Medical Video Research Articles

When Online and Offline Environments Meet

Theory of situational privacy and self-disclosure posits that perceived levels of privacy are determined by perceptions of the environment, and that certain levels of privacy are necessary for self-disclosure. In video consultations, auditory and visual aspects of the doctor’s environment can cause patients to experience more or less privacy. To provide empirical evidence for these theoretical assumptions, we conducted a 2 (auditory environmental factor: doctor wearing headphones vs. not) by 2 (visual environmental factor: doctor showing the entire office vs. not) between-subjects scenario-based experiment (N = 163). Participants imagined themselves in a video consultation with a doctor and reported their information and territory privacy concerns and willingness to disclose to the doctor. Results showed that the ability to see the doctor’s entire office led to lower information privacy concerns, which – in turn – were associated with increased willingness to disclose medical information to a doctor. Wearing headphones by the doctor did not affect privacy concerns and self-disclosure. Manipulations of both the auditory and visual environment were not significantly associated with territory privacy concerns. These results provide direction for further research on environmental factors and their impact on patients’ privacy concerns and self-disclosure during medical video communications.

On-the-fly point annotation for fast medical video labeling.

In medical research, deep learning models rely on high-quality annotated data, a process often laborious and time-consuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two corners makes the process inherently frame-by-frame. Given the scarcity of experts' time, efficient annotation methods suitable for clinicians are needed. We propose an on-the-fly method for live video annotation to enhance the annotation efficiency. In this approach, a continuous single-point annotation is maintained by keeping the cursor on the object in a live video, mitigating the need for tedious pausing and repetitive navigation inherent in traditional annotation methods. This novel annotation paradigm inherits the point annotation's ability to generate pseudo-labels using a point-to-box teacher model. We empirically evaluate this approach by developing a dataset and comparing on-the-fly annotation time against traditional annotation method. Using our method, annotation speed was faster than the traditional annotation technique. We achieved a mean improvement of AP@50 over conventional method at equivalent annotation budgets on the developed dataset. Without bells and whistles, our approach offers a significant speed-up in annotation tasks. It can be easily implemented on any annotation platform to accelerate the integration of deep learning in video-based medical research.

Этико-философские аспекты цифровой танатологии

Modern man’s digital being has changed his attitude towards the most important cultural existentials, including the problem of death, which was sacred in previous eras. The article analyzes the characteristics of digital thanatology and raises the philosophical questions about the modes of digital afterlife. From an ethical and legal point of view, the article draws attention to the need to legally restrict the circulation on the Internet of the content depicting the bodies or parts of the bodies of military personnel killed in combat zones. The article analyzes moral and ethical nuances of new socio-cultural practices: selfies with the dead; streaming from funerals, creation of digital cemeteries and sharing of medical autopsy videos on Internet platforms. The author proposes the inclusuion of “Digital Ethics” course in the university curriculum on Ethics in order to prevent the devaluation of the problem of death under the influence of digitalization.

The emergence of MedTok: a qualitative analysis of popular medical TikTok videos.

Social media is increasingly being used by the public as a medium for health information. Unfortunately, misinformation has become widely available on these sites, often being provided using content that is designed to be more popular and engaging, and it is difficult for the public to differentiate between what is true and what is false. TikTok is one of these platforms and has been rapidly growing over the last few years. As an increasing number of people look to TikTok for their health information, it is important that quality information is accessible and popular on the platform. We conducted a review of TikTok videos using the top 10 videos to show when searching for 13 common conditions. Characteristics of both the creator and video were recorded and analyzed. Videos on conditions commonly diagnosed younger were commonly produced by younger creators with the condition, often based on their own experiences. Conversely, videos on conditions commonly diagnosed older were commonly produced by healthcare professionals providing educational information. Though for conditions affecting older individuals healthcare professionals may be able to create didactic, educational videos, for those affecting younger individuals, it may be beneficial to partner with younger creators, or "influencers," to produce more viral content. Further studies may expand on these ideas to encompass more facets of healthcare. As this study did not analyze the quality of the information in the videos, future research should also focus on determining the quality of popular content on TikTok and other social media platforms.

