Meny
SPKI et et regionalt kompetansemiljø for implementering av KI for bruk i helsetjenesten som er opprettet i samarbeid mellom UNN HF, UiT Norges arktiske universitet og Helse Nord RHF.
Senteret er pasient- og klinikknært, i den forstand at satsning på utprøving, innovasjon og implementering av kliniske beslutningsstøtteverktøy, til nytte for pasienter og klinikere, er den sentrale aktiviteten ved senteret. SPKI legger til rette for at nye ideer og produkter som kan forbedre diagnostikk- og pasientbehandling blir implementert i klinikken.
Senteret skal dessuten være en brobygger som sørger for at forskningen ved UNN og UiT innen nye maskinlæringsmetoder for analyse og informasjonsuthenting fra komplekse pasientdata, koples med den medisinske kunnskapen i de kliniske miljøene på veien mot implementering. Det er nettopp denne tette organisatoriske koplingen mellom fagfolk innen teknologi og medisin, kombinert med tilgang til og bruk av oppdaterte pasientdata, som vil gjøre det mulig å utvikle kvalitetssikrede, virksomme og trygge KI-løsninger.
Å være et regionalt kompetansemiljø som kan drive med rådgivning, spre kunnskap, lage veiledninger og retningslinjer, mv
Være møteplass for klinikere og forskere med interesse for KI.
Bidra til å spre kunnskap, informasjon, erfaringer, resultater.
Utredninger for å fasilitere innføring av kunstig intelligens.
Informere om og bidra til kartlegging av KI-produkter.
Bidra til å etablere åpne forskningsdatabaser bestående av anonymisert helsedata.
Sikre samarbeid på tvers av regioner og helseforetak for optimal utnyttelse av ressurser og kompetanse.
Bygge nettverk og bidra til matchmaking mellom klinikere, forskere, bedrifter og start-ups, studenter, andre samarbeidspartnere.
Støtte fagmiljøer i utprøving og implementering av KI i klinisk virksomhet og i å utføre prospektive kliniske KI-studier.
Inngå i prosjektutviklingen – fra idé til implementering og bidra til søknader om ekstern finansiering.
Kanalisere henvendelser om KI-faglige og juridiske utfordringer.
Kompetanse på utprøving, validering og drift av KI-løsninger.
SPKI er organisert som en avdeling på Fag- og kvalitetssenteret på UNN.
Hovedbidraget fra UiT til SPKI kommer gjennom NT-fakultetet, med tyngdepunkt i Forskningsgruppa for maskinlæring og SFI Visual Intelligence, og Helsefakultetet, med tyngdepunkt ved Institutt for klinisk medisin (IKM).
Nettverk og samarbeid er svært avgjørende for å lykkes når man jobber med forskning, innovasjon og ny teknologi. Her finner dere en liste over aktører innen klinisk KI, hovedsaklig i Norge, slik at det blir enkelt for dere å nå ut til passende samarbeidspartnere.
Vær oppmerksom på at listen nedenfor ikke er fullstendig. Hvis du har forslag til flere miljøer og aktører som bør nevnes, vennligst kontakt oss.
The core strength of UiT Machine Learning Group is in basic research for advancing statistical machine learning & AI methodology to face the societal and industrial data-driven challenges of the future. The group has been doing research on machine learning for health and clinical AI for more than a decade and is a key contributor to the activity at SPKI.
Visual Intelligence is a Centre for research-based innovation that aims to be the lead provider of novel deep learning-based solutions for cutting-edge complex image analysis. One of the main innovation areas for Visual Intelligence is medicine and health. Visual Intelligence is advancing deep learning in medical imaging to solve challenges e.g. related with limited training data and explainability of black-box predictions. Hence, Visual Intelligence and SPKI operates in close synergy.
This is a large-scale interdisciplinary research consortium funded by UiT. Leveraging the development of new theory and methodological research, the aim of the consortium is to fill knowledge gaps between ML and organizational implementation of new solutions to translate basic, translational and clinical medical research, leading to high societal impact,
Kunstig intelligens i norsk helsetjeneste (KIN) is a national network for artificial intelligence in the health service, which connects professional environments from all over the country by establishing meeting places for joint discussion and exchange of knowledge about the implementation of artificial intelligence in the health sector. The network is open to anyone who wants to participate and share their work.
The Norwegian coordination project «Better use of artificial intelligence» is part of the work on the National Health and Hospital Plan 2020-2023, and will help and guide the health service so that it can succeed in using artificial intelligence in a safe way. The project is a collaboration between Helsedirektoratet, Direktoratet for e-helse, Statens legemiddelverk, Helsetilsynet, Folkehelseinstituttet, KS, and the regional health authorities.
