Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
The document discusses the use of generative AI in healthcare. It defines generative AI as technology that can generate diverse content like images, text, and audio. Generative AI uses neural networks to identify patterns in data and generate new content. It has various applications in healthcare like drug discovery, medical imaging, disease diagnosis, and medical research. The document outlines several use cases of generative AI and factors contributing to its growth in healthcare. It predicts generative AI will continue transforming healthcare by enabling personalized medicine, virtual clinical trials, and a deeper understanding of human health.
Artificial Intelligence In Medical IndustryDataMites
The document discusses the use of artificial intelligence and machine learning in the medical industry. It describes how AI can be used to analyze and understand complex medical data, aiding in tasks like cancer diagnosis, drug development through protein folding, and detecting heart diseases using smartwatches. The document also lists several other medical applications of AI such as diagnostic decision support, self-diagnosis through AI doctors, monitoring medication usage, detecting hospital infections through computer vision, and using AI to treat social anxiety.
Artificial intelligence is disrupting healthcare in several ways:
- AI is improving disease prediction, customized medicine development, and other areas of human biology.
- The growth of AI in healthcare is driven by factors like increased funding, demand for precision medicine, and cost reductions, allowing for more accurate and early disease diagnosis.
- However, some end users are reluctant to adopt AI healthcare technologies due to lack of trust and potential risks, though AI also offers opportunities to improve outcomes for patients and in emerging markets.
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
This document presents an MSc thesis on big data in healthcare. It discusses how the healthcare sector is generating large amounts of data and how big data can be used in healthcare. The document outlines a plan to first discuss why big data is important in healthcare, providing examples of data usage history and current applications. It then details how big data can be collected, processed and analyzed in the healthcare sector using tools like Hadoop, Hive, Pig and Sqoop. The future potential of big data in healthcare is also envisioned, with real-time uses.
The document discusses machine learning in healthcare and life sciences. It provides an overview of machine learning techniques like classification, regression, and clustering. It then discusses IBM's Watson Data Platform and Data Science Experience for developing and deploying machine learning models at scale. The document presents a case study on using deep learning for lung cancer detection from medical images. It concludes with recommendations for applying machine learning including the importance of data and shortening development cycles.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Artificial intelligence enters the medical fieldRuchi Jain
In the medical and health field, artificial intelligence can help reduce the cost of ongoing health operations, and can have an impact on the quality of medical care for patients everywhere. By diagnosing diseases earlier, AI can also improve patient outcomes. No matter how you look at it, artificial intelligence has great potential in healthcare.
Generative AI in Healthcare Market - Copy - Copy.pptxGayatriGadhave1
The document discusses the use of generative AI in healthcare. It defines generative AI as technology that can generate diverse content like images, text, and audio. Generative AI uses neural networks to identify patterns in data and generate new content. It has various applications in healthcare like drug discovery, medical imaging, disease diagnosis, and medical research. The document outlines several use cases of generative AI and factors contributing to its growth in healthcare. It predicts generative AI will continue transforming healthcare by enabling personalized medicine, virtual clinical trials, and a deeper understanding of human health.
Artificial Intelligence In Medical IndustryDataMites
The document discusses the use of artificial intelligence and machine learning in the medical industry. It describes how AI can be used to analyze and understand complex medical data, aiding in tasks like cancer diagnosis, drug development through protein folding, and detecting heart diseases using smartwatches. The document also lists several other medical applications of AI such as diagnostic decision support, self-diagnosis through AI doctors, monitoring medication usage, detecting hospital infections through computer vision, and using AI to treat social anxiety.
Artificial intelligence is disrupting healthcare in several ways:
- AI is improving disease prediction, customized medicine development, and other areas of human biology.
- The growth of AI in healthcare is driven by factors like increased funding, demand for precision medicine, and cost reductions, allowing for more accurate and early disease diagnosis.
- However, some end users are reluctant to adopt AI healthcare technologies due to lack of trust and potential risks, though AI also offers opportunities to improve outcomes for patients and in emerging markets.
Artificial intelligence (AI) is already transforming healthcare. It's an invaluable tool, capable of storing and processing vast amounts of data almost simultaneously. AI allows for rapid and accurate diagnosis, early detection, advanced research and much more.
This document presents an MSc thesis on big data in healthcare. It discusses how the healthcare sector is generating large amounts of data and how big data can be used in healthcare. The document outlines a plan to first discuss why big data is important in healthcare, providing examples of data usage history and current applications. It then details how big data can be collected, processed and analyzed in the healthcare sector using tools like Hadoop, Hive, Pig and Sqoop. The future potential of big data in healthcare is also envisioned, with real-time uses.