A review on current progress of semantic segmentation

Semantic segmentation, as an important task in the field of computer vision, has wide applications in image analysis and scene analysis. These application domains include autonomous driving, medical image analysis, image identification, and intelligent video surveillance. However, it faces many challenges due to the complex image structures and some confusing relationships between objects. This paper aims to provide an overview of key concepts in the field of semantic segmentation, including datasets and annotations, data augmentation, some relevant algorithms and models, and loss functions. By introducing and analyzing these concepts, we can gain a comprehensive understanding of the research progress and future directions in semantic segmentation. This paper also provides research advancements in the field of semantic segmentation. Through the introduction and analysis of these different concepts, we gain a deeper understanding of the current state and challenges of semantic segmentation. With the continuous development of deep learning techniques, we can expect semantic segmentation to have broader applications in the fields of computer vision and artificial intelligence.

Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation

The development of automated methods for analyzing medical images of colon cancer is one of the main research fields. A colonoscopy is a medical treatment that enables a doctor to look for any abnormalities like polyps, cancer, or inflammatory tissue inside the colon and rectum. It falls under the category of gastrointestinal illnesses, and it claims the lives of almost two million people worldwide. Video endoscopy is an advanced medical imaging approach to diagnose gastrointestinal disorders such as inflammatory bowel, ulcerative colitis, esophagitis, and polyps. Medical video endoscopy generates several images, which must be reviewed by specialists. The difficulty of manual diagnosis has sparked research towards computer-aided techniques that can quickly and reliably diagnose all generated images. The proposed methodology establishes a framework for diagnosing coloscopy diseases. Endoscopists can lower the risk of polyps turning into cancer during colonoscopies by using more accurate computer-assisted polyp detection and segmentation. With the aim of creating a model that can automatically distinguish polyps from images, we presented a modified DeeplabV3+ model in this study to carry out segmentation tasks successfully and efficiently. The framework's encoder uses a pre-trained dilated convolutional residual network for optimal feature map resolution. The robustness of the modified model is tested against state-of-the-art segmentation approaches. In this work, we employed two publicly available datasets, CVC-Clinic DB and Kvasir-SEG, and obtained Dice similarity coefficients of 0.97 and 0.95, respectively. The results show that the improved DeeplabV3+ model improves segmentation efficiency and effectiveness in both software and hardware with only minor changes.

Assessing the quality of breast cancer-related videos on TikTok: A cross-sectional study.

Breast cancer, the most common cancer in women globally, highlights the need for patient education. Despite many breast cancer discussions on TikTok, their scientific evaluation is lacking. Our study seeks to assess the content quality and accuracy of popular TikTok videos on breast cancer, to improve the dissemination of health knowledge. On August 22, 2023, we collected the top 100 trending videos from TikTok's Chinese version using "breast cancer/breast nodule" as keywords. We noted their length, TikTok duration, likes, comments, favorites, reposts, uploader types, and topics. Four assessment tools were used: Goobie's six questions, the Patient Educational Material Assessment Tool (PEMAT), the Video Information and Quality Index (VIQI), and the Global Quality Score (GQS). These instruments evaluate videos based on content, informational integrity, and overall quality. Among the 100 videos, content quality was low with Goobie's questions mostly scoring 0, except for management at 1.0 (QR 1.0). PEMAT scores were moderate: 54.1 (QR 1.6) for sum, 47.0 (QR 18.8) for PEMAT-A, and 52.3 (QR 11.7) for PEMAT-U. Regarding the quality of information, the VIQI (sum) median was 14.1 (QR 0.2). Additionally, the median GQS score was 3.5 (QR 0.1). Medical professionals' videos focused on breast cancer stages, while patient videos centered on personal experiences. Patient videos had lower content and overall quality compared to those by medical professionals (PEMAT, GQS: P < 0.001, P = 0.004) but received more comments, indicating higher engagement (all P < 0.05). TikTok's breast cancer content shows educational potential, but while informational quality is moderate, content quality needs improvement. Videos by medical professionals are of higher quality. We recommend increased involvement of healthcare professionals on TikTok to enhance content quality. Non-medical users should share verified information, and TikTok should strengthen its content vetting. Users must scrutinize the credibility of health information on social platforms.