CAI-X (Dansk center for klinisk kunstig intelligens/Danish Centre for Clinical Artificial Intelligence) is a joint center between the University of Southern Denmark (SDU) and Odense University Hospital (OUH). The center aims to ensure a close connection between patients, health care staff, researchers and engineering experts to bridge clinical challenges with technical expertise to provide new treatment solutions. SPKI has entered a strategic cooperation on clinical AI with CAI-X.
The Norwegian Centre for E-health Research will contribute to knowledge-based development in the area of e-health by means of research, collaboration and the dissemination of knowledge. The centre does research on all levels of health care: disease prevention, self-management, primary and specialist healthcare and rehabilitation.
As part of DIPS AS, Norway’s leading vendor of e-health solutions, the DIPS Research and Innovation department aims to improve electronic health record systems by applying findings from research into AI, natural language processing and IoT devices. It also aims to facilitate new patient-oriented AI applications by providing standardized interfaces and open testing environments for its products.
The Health Data Lab is hosted by UiT, and aims to provide the systems, methods, and tools needed to analyse and interpret complex health datasets. The activities at the Lab are mainly threefold; build and experimentally evaluate infrastructure systems for bioinformatics and machine learning analyses, apply bioinformatics, statistics, and machine learning methods for novel health data analyses, and build and evaluate data exploration and interpretation tools.
180°N – Norwegian Nuclear Medicine Consortium aims to strengthen research in nuclear medicine connected to the nuclear medicine equipment donated by Trond Mohn Foundation and Tromsø Research Foundation to the universities and hospitals in Trondheim, Tromsø and Bergen.
The GEMINI center MIRA: Medical Imaging Research and AI is established to bring together researchers and clinical experts working with medical image analysis at NTNU, St. Olavs hospital, and SINTEF. The center focuses on artificial intelligence applications for MR, ultrasound, and CT imaging.
PRESIMAL is a national network for PRESision Imaging and MAchine Learning funded by Nasjonal samarbeidsgruppe for helseforskning i spesialisthelsetjenesten (NSG), lead and coordinated by Mohn Medical Imaging and Visualization Centre (MMIV). PRESIMAL-partners are research groups from all Health Regions and their local universities, and the national networks Norwegian Artificial Intelligence Research Consortium (NORA), Kunstig Intelligens i Norsk Helsetjeneste (KIN), Eitri Medical Incubator and Center for Patient-centered AI (SPKI). PRESIMAL aims to promote research related to medical AI with a special focus on the clinial implementation of precision imaging and machine learning . The network supports researcher exchange through travel grants, educational activities by organizing interdisclipinary autumn research schools, fascilitatation and collaboration in hackatons, conferences, workshops and seminars. PRESIMAL is open to include further participants from across Norway and welcome research groups and networks to get in touch.
CAIR – Centre for Artificial Intelligence Research is hosted by the Department of ICT at the University of Agder. The centre was opened on 2nd of March 2017 and aims to address unsolved problems and push the research frontier, seeking superintelligence by providing an attractive environment for cutting-edge research.
The Artificial intelligence and digital pathology in cancer (AICAN) is a cross disciplinary research group, which incorporates NTNU, St. Olavs hospital, Levanger hospital and SINTEF. The group aims to explore the application of artificial intelligence for interpreting histopathological slides from cancer.
The Mohn Medical Imaging and Visualization Centre (MMIV) is a joint centre between the University of Bergen and the Haukeland University Hospital. The centre aims to research new methods in quantitative imaging and interactive visualization so as to predict changes in health and disease across spatial and temporal scales, which encompasses research in tissue feature detection, feature extraction and feature prediction.
CRAI group at Oslo University Hospital act as a research hub within the Division of Radiology and Nuclear Medicine, with research group members spanning a wide range of disciplines and research areas, who are interested in using AI and other advanced computational tools as an integral part of their research.
The Norwegian Open Artificial Intelligence Lab (NAIL) centre is located in Trondheim, and hosted by NTNU, Faculty of Information Technology and Electrical Engineering. NAIL is a hub for research, education, and innovation within AI.
The Institute for Cancer Genetics and Informatics (ICGI) is a department within the Division of Cancer Medicine at Oslo University Hospital (OUS). The ICGI research applicability and uses of artificial intelligence so as to develop method for cancer treatment focusing on digital image analysis. The goal is to enable better cancer treatment through diagnostics based on artificial intelligence (AI).
© Kopibeskyttelse SPKI. Alle rettigheter reservert | Nettside levert av Nettrakett.no
Cookie | Duration | Description |
---|---|---|
cookielawinfo-checkbox-analytics | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics". |
cookielawinfo-checkbox-functional | 11 months | The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". |
cookielawinfo-checkbox-necessary | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary". |
cookielawinfo-checkbox-others | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. |
cookielawinfo-checkbox-performance | 11 months | This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance". |
viewed_cookie_policy | 11 months | The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data. |