The document discusses machine learning in healthcare and life sciences. It provides an overview of machine learning techniques like classification, regression, and clustering. It then discusses IBM's Watson Data Platform and Data Science Experience for developing and deploying machine learning models at scale. The document presents a case study on using deep learning for lung cancer detection from medical images. It concludes with recommendations for applying machine learning including the importance of data and shortening development cycles.
Healthcare AI will undoubtedly become one of the fastest growing industries in the industry. Although the medical and health artificial intelligence industry was valued at US$ 600 million in 2014 , it is expected to reach a staggering US$ 150 billion by 2026. There are countless AI applications in the healthcare industry, let’s look at some outstanding ones.
Artificial intelligence has great potential to revolutionize healthcare. It can help predict ICU transfers and hospital readmissions by identifying at-risk patients from their medical data. AI is also used in medical testing through new methods like bloodless blood testing using smartphone ECGs. It improves clinical workflows by reducing physician burnout through tools like vein finders. AI helps prevent infections by monitoring patients for early signs of sepsis or other healthcare-acquired infections. During the COVID-19 pandemic, AI has assisted with tracking and forecasting outbreaks, diagnosing patients, processing health claims, and developing new drugs to treat the virus.
This presentation was given at the Manufacturing Industry Trade Show hosted by the Idaho Manufacturing Alliance on Dec 2nd 2021. Please feel to reach out if you are interested in having a similar presentation delivered to your company or community.
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
AI is increasingly being used in the healthcare sector to address various challenges. It has applications ranging from early disease detection using medical data mining to aiding drug discovery. While major technology companies like IBM, Google, and Microsoft are actively working on developing AI solutions for healthcare, there are also numerous startups in this space. However, adoption of AI in healthcare is still at an early stage due to challenges like lack of digitization of patient records in some regions and fears around job losses. As more data becomes available and technologies advance, AI is expected to play a transformative role in improving healthcare outcomes and efficiency.
This document discusses the application of machine learning in healthcare. It begins with an introduction of the author and their background in machine learning engineering. It then discusses the UN Sustainable Development Goals around health and highlights non-communicable and infectious diseases as areas machine learning could help address. The document outlines how machine learning can help expand medical knowledge, disseminate information, enable personalized medicine, and increase patient engagement. It also discusses best practices for business understanding, data modeling, and feature engineering when applying machine learning in healthcare.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
AI in pharmacy: Revolutionizing HealthcareDarvan Shvan
Explore the revolutionary impact of AI in pharmacy on Skillshare! Dive into the synergy of technology and healthcare, discovering AI's role in drug discovery, personalized medicine, and telepharmacy. Uncover how predictive analytics enhances patient outcomes, while addressing ethical considerations and future trends.
The State of Artificial Intelligence in Sales and Marketing Gabe Larsen
Artificial Intelligence vs the Sales Rep is the new debate that’s heating up on social networks today. Is artificial intelligence going to replace the role of the sales development representative and all that it entails?
Are buyers really looking to interact with systems, rather than people?
Will an AI ever be able to master complex human interactions such as business transactions?
At what point in the buyer’s journey is a salesperson absolutely necessary?
AI is all the hype these days, with some believing it can do everything better than humans -- even selling. At the same time, trends show that every decently sized sales organization is betting on sales development representatives to fill their pipeline with hot leads.
In a recent survey created in collaboration with AA-ISP, Tenbound and Vengreso, InsideSales.com asked sales leaders whether AI will replace sales development reps-- and an overwhelming majority of 78% said “NO.”
What’s your opinion? Will AI end up replacing some sales development roles, or will it enable and support them, changing the way they do their day-to-day interaction?
Join the discussion to learn:
> How is AI being used in Sales
> What are the sales and marketing trends in AI for 2019
> Sneak preview of the 2019 State of AI Research
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
The document discusses health IT and smart hospitals. It provides biographical information about the speaker, including their medical education and research interests in health IT for quality of care, social media, IT management, security and privacy. The outline indicates the talk will cover the road to digitizing healthcare, what constitutes a "smart hospital", and moving toward a smart hospital.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Searching for chemical information using PubChemSunghwan Kim
Presented at the 257th American Chemical Society (ACS) National Meeting in Orlando, FL (April 1, 2019). [CHED 303]
==== Abstract ====
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database, which provides information on a broad range of chemical entities, including small molecules, lipids, carbohydrates, and (chemically-modified) amino acid and nucleic acid sequences (including siRNA and miRNA). With three million unique users per month at peak, PubChem is ranked as one of the most visited chemistry websites in the world. A substantial number of PubChem users are between ages 18 and 24, who are likely to be undergraduate or graduate students at academic institutions. Therefore, PubChem has a great potential as an online resource for chemical education. In this talk, we will present “PubChem Search”, a new web interface that allows users to quickly find desired chemical information. This interface supports chemical name search as well as various types of chemical structure search, including identity/similarity search, superstructure/substructure search, and molecular search. Using PubChem Search, it is also possible to search for journal articles or patent documents that mention a given chemical. The hits returned from a search can be downloaded to local machines or further refined or analyzed in conjunction with other PubChem tools and services. In this presentation, we will demonstrate how the PubChem Search interface can be used to search beyond google for chemical information of interest.
