest a significant trend toward self-medication among healthcare workers, along with a strong inclination to use online resources to search for health-related information. (4) Conclusions: The development of a validated instrument to measure the influence of the different causes that lead healthcare personnel to practice self-medication, guaranteeing validity and efficacy, allows us to establish strategies to reduce this growing problem.

Impact of the Rise of Artificial Intelligence in Radiology: What Do Students Think?

The rise of artificial intelligence (AI) in medicine, and particularly in radiology, is becoming increasingly prominent. Its impact will transform the way the specialty is practiced and the current and future education model. The aim of this study is to analyze the perception that undergraduate medical students have about the current situation of AI in medicine, especially in radiology. A survey with 17 items was distributed to medical students between 3 January to 31 March 2022. Two hundred and eighty-one students correctly responded the questionnaire; 79.3% of them claimed that they knew what AI is. However, their objective knowledge about AI was low but acceptable. Only 24.9% would choose radiology as a specialty, and only 40% of them as one of their first three options. The applications of this technology were valued positively by most students, who give it an important Support Role, without fear that the radiologist will be replaced by AI (79.7%). The majority (95.7%) agreed with the need to implement well-established ethical principles in AI, and 80% valued academic training in AI positively. Surveyed medical students have a basic understanding of AI and perceive it as a useful tool that will transform radiology.

Bibliometric analyses of applications of artificial intelligence on tuberculosis

Background

Tuberculosis is one of the leading causes of death worldwide affecting mainly low- and middle-income countries. Therefore, the objective is to analyze the bibliometric characteristics of the application of artificial intelligence (AI) in tuberculosis in Scopus.

Methods

A bibliometric study, the Scopus database was used using a search strategy composed of controlled and free terms regarding tuberculosis and AI. The search fields "TITLE," "ABSTRACT," and "AUTHKEY" were used to find the terms. The collected data were analyzed with Scival software. Bibliometric data were described through the figures and tables summarized by absolute values and percentages.

Results

Thousand and forty-one documents were collected and analyzed. Yudong Zhang was the author with the highest scientific production; however, K. C. Santosh had the greatest impact. Anna University (India) was the institution with the highest number of published papers. Most papers were published in the first quartile. The United States led the scientific production. Articles with international collaboration had the highest impact.

Conclusion

Articles related to tuberculosis and AI are mostly published in first quartile journals, which would reflect the need and interest worldwide. Although countries with a high incidence of new cases of tuberculosis are among the most productive, those with the highest reported drug resistance need greater support and collaboration.