Exploring the Usage of ChatGPT Among Medical Students in the United States.

Chat Generative Pretrained Transformer (ChatGPT) is a large language model developed by OpenAI that has gained widespread interest. It has been cited for its potential impact on health care and its beneficial role in medical education. However, there is limited investigation into its use among medical students. In this study, we evaluated the frequency of ChatGPT use, motivations for use, and preference for ChatGPT over existing resources among medical students in the United States. Data was collected from an original survey consisting of 14 questions assessing the frequency and usage of ChatGPT in various contexts within medical education. The survey was distributed via email lists, group messaging applications, and classroom lectures to medical students across the United States. Responses were collected between August and October 2023. One hundred thirty-one participants completed the survey and were included in the analysis. Of the total, 48.9% respondents responded that they have used ChatGPT in medical studies. Among ChatGPT users, 43.7% of respondents report using ChatGPT weekly, several times per week, or daily. ChatGPT is most used for writing, revising, editing, and summarizing purposes. 37.5% and 41.3% of respondents reported using ChatGPT more than 25% of the working time for these tasks respectively. Among respondents who have not used ChatGPT, more than 50% of respondents reported they were extremely unlikely or unlikely to use ChatGPT across all surveyed scenarios. ChatGPT users report they are more likely to use ChatGPT over directly asking professors or attendings (45.3%), textbooks (42.2%), and lectures (31.7%), and least likely to be used over popular flashcard application Anki (11.1%) and medical education videos (9.5%). ChatGPT is an increasingly popular resource among medical students, with many preferring ChatGPT over other traditional resources such as professors, textbooks, and lectures. Its impact on medical education will only continue to grow as its capabilities improve.

Quality of Pelvic Floor Therapy Videos on YouTube: Does Popularity Predict Reliability?

This study reviewed instructional videos on YouTube regarding pelvic floor physical therapy and assessed the association between the videos' popularity and the reliability of the videos' content. YouTube was searched using the terms relevant to pelvic floor muscle training. The first 100 videos for each search term were screened, and relevant metrics were collected for those meeting the inclusion criteria. Videos were assessed by 2 independent, trained health care professionals for reliability using the Medical Quality Video Evaluation (MQ-VET) tool, the modified DISCERN tool, the Journal of American Medical Association benchmark criteria, and the Global Quality Score. Popularity was assessed using the Video Power Index (VPI). Higher values for all scoring systems correlated with greater reliability and greater popularity, respectively. Five hundred videos were screened. Two hundred thirty-four videos were duplicates, and 99 did not meet the inclusion criteria. A total of 167 videos were reviewed. The median VPI and MQ-VET score was 201,114.76 (interquartile range, 7,194,020.29) and 48.00 (interquartile range, 12.75), respectively. Spearman's R value was 0.292 (P < 0.001), demonstrating a weak positive correlation between MQ-VET scores and VPI. The interrater reliability of the MQ-VET was good, with an intraclass correlation coefficient of 0.86 (95% confidence interval, 0.71-0.92). In summary, we identified a statistically significant but weak positive correlation between the reliability and popularity of YouTube videos about pelvic floor physical therapy.

Psychedelic Drug Microdosing Practices: A Qualitative Online Exploration.

"Microdosing," defined as the consumption of small, sub-hallucinogenic quantities of psychedelic drugs, has gained recent popularity. Microdosing is a relatively new concept, therefore no scientific recommendations exist on how to prepare and consume microdoses. Many consumers obtain microdosing information online. Few studies have investigated the content of this information; thus, the present study aimed to do so by collecting a large set of online microdosing information. A qualitative approach was taken to compile and characterize online microdosing information. Medical databases, video websites, online forums, drug-specific websites and forums, search engines, and social media websites were searched. A total of 174 unique resources were found, detailing the types of substances, preparation methods, doses, schedules, and safety strategies used by people who microdose. Future research is recommended to further explore how people prepare microdoses through in-person interviews and sample collection.

DeepPyramid+: medical image segmentation using Pyramid View Fusion and Deformable Pyramid Reception

PurposeSemantic segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and surgical video segmentation is challenging due to the diverse features of relevant classes, including heterogeneity, deformability, transparency, blunt boundaries, and various distortions. We propose a network architecture, DeepPyramid+, which addresses diverse challenges encountered in medical image and surgical video segmentation.MethodsThe proposed DeepPyramid+ incorporates two major modules, namely “Pyramid View Fusion” (PVF) and “Deformable Pyramid Reception” (DPR), to address the outlined challenges. PVF replicates a deduction process within the neural network, aligning with the human visual system, thereby enhancing the representation of relative information at each pixel position. Complementarily, DPR introduces shape- and scale-adaptive feature extraction techniques using dilated deformable convolutions, enhancing accuracy and robustness in handling heterogeneous classes and deformable shapes.ResultsExtensive experiments conducted on diverse datasets, including endometriosis videos, MRI images, OCT scans, and cataract and laparoscopy videos, demonstrate the effectiveness of DeepPyramid+ in handling various challenges such as shape and scale variation, reflection, and blur degradation. DeepPyramid+ demonstrates significant improvements in segmentation performance, achieving up to a 3.65% increase in Dice coefficient for intra-domain segmentation and up to a 17% increase in Dice coefficient for cross-domain segmentation.ConclusionsDeepPyramid+ consistently outperforms state-of-the-art networks across diverse modalities considering different backbone networks, showcasing its versatility. Accordingly, DeepPyramid+ emerges as a robust and effective solution, successfully overcoming the intricate challenges associated with relevant content segmentation in medical images and surgical videos. Its consistent performance and adaptability indicate its potential to enhance precision in computerized medical image and surgical video analysis applications.

A Smartphone use in Medical Photography

The modern era is becoming more and more digital. The use of smartphones for taking photos is widespread in the medical field. Most often, these photos are used for patient care, but also these photographs are used for educational and teaching purposes. Through a survey carried out among our university hospital doctors (HU), we wanted to establish an inventory of current practices in terms of taking but also using and storing medical photos and/or videos. Methods Prospective cross-sectional study, carried out among 1400 HU teaching doctors affiliated with the Faculty of Medicine of Tunis, via a Google Forms questionnaire pre-established by the digital pedagogy committee, and sent by email through institutional mailing lists. Results We received 223 (15.9%) survey participants, divided into 52 specialties. Only 2.7% did not use their “smartphones” to take medical photos. Smartphones were used for taking photos and/or videos of patients (76.7%), medical records (70.9%), and slides, samples or radiological images (72.2%). Twenty-two percent of respondents were reluctant to use a smartphone for taking medical photos, due to the risk of loss or theft of the phone (69.2%) and a question of confidentiality and ethics (59. 3%). The taking of medical photos and/or videos was for patient care (81.6%), for educational purposes or for research (90.6%), for personal reference (39.5%), for entertainment or for social networks (0.9%). The smartphone would be the most accessible and the easiest to use (non-accessibility of a traditional camera (58.7%)). The classic camera, on the other hand, is considered the most secure. Nearly half of doctors (48.4%) stored their medical photos in a dedicated database, 21.5% with their personal photos and 42.2% on their personal computer. An inter-colleague exchange of medical photos via instant messaging on social networks in 47.3% of cases. The patient’s oral consent is always requested in 58.3% (46% however consider this to be insufficient). Written consent is obtained in 4.5%. However, 7.6% do not request any. The need to establish legal procedures for taking these photos, by establishing standardized consent forms, was mentioned by most of the responses. Conclusions The use of smartphones for taking medical photos and/or videos is widespread, easy and useful. However, consent must be detailed. Standardized procedures must be put in place to protect patients but also doctors.