Artificial intelligence has wide applications in medical imaging for analyzing images. Deep learning is commonly used for tasks like automated detection, segmentation, and classification of lesions or abnormalities from medical images. While AI systems have achieved high accuracy in some narrow tasks like nodule detection, integrating AI safely into clinical practice poses challenges regarding data privacy, system transparency, and regulatory approval. Overall AI has potential to improve healthcare by making imaging interpretation more efficient and accurate, but careful management of technology and change is needed.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
The document discusses how artificial intelligence can help address challenges posed by infectious diseases. It describes how AI uses past disease data to predict outbreaks, and how algorithms created from behavioral and epidemiological data can help target prevention efforts. The document also outlines several successes of AI in predicting disease outbreaks like dengue fever in advance. Overall, the document advocates that AI has great potential to help monitor infectious diseases and facilitate more proactive public health responses if its tools are developed and applied effectively.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
This document provides an overview of artificial intelligence and its applications in healthcare. It begins with definitions of AI and machine learning. It then reviews the history of AI from ancient times to recent developments. Current uses of AI in healthcare discussed include predictive analytics, disease detection via pattern recognition, patient self-monitoring, and scheduling. Barriers to the adoption of AI in healthcare and future applications are also mentioned.
Artificial intelligence is being used in healthcare in several ways: to detect diabetic retinopathy from retinal images, enable low-dose CT scans with improved image quality, and analyze chest CT scans and patient data to rapidly detect COVID-19. Startups are also applying AI to portable retinal imaging devices and AI-powered robots are being used to screen for COVID-19 in hospitals. Going forward, AI systems across hospitals will share aggregated clinical data to continuously learn and identify new medical patterns that can improve diagnosis and treatment.
Wenn Maschinen Menschen bewerten: To-dos für TeilhabeKonrad Lischka
Politischer und gesellschaftlicher Handlungsbedarf bei maschinellen Entscheidungen / Prozessen algorithmischer Entscheidungsfindung.
Gehalten bei der Konferenz "Das ist Netzpolitik!", 1.9.2017
Artificial intelligence has great potential to revolutionize healthcare. It can help predict ICU transfers and hospital readmissions by identifying at-risk patients from their medical data. AI is also used in medical testing through new methods like bloodless blood testing using smartphone ECGs. It improves clinical workflows by reducing physician burnout through tools like vein finders. AI helps prevent infections by monitoring patients for early signs of sepsis or other healthcare-acquired infections. During the COVID-19 pandemic, AI has assisted with tracking and forecasting outbreaks, diagnosing patients, processing health claims, and developing new drugs to treat the virus.
This presentation was given at the Manufacturing Industry Trade Show hosted by the Idaho Manufacturing Alliance on Dec 2nd 2021. Please feel to reach out if you are interested in having a similar presentation delivered to your company or community.
APPLICATION OF DATA SCIENCE IN HEALTHCAREAnnaAntony16
About the application of data science in healthcare. Healthcare is an essential field that touches on people's lives in many ways, and it has been revolutionized by data science over the years. Data science has enabled healthcare providers to better understand patients' needs, identify the root causes of diseases, and design effective treatment plans.
This talk gives an introduction about Healthcare Use cases - The AI ladder and Lifestyle AI at Scale Themes The iterative nature of the workflow and some of the important components to be aware in developing AI health care solutions were being discussed. The different types of algorithms and when machine learning might be more appropriate in deep learning or the other way will also be discussed. Use cases in terms of examples are also shared as part of this presentation .
AI is increasingly being used in the healthcare sector to address various challenges. It has applications ranging from early disease detection using medical data mining to aiding drug discovery. While major technology companies like IBM, Google, and Microsoft are actively working on developing AI solutions for healthcare, there are also numerous startups in this space. However, adoption of AI in healthcare is still at an early stage due to challenges like lack of digitization of patient records in some regions and fears around job losses. As more data becomes available and technologies advance, AI is expected to play a transformative role in improving healthcare outcomes and efficiency.
This document discusses the application of machine learning in healthcare. It begins with an introduction of the author and their background in machine learning engineering. It then discusses the UN Sustainable Development Goals around health and highlights non-communicable and infectious diseases as areas machine learning could help address. The document outlines how machine learning can help expand medical knowledge, disseminate information, enable personalized medicine, and increase patient engagement. It also discusses best practices for business understