Thematische Bibliographien / Medical AI

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Medical AI“

Autor: Grafiati
Veröffentlicht am 24. Juni 2021
Zuletzt aktualisiert am 21. Februar 2023

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Zeitschriftenartikel zum Thema "Medical AI"

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Lu, Donna. „DeepMind's medical AI“. New Scientist 243, Nr. 3242 (August 2019): 15. http://dx.doi.org/10.1016/s0262-4079(19)31464-2.

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Konno, Masamitsu, und Hideshi Ishii. „AI for medical use“. Oncotarget 10, Nr. 2 (04.01.2019): 86–87. http://dx.doi.org/10.18632/oncotarget.26556.

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Stower, Hannah. „Transparency in medical AI“. Nature Medicine 26, Nr. 12 (Dezember 2020): 1804. http://dx.doi.org/10.1038/s41591-020-01147-y.

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Willyard, Cassandra. „Can AI Fix Medical Records?“ Nature 576, Nr. 7787 (18.12.2019): S59—S62. http://dx.doi.org/10.1038/d41586-019-03848-y.

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Price, W. Nicholson. „Distributed Governance of Medical AI“. SMU Science and Technology Law Review 25, Nr. 1 (2022): 3. http://dx.doi.org/10.25172/smustlr.25.1.2.

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Artificial intelligence (AI) has the potential to democratize expertise in medicine, bring expertise previously limited to specialists to a variety of health-care settings. But AI can easily falter, and making sure that AI works well across that variety of settings is a challenging task. Centralized governance, such as review by the Food and Drug Administration, can only do so much, since system performance will depend on the particular health-care setting and how the AI system is integrated into setting-specific clinical workflows. This Essay presents the need for distributed governance, where some oversight tasks are undertaken in localized settings. It points out the resource-based challenges of such governance and offers policy suggestions to ease the burden.
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Calisto, Francisco Maria, Carlos Santiago, Nuno Nunes und Jacinto C. Nascimento. „BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions“. Artificial Intelligence in Medicine 127 (Mai 2022): 102285. http://dx.doi.org/10.1016/j.artmed.2022.102285.

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Longoni, Chiara, Andrea Bonezzi und Carey K. Morewedge. „Resistance to Medical Artificial Intelligence“. Journal of Consumer Research 46, Nr. 4 (03.05.2019): 629–50. http://dx.doi.org/10.1093/jcr/ucz013.

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Abstract Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers’ unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) that only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.
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s, Keerthi Rani. „Artificial Intelligence on Medical Fields“. Data Analytics and Artificial Intelligence 3, Nr. 2 (01.02.2023): 113–15. http://dx.doi.org/10.46632/daai/3/2/21.

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This paper is about a overview of AI in medical field, dealing with recent and future applications that are related to AI. The aim is to develop knowledge and information about AI among the primary care physicians in the health care. Firstly, I've described about what is Artificial Intelligence then, who’s the father of it, what are the types of AI that is used in the medical field, features of AI, approaches and its needs. This paper is also about how AI is used in the health care, diagnosis, creation of new drug and delivery of drug, AI in COVID-19 pandemic, how it is used to analyze CT scans, x-rays, MRIs and about how Machine Learning is used in the health care and also how google is dealing with the future problem using Machine learning.
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Iqbal, Shazia. „Are Medical Educators Primed To Adopt Artificial Intelligence In Healthcare System And Medical Education?“ Health Professions Educator Journal 5, Nr. 1 (22.04.2022): 7–8. http://dx.doi.org/10.53708/hpej.v5i1.1707.

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Currently, the acceptance of artificial intelligence (AI) in thehealth care system and medical education is growing rapidly.It is crucial to involve healthcare professionals and medicaleducators in developing, authenticating, and implementingAI-enabled tools in medicine. A lack of AI knowledge andinadequate preparation to embrace this revolution will leadto orthodox patient care. Consequently, medical support willnot be optimal to keep pace with rapidly changing healthcaretrends word widely. This is a significant barrier to adoptingand implementing AI that will affect the quality of patient careand the future of the healthcare sector. In addition, the limitedexisting AI education programs are a barrier to the developmentand implementation of AI-assisted applications at various levelsof medical education (Charow et al., 2021).
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SAGA, Ryosuke. „AI and Visualization with Medical Data“. Journal of the Visualization Society of Japan 38, Nr. 151 (2018): 19–22. http://dx.doi.org/10.3154/jvs.38.151_19.

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Dissertationen zum Thema "Medical AI"

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Baban, Hanna, und Olivia Grauning. „Using Fetal Myocardial Velocity Recordings to Evaluate an AI Platform to Predict High-risk Deliveries“. Thesis, KTH, Medicinteknik och hälsosystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255858.

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Diagnosing abnormal fetal cardiac function using ultrasound is a complicated procedure which makes it difficult to obtain high quality results from ultrasound examinations that are performed shortly before delivery. Color tissue Doppler imaging (cTDI) is the echocardiographic technique that has been used to obtain the data for this project. Subtle changes in the fetal cardiac function caused by a variety of complications can possibly be detected using cTDI. Fetuses suffering from these complications are often involved in high-risk deliveries. Combining the data obtained from cTDI with Artificial Intelligence (AI) may improve precision and accuracy when it comes to diagnosing pathological conditions involving fetal cardiac function before delivery. AI uses machines to perform and execute tasks that are characteristic of human intelligence. AI can be achieved by using deep learning. Deep learning uses algorithms called artificial neural networks that are inspired by the biological structure and function of the human brain. The neural networks classify information in a similar manner to the human brain. A platform that uses deep learning can make statements or predictions based on the data fed to it. The AI platform Peltarion uses deep learning to perform tasks. The aim of this project was to use Peltarion to evaluate the possibility of predicting high-risk deliveries with abnormal perinatal outcome by using data obtained by cTDI velocity recordings of the fetal heart. The data included myocardial velocity recordings from 107 pregnancies, out of the 107 pregnancies 82 of the babies were born healthy while 25 babies had an adverse perinatal outcome. The data was uploaded in the platform and three models were built and trained in order to evaluate the performance of the platform using the data. The parameters that have been used to determine the results are loss, accuracy and precision. The results showed that the accuracy parameter was measured to be 0.8 in all cases which means that the model correctly predicts if a fetal heart is healthy or likely to have an adverse outcome 80% of the time. The precision parameter was measured to be around 0.4 which means out of all the times the model predicted a fetal heart to have an adverse outcome, only 40% truly had an adverse outcome. It was concluded that a substantially larger amount of evenly distributed data is required to appropriately evaluate the possibility of using fetal myocardial velocity recordings as data for the AI platform Peltarion to predict high-risk deliveries.
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Hägnestrand, Ida, und Söraas Nina Lindström. „Artificiell intelligens för radiologisk diagnostisering av knäartros : Hur bildkvalitetsförsämringar påverkar en AI-programvaras diagnostisering“. Thesis, KTH, Medicinteknik och hälsosystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298228.