Multimodal Distillation Pre-training Model for Ultrasound Dynamic Images Annotation.

With the development of medical technology, ultrasonography has become an important diagnostic method in doctors' clinical work. However, compared with the static medical image processing work such as CT, MRI, etc., which has more research bases, ultrasonography is a dynamic medical image similar to video, which is captured and generated by a real-time moving probe, so how to deal with the video data in the medical field and cross modal extraction of the textual semantics in the medical video is a difficult problem that needs to be researched. For this reason, this paper proposes a pre-training model of multimodal distillation and fusion coding for processing the semantic relationship between ultrasound dynamic Images and text. Firstly, by designing the fusion encoder, the visual geometric features of tissues and organs in ultrasound dynamic images, the overall visual appearance descriptive features and the named entity linguistic features are fused to form a unified visual-linguistic feature, so that the model obtains richer visual, linguistic cues aggregation and alignment ability. Then, the pre-training model is augmented by multimodal knowledge distillation to improve the learning ability of the model. The final experimental results on multiple datasets show that the multimodal distillation pre-training model generally improves the fusion ability of various types of features in ultrasound dynamic images, and realizes the automated and accurate annotation of ultrasound dynamic images.

Social aesthetics of medical video games

Social aesthetics of medical video games

Digital learning about patients: An online survey of German medical students investigating learning strategies for family medical video consultations.

Training in video consultations is seldom included in the curriculum for future physicians. Exploration of preferred teaching methods and learning objectives in this context among medical students remains limited. This study addresses this research gap by conducting a survey among medical students in Germany to assess their educational requirements concerning video consultations and patient-centred distance learning. This quantitative study employed an online questionnaire designed for German medical students, following the guidelines of the International Association for Health Professions Education. The study primarily focused on discerning the didactic preferences related to patient-centred digital teaching regarding family medical video consultations. We provided a detailed explanation of a concrete learning concept, a family medical synchronous distance learning seminar. Subsequently, we surveyed students to gauge their needs, expectations, and evaluations of this concept. The collected data were subjected to descriptive analysis. The analysis revealed that students aspire to offer video consulting services to their patients in the future (sample size (n) = 369, median (med) = 68 of 101 Likert scale points, interquartile range (IQR) = 53.75), despite having limited knowledge in this area (n = 353, med = 21, IQR = 33.25). To acquire expertise in telehealth, students favor blended learning models (n = 331, med = 76, IQR = 50). They also recognize the benefits of distance learning, particularly for students with family responsibilities or those who must travel long distances to their learning institutions,. The presented distance seminar concept resonated with them (n = 278, med = 72.5, IQR = 50.5), surpassing five other digital learning models in preference. Furthermore, they expressed a desire for its continued implementation beyond the Coronavirus SARS-CoV-2 pandemic (n = 188, med = 77.5, IQR = 44.75). The deficiency in medical school education regarding video consultations requires attention. This issue could be resolved by integrating one of the five distance learning concepts outlined in this article.

Third-Line Overactive Bladder Therapies on TikTok: What Does the Public Learn?

Millions of people rely on social media platforms, including TikTok, for health-related information. TikTok has not yet been evaluated as an information source for overactive bladder (OAB) third-line therapies. Our aim was to assess TikTok videos on third-line therapies for OAB for misinformation and quality. In this cross-sectional analysis, we abstracted the top 50 TikTok videos for keywords: "Axonics," "sacral neuromodulation," "Interstim," "PTNS," "posterior tibial nerve stimulation," and "bladder Botox." Videos were scored for quality by 3 independent reviewers using the Medical Quality Video Evaluation Tool (MQ-VET). Two reviewers determined if videos contained misinformation. Of 300 videos screened, 119 videos were included. Twenty-four (21%) were created by medical professionals (MPs). Medical professional videos were more frequently shared (5 vs 1, P < 0.01) but had similar views, likes, comments, and length. Although MP videos had significantly higher MQ-VET scores (43 vs 27, P < 0.01), there was no difference in the rate of misinformation between MP and non-MP videos (21% vs 18%). Twenty-two videos (18.4%) contained misinformation, which were 3 times longer (50.5 vs 15 seconds, P < 0.01) and had higher MQ-VET scores (34.5 vs 27, P = 0.03) than those without misinformation. Common themes of misinformation pertained to therapy indication, mechanism of action, and patient limitations after undergoing therapy. Many TikTok videos on OAB third-line therapies contain misinformation. Most of these videos were not of high quality and created by the public. Medical professionals should be aware of misinformation permeating TikTok, given its large audience, and aim to promote or offer educational material of better accuracy and quality.

Artificial Intelligence-Based Face Transformation in Patient Seizure Videos for Privacy Protection

ObjectiveTo investigate the feasibility and accuracy of artificial intelligence (AI) methods of facial deidentification in hospital-recorded epileptic seizure videos, for improved patient privacy protection while preserving clinically important features of seizure semiology. Patients and MethodsVideos of epileptic seizures displaying seizure-related involuntary facial changes were selected from recordings at Taipei Veterans General Hospital Epilepsy Unit (between August 1, 2020 and February 28, 2023), and a single representative video frame was prepared per seizure. We tested 3 AI transformation models: (1) morphing the original facial image with a different male face; (2) substitution with a female face; and (3) cartoonization. Facial deidentification and preservation of clinically relevant facial detail were calculated based on: (1) scoring by 5 independent expert clinicians and (2) objective computation. ResultsAccording to the clinician scoring of 26 facial frames in 16 patients, the best compromise between deidentification and preservation of facial semiology was the cartoonization model. A male facial morphing model was superior to the cartoonization model for deidentification, but clinical detail was sacrificed. Objective similarity testing of video data reported deidentification scores in agreement with the clinicians’ scores; however, preservation of semiology gave mixed results likely due to inadequate existing comparative databases. ConclusionArtificial intelligence-based face transformation of medical seizure videos is feasible and may be useful for patient privacy protection. In our study, the cartoonization approach provided the best compromise between deidentification and preservation of seizure semiology.

Perceptions of and Preferences for Telemedicine Use Since the Early Stages of the COVID-19 Pandemic: Cross-Sectional Survey of Patients and Physicians.

While the use of telemedicine (TLM) increased worldwide during the early phases of the COVID-19 pandemic, little is known about the use and acceptance of TLM post the COVID-19 pandemic. This study aims to evaluate patients' and physicians' self-reported use, preferences, and acceptability of different types of TLM after the initial phases of the COVID-19 pandemic. We conducted a cross-sectional survey among patients and physicians in Geneva, Switzerland, between September 2021 and January 2022. Patients in waiting rooms of both private and public medical centers and emergency services were invited to answer a web-based questionnaire. Physicians working in private and public settings were invited by email to answer a similar questionnaire. The questionnaires assessed participants' sociodemographics and digital literacy; self-reported use of TLM; as well as preferences and acceptability of TLM for different clinical situations. A total of 567 patients (309/567, 55% women) and 448 physicians (230/448, 51% women and 225/448, 50% in private practice) responded to the questionnaire. Patients (263/567, 46.5%) and physicians (247/448, 55.2%) generally preferred the phone over other TLM formats and considered it to be acceptable for most medical situations. Email (417/567, 73.6% and 308/448, 68.8%) was acceptable for communicating exam results, and medical certificates (327/567, 67.7% and 297/448, 66.2%) and video (302/567, 53.2% and 288/448, 64.3%) was considered acceptable for psychological support by patients and physicians, respectively. Older age was associated with lower acceptability of video for both patients and physicians (odds ratio [OR] 0.03, 95% CI 0.00-0.33 and OR 0.23, 95% CI 0.08-0.66) while previous use of video was positively associated with video acceptability (OR 3.16, 95% CI 1.84-5.43 and OR 3.34, 95% CI 2.91-5.54). Psychiatrists and hospital physicians were more likely to consider video to be acceptable (OR 10.79, 95% CI 3.96-29.30 and OR 3.97, 95% CI 2.23-7.60). Despite the development of video, the acceptability of video remains lower than that of the phone for most health issues or patient requests. There is a need to better define for which patients and in which medical situations video can become safe and efficient.