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Framgången av mönsterigenkänning inom AI (artificiell intelligens) har skapat höga förväntningar om att AI ska kunna appliceras inom vården, framför allt inom radiologi. Det danska företaget Radiobotics har utvecklat en maskininlärningsbaserad programvara som diagnostiserar knäartros, för att assistera vårdpersonalen i deras arbete. Denna AI-programvara vid namn RBknee analyserar en röntgenbild utifrån tre diagnostiska parametrar som förekommer vid knäartros, för att sedan sammanställa de radiologiska fynden i en skriftlig rapport tillsammans med en slutgiltig diagnos. För att få förståelse för hur RBknees analysförmåga påverkas av en bildkvalitetsförsämring undersöktes för vilken kontrast och brusnivå som RBknee genererar ett felaktigt utlåtande gällande de diagnostiska parametrarna och slutdiagnosen. Vidare undersöktes om graden av knäartros påverkade RBknee analysförmåga vid en bildkvalitetsförsämring. Ett bildunderlag med kliniskt tagna slätröntgenbilder av knän degraderades med avseende på kontrast och brus för att sedan analyseras av RBknee. Förändringar av RBknees utlåtande för de degraderade bilderna jämfört med originalbildens utlåtande sammanställdes och studerades. Resultatet visade att det inte gick att identifiera en specifik försämringsgrad av bildkvaliteten där RBknee genererade ett felaktigt utlåtande. RBknees förmåga att generera ett korrekt utlåtande var bättre vid en kontrastdegradering än vid en brusdegradering. Det konstaterades att en ökad brusnivå ökade risken för ett felaktigt utlåtande av RBknee, samt att brusets position på röntgenbilden hade en påverkan. Det gick även att fastställa att röntgenbilder av knän med en lägre grad av knäartros i högre grad riskerade att få felaktiga utlåtanden av RBknee.
The success of pattern recognition in AI (artificial intelligence) has brought high expectations for AI to be applied in healthcare, especially in radiology. A machine learning software for knee osteoarthritis diagnosis has been developed by the Danish company Radiobotics. The AI software, named RBknee, analyses digital radiographs and annotates osteoarthritis related findings. The findings, together with a conclusion, are compiled in a written report. RBknee is intended to assist healthcare professionals in radiographic analysis. How RBknees analytical ability is affected by a reduced image quality was studied by examining the contrast and noise level which cause RBknee to generate incorrect findings and conclusions. If the image quality reduction caused RBknees analytically ability to differ with different degrees of knee osteoarthritis, was also studied. The image quality of clinical digital radiographs of knees was reduced and analysed by RBknee. RBknees findings and conclusion were compared with the report of the original image, where the changes were compiled into tables. No specific reduction of image quality that restricted RBknee analytically ability was established in the study. An increased noise level seemed to increase the risk of receiving an incorrect report by RBknee. RBknees ability to generate correct report was better for contrast degraded images than for images with increased noise level. The position of the noise in the radiograph also seemed to have an impact on RBknees analytical ability. It was also possible to establish that knees with a lower degree of knee osteoarthritis were more likely to receive an incorrect report from RBknee.
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Björklund, Pernilla. „The curious case of artificial intelligence : An analysis of the relationship between the EU medical device regulations and algorithmic decision systems used within the medical domain“. Thesis, Uppsala universitet, Juridiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-442122.

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The healthcare sector has become a key area for the development and application of new technology and, not least, Artificial Intelligence (AI). New reports are constantly being published about how this algorithm-based technology supports or performs various medical tasks. These illustrates the rapid development of AI that is taking place within healthcare and how algorithms are increasingly involved in systems and medical devices designed to support medical decision-making.  The digital revolution and the advancement of AI technologies represent a step change in the way healthcare may be delivered, medical services coordinated and well-being supported. It could allow for easier and faster communication, earlier and more accurate diagnosing and better healthcare at lower costs. However, systems and devices relying on AI differs significantly from other, traditional, medical devices. AI algorithms are – by nature – complex and partly unpredictable. Additionally, varying levels of opacity has made it hard, sometimes impossible, to interpret and explain recommendations or decisions made by or with support from algorithmic decision systems. These characteristics of AI technology raise important technological, practical, ethical and regulatory issues. The objective of this thesis is to analyse the relationship between the EU regulation on medical devices (MDR) and algorithmic decision systems (ADS) used within the medical domain. The principal question is whether the MDR is enough to guarantee safe and robust ADS within the European healthcare sector or if complementary (or completely different) regulation is necessary. In essence, it will be argued that (i) while ADS are heavily reliant on the quality and representativeness of underlying datasets, there are no requirements with regard to the quality or composition of these datasets in the MDR, (ii) while it is believed that ADS will lead to historically unprecedented changes in healthcare , the regulation lacks guidance on how to manage novel risks and hazards, unique to ADS, and that (iii) as increasingly autonomous systems continue to challenge the existing perceptions of how safety and performance is best maintained, new mechanisms (for transparency, human control and accountability) must be incorporated in the systems. It will also be found that the ability of ADS to change after market certification, will eventually necessitate radical changes in the current regulation and a new regulatory paradigm might be needed.
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Söllner, Michaela [Verfasser], Jörg [Akademischer Betreuer] Königstorfer, Jörg [Gutachter] Königstorfer und Martina [Gutachter] Steul-Fischer. „Paving the Way for Medical AI: Consumer Response to Artificial Intelligence in Healthcare / Michaela Söllner ; Gutachter: Jörg Königstorfer, Martina Steul-Fischer ; Betreuer: Jörg Königstorfer“. München : Universitätsbibliothek der TU München, 2021. http://d-nb.info/1231434643/34.

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Kantedal, Simon. „Evaluating Segmentation of MR Volumes Using Predictive Models and Machine Learning“. Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171102.

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A reliable evaluation system is essential for every automatic process. While techniques for automatic segmentation of images have been extensively researched in recent years, evaluation of the same has not received an equal amount of attention. Amra Medical AB has developed a system for automatic segmentation of magnetic resonance (MR) images of human bodies using an atlas-based approach. Through their software, Amra is able to derive body composition measurements, such as muscle and fat volumes, from the segmented MR images. As of now, the automatic segmentations are quality controlled by clinical experts to ensure their correctness. This thesis investigates the possibilities to leverage predictive modelling to reduce the need for a manual quality control (QC) step in an otherwise automatic process. Two different regression approaches have been implemented as a part of this study: body composition measurement prediction (BCMP) and manual correction prediction (MCP). BCMP aims at predicting the derived body composition measurements and comparing the predictions to actual measurements. The theory is that large deviations between the predictions and the measurements signify an erroneously segmented sample. MCP instead tries to directly predict the amount of manual correction needed for each sample. Several regression models have been implemented and evaluated for the two approaches. Comparison of the regression models shows that local linear regression (LLR) is the most performant model for both BCMP and MCP. The results show that the inaccuracies in the BCMP-models, in practice, renders this approach useless. MCP proved to be a far more viable approach; using MCP together with LLR achieves a high true positive rate with a reasonably low false positive rate for several body composition measurements. These results suggest that the type of system developed in this thesis has the potential to reduce the need for manual inspections of the automatic segmentation masks.
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Johnson, Beverly Elaine. „Attitudes and Perceptions of Mental Health Treatment for Native American Clients“. ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4524.

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The need for mental health service is increasing in American Indian/Alaska Native (AI/AN) communities. While research has examined the availability, access, and effectiveness of provided services to the AI/AN, very little is known about the influence of the attitude and perceptions of both clinicians and clients in their therapeutic relationship in the treatment process. Using the frameworks of liberation, oppression, and trauma theory, this qualitative phenomenological study explored mental health service delivery and utilization issues within an AI/AN community. Data were collected through semistructured interviews with 14 clinician and client participants. The data were sorted into themes and subthemes and analyzed using the NVivo 11 computer software. Intergenerational struggle represented the primary theme and other subthemes such as assimilation, acculturation, and communication were among some of the secondary themes gathered from the data. Analysis of the themes provided greater insights into the dynamics of the participant's lived experience in various organizational structures within the larger community as well as a better understanding of mental health service delivery and utilization in maintaining sobriety in their daily struggles. The results indicated that intergenerational struggle along with other environmental factors were the chief causes of their cyclical journey through the penal and other systems; thus reducing their ability in maintaining longer sobriety and in improving their mental health. The implications for positive social change in this study include the reduction of stigma associated with these health issues through the education of the community and in training clinicians in factor-specific issues impacting life altering critical events in AI/AN struggles.
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Walker, Donald. „Similarity Determination and Case Retrieval in an Intelligent Decision Support System for Diabetes Management“. Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1194562654.

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Mercatali, Martina. „La traduzione medica ai tempi del Coronavirus: i protocolli clinici per il trattamento della malattia da COVID-19“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il presente elaborato mira a illustrare l'importanza della traduzione medica, presentando e analizzando la traduzione di 10 protocolli clinici sulla sperimentazione di farmaci per il trattamento della malattia COVID-19. Il primo capitolo sarà una panoramica sulle lingue speciali per presentare le caratteristiche del linguaggio tecnico-scientifico. Successivamente, si indagherà sull'importanza della traduzione nella divulgazione scientifica, concentrandosi in particolare sulla traduzione medica, proponendo un excursus storico e spiegando perché la medicina è stato il primo ambito scientifico a prosperare grazie alla traduzione. Il secondo capitolo sarà dedicato agli studi e ai protocolli clinici sia da un punto di vista storico che attuale, prendendo in considerazione come la pandemia da COVID-19 abbia influenzato la necessità di traduzione nella sperimentazione clinica. Il terzo capitolo delineerà il progetto finale dell’elaborato, concentrandosi sulla base teorica dell'analisi del testo di partenza orientata alla traduzione che sarà poi applicata ai testi in questione. Il quarto e ultimo capitolo presenterà i principali problemi riscontrati durante il processo di traduzione, focalizzandosi su questioni pragmatiche, convenzionali e linguistiche che saranno poi prese in considerazione per giustificare le strategie traduttive adottate.
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Zuffa, Elisa <1979&gt. „La suscettibilità genetica al linfoma di Hodgkin e ai tumori secondari: due storie o due capitoli della stessa storia?“ Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/994/1/Tesi_Zuffa_Elisa.pdf.

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Zuffa, Elisa <1979&gt. „La suscettibilità genetica al linfoma di Hodgkin e ai tumori secondari: due storie o due capitoli della stessa storia?“ Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/994/.

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Bücher zum Thema "Medical AI"

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Jiang, Richard, Danny Crookes, Hua-Liang Wei, Li Zhang und Paul Chazot. Recent Advances in AI-enabled Automated Medical Diagnosis. New York: CRC Press, 2022. http://dx.doi.org/10.1201/9781003176121.

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Bracci, Fabio, und Giuseppe Cardamone. Presenze: Migranti e accesso ai servizi socio-sanitari. Milano, Italy: FrancoAngeli, 2005.

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Qi, Yuan, Hrsg. "Fei dian" shi qi zui ke ai de ren. Shanghai: Shanghai jiao yu chu ban she, 2003.

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Italiane alla guerra: L'assistenza ai feriti 1915-1918. Venezia: Marsilio, 2003.

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Giannoni, Margherita. Equità nell'accesso ai servizi sanitari, disuguaglianze di salute e immigrazione: La performance dei servizi sanitari. Milano, Italy: FrancoAngeli, 2010.

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Ai, dai wo qu yuan fang: Taiwan a mo yi zhen ji xing = Take wing with love. Xinbei Shi: Yi qi lai chu ban, 2012.

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1957-, Quaglini Silvana, Barahona P. 1954- und Andreassen Steen, Hrsg. Artificial intelligence in medicine: 8th Conference on AI in Medicine in Europe, AIME 2001, Cascais, Portugal, July 1-4, 2001 : proceedings. Berlin: Springer, 2001.

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Lanati, Antonella. Qualità in biotech e pharma: Gestione manageriale dei processi, dalla ricerca ai suoi prodotti. Milano: Springer-Verlag Milan, 2010.

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Ru ai quan shu: Breast cancer, the complete guide. Taibei Shi: Yuan shui wen hua, 2001.

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Dieta, salute, calendari: Dal regime stagionale antico ai regimina mensium medievali : origine di un genere nella letteratura medica occidentale. Spoleto (Perugia): Centro italiano di studi sull'alto Medioevo, 2007.

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Buchteile zum Thema "Medical AI"

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Li, Ron C., Naveen Muthu, Tina Hernandez-Boussard, Dev Dash und Nigam H. Shah. „Explainability in Medical AI“. In Cognitive Informatics in Biomedicine and Healthcare, 235–55. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09108-7_8.

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Anshik. „Medical Imaging“. In AI for Healthcare with Keras and Tensorflow 2.0, 245–312. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7086-8_8.

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Liu, Zhiyi, und Yejie Zheng. „AI Medical Treatment: Epidemic, Death and Love“. In AI Ethics and Governance, 47–61. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2531-3_4.

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Shaikh, Khalid, Sabitha Krishnan und Rohit Thanki. „AI in Healthcare and Medical Imaging“. In Artificial Intelligence in Breast Cancer Early Detection and Diagnosis, 67–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59208-0_4.

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Schaefferkoetter, Josh. „Evolution of AI in Medical Imaging“. In Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging, 37–56. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00119-2_4.

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Tsafack Chetsa, Ghislain Landry. „AI in the Medical Decision Context“. In Towards Sustainable Artificial Intelligence, 103–21. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7214-5_8.

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Liang, Yuan, Lei He und Xiang ‘Anthony’ Chen. „Human-Centered AI for Medical Imaging“. In Human–Computer Interaction Series, 539–70. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82681-9_16.

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Lokesh, Govvala, T. Kavya Tejaswy, Y. Sai Meghana und M. Kameswara Rao. „Medical Report Analysis Using Explainable Ai“. In Lecture Notes in Electrical Engineering, 1083–90. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7985-8_113.

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Viñas, Ramon, Xu Zheng und Jer Hayes. „A Graph-based Imputation Method for Sparse Medical Records“. In Multimodal AI in Healthcare, 377–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14771-5_27.

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Morey, José M., Nora M. Haney und Woojin Kim. „Applications of AI Beyond Image Interpretation“. In Artificial Intelligence in Medical Imaging, 129–43. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-94878-2_11.

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Konferenzberichte zum Thema "Medical AI"

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Subhashini, Mareddy Sushma, Basampally Yeshwanth Goud, Merugu Nikhil und Gantla Sai Kumar Reddy. „AI Medical Diagnosis Application“. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432301.

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Gandhi, Meera, Vishal Kumar Singh und Vivek Kumar. „IntelliDoctor - AI based Medical Assistant“. In 2019 Fifth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). IEEE, 2019. http://dx.doi.org/10.1109/iconstem.2019.8918778.

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Cai, Yuwei, Ruiquan Chen, Hao Liu, Zheng Peng, Yufeng Yuan, Yuan Lu und Xiao Peng. „Lung nodule CT medical image analysis based on deep learning“. In AI in Optics and Photonics, herausgegeben von Chao Zuo. SPIE, 2023. http://dx.doi.org/10.1117/12.2652067.

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Daskalopulu, Aspassia, Eleutherios Tsoukalas und Dimitrios Bargiotas. „Normative and Fuzzy Aspects of Medical AI“. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2019. http://dx.doi.org/10.1109/bibe.2019.00109.

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Jothi, J. Nirmal, S.Poongodi, V.Chinnammal, L.Kannagi, M.Panneerselvam und R. Thandaiah Prabu. „AI Based Humanoid Chatbot for Medical Application“. In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2022. http://dx.doi.org/10.1109/icosec54921.2022.9951910.

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Mirsky, Yisroel. „Discussion Paper: The Integrity of Medical AI“. In ASIA CCS '22: ACM Asia Conference on Computer and Communications Security. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3494109.3527191.

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Staron, Miroslaw, Helena Odenstedt Herges, Silvana Naredi, Linda Block, Ali El-Merhi, Richard Vithal und Mikael Elam. „Robust Machine Learning in Critical Care — Software Engineering and Medical Perspectives“. In 2021 IEEE/ACM 1st Workshop on AI Engineering - Software Engineering for AI (WAIN). IEEE, 2021. http://dx.doi.org/10.1109/wain52551.2021.00016.

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Kapcia, Marcin, Hassan Eshkiki, Jamie Duell, Xiuyi Fan, Shangming Zhou und Benjamin Mora. „ExMed: An AI Tool for Experimenting Explainable AI Techniques on Medical Data Analytics“. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00134.

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Schaekermann, Mike, Graeme Beaton, Elaheh Sanoubari, Andrew Lim, Kate Larson und Edith Law. „Ambiguity-aware AI Assistants for Medical Data Analysis“. In CHI '20: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3313831.3376506.

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Vinayagam, P., A. Hameem Irfana, K. Sireesha, N. Tabassum Fathima und K. Tanuja. „An AI Tool for Emergency Medical Assistance system“. In 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2022. http://dx.doi.org/10.1109/icosec54921.2022.9951990.

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Berichte der Organisationen zum Thema "Medical AI"

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Hooker, Reece. Special Report: AI in medical research. Monash University, August 2022. http://dx.doi.org/10.54377/b8f8-a681.

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Hooker, Reece. Special Report: AI in medical research. Monash University, August 2022. http://dx.doi.org/10.54377/b8f8-a681.

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Purnomo, Mauridhi Hery, Akhmad Musafa und Ardyono Priyadi. AI a jolt of energy for Indonesian medical research. Herausgegeben von Ria Ernunsari und Reece Hooker. Monash University, August 2022. http://dx.doi.org/10.54377/65e9-c61c.

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Purnomo, Mauridhi Hery, Akhmad Musafa und Ardyono Priyadi. AI a jolt of energy for Indonesian medical research. Herausgegeben von Ria Ernunsari und Reece Hooker. Monash University, August 2022. http://dx.doi.org/10.54377/65e9-c61c.

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Stern, Ariel Dora. The Regulation of Medical AI: Policy Approaches, Data, and Innovation Incentives. Cambridge, MA: National Bureau of Economic Research, Dezember 2022. http://dx.doi.org/10.3386/w30639.

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Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, Juli 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.
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Ferryman, Kadija. Framing Inequity in Health Technology: The Digital Divide, Data Bias, and Racialization. Just Tech, Social Science Research Council, Februar 2022. http://dx.doi.org/10.35650/jt.3018.d.2022.

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" Since 2010, there has been an exponential growth in health data and health information technologies, such as electronic health records (EHRs), and AI-enabled medical tools. Despite the growth and investment in these technologies, they have had few positive effects on health outcomes, especially for marginalized populations. This review begins by addressing common rhetorical and ethical responses to inequities in health technologies, such as the digital divide and data bias frames. It then problematizes both approaches before proposing that examining racialization, or the creation and circulation of racial hierarchies, can contribute to a more comprehensive framework for facilitating health equity in health information technology."
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Phinisee, Eri, Autumn Toney und Melissa Flagg. AI and Industry: Postings and Media Portrayals. Center for Security and Emerging Technology, Mai 2021. http://dx.doi.org/10.51593/20200059.

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Artificial intelligence is said to be transforming the global economy and society in what some dub the “fourth industrial revolution.” This data brief analyzes media representations of AI and the alignments, or misalignments, with job postings that include the AI-related skills needed to make AI a practical reality. This potential distortion is important as the U.S. Congress places an increasing emphasis on AI. If government funds are shifted away from other areas of science and technology, based partly on the representations that leaders and the public are exposed to in the media, it is important to understand how those representations align with real jobs across the country.
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Borrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, Dezember 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.

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The integration of novel technologies for monitoring and investigating compliance can enhance the effectiveness of regimes related to weapons of mass destruction (WMD). This report looks at the potential role of four novel approaches based on recent technological advances – remote sensing tools; open-source satellite data; open-source trade data; and artificial intelligence (AI) – in monitoring and investigating compliance with WMD treaties. The report consists of short essays from leading experts that introduce particular technologies, discuss their applications in WMD regimes, and consider some of the wider economic and political requirements for their adoption. The growing number of space-based sensors is raising confidence in what open-source satellite systems can observe and record. These systems are being combined with local knowledge and technical expertise through social media platforms, resulting in dramatically improved coverage of the Earth’s surface. These open-source tools can complement and augment existing treaty verification and monitoring capabilities in the nuclear regime. Remote sensing tools, such as uncrewed vehicles, can assist investigators by enabling the remote collection of data and chemical samples. In turn, this data can provide valuable indicators, which, in combination with other data, can inform assessments of compliance with the chemical weapons regime. In addition, remote sensing tools can provide inspectors with real time two- or three-dimensional images of a site prior to entry or at the point of inspection. This can facilitate on-site investigations. In the past, trade data has proven valuable in informing assessments of non-compliance with the biological weapons regime. Today, it is possible to analyse trade data through online, public databases. In combination with other methods, open-source trade data could be used to detect anomalies in the biological weapons regime. AI and the digitization of data create new ways to enhance confidence in compliance with WMD regimes. In the context of the chemical weapons regime, the digitization of the chemical industry as part of a wider shift to Industry 4.0 presents possibilities for streamlining declarations under the Chemical Weapons Convention (CWC) and for facilitating CWC regulatory requirements.
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