Measuring the quality of YouTube videos on anxiety: a study using the global quality scale and discern tool

Anxiety is a persistent feeling of fear. The onset usually starts in childhood and can have adverse effects in adolescence. In the past, the public used to seek medical advice from professionals. In the modern era, free video-sharing platforms like YouTube have gained popularity among the masses. It has been observed that the public consults medical information from these videos. Therefore, it has become necessary for medical experts to assess the quality of these videos and prevent misinformation. On 15 August 2022, a YouTube search was done on treating and managing anxiety. 75 videos were selected according to inclusion criteria. The videos were divided into two categories: useful and misleading. The global quality scale (GQS) and DISCERN tool were used to review and assess these videos independently. Out of 169 videos analyzed, 137 had useful information and 32 had misleading information content. Useful content videos had a higher number of DISCERN and GQS than misleading content videos. The analysis showed that physician-based 51 videos have useful information. It was also observed that useful videos had more likes and views. Therefore, medical professionals should be encouraged to make medical education and awareness videos because physician-based videos seem to have the highest credibility and popularity among the general public.

Dissemination of health-related technologies in Germany between 2020 and 2022

Abstract Background Health systems are facing major changes due to digitalization. ICTs offer the potential to improve healthcare, but the impact on health inequities is unclear. Therefore, the following research question will be addressed: How will health-related technologies be used in Germany between 2020 and 2022 and are there age- and gender-specific differences? Methods This analysis is based on three cross-sectional online surveys conducted in 2020 (N = 1570) and in 2022 (N = 1200; N = 1850). The sample is representative for age, gender, and federal state according to Eurostat. Participants indicated whether they used (yes/no) online booking for medical appointments (OB), video consultation (VC), and app-based transmission of health data to health insurers (DT). In 2020, participants were asked if they had used such technologies before and during the COVID-19 pandemic. For the stratified analysis, a Chi2-test was used. Results Before COVID-19, 34.5% of the participants used OB, 4.1% VC, and 18.8% DT. VC and DT differed by age, OB by gender only. In May 2020, there was a decrease in use of OB (21.6%) and DT (12.0%), but not VC (5.4%). Age-related differences existed for VC and DT. In March 2022, an increased usage was observed for all technologies (OB: 40.4%, DT: 22.3%, VC: 7.1%). Gender and age differences occurred for VC, but for age only for OB and DT. An increased use was also observed in autumn 2022 (OB: 57.6%, DT. 35.4%, VC: 11.1%). The usage of such technologies continued to vary by age and gender, with one exemption: no gender-specific differences appeared for VC. Conclusions With the exception of the onset of the COVID-19 pandemic, an increased diffusion of health-related technologies has been observed, accompanied by an elevated availability of such technologies after the pandemic. Although the diffusion of such technologies increased from 2020 to 2022, age- and gender-specific differences regarding their usage seem to emerge and tend to consolidate. Key messages • Between 2020 and 2022, an increased utilization of health technologies can be observed, which goes hand in hand with an expanded diffusion of such technologies in the German health system. • Not all population groups participate in digitization in the same way. Whether these differences will contribute to health inequities needs to be closely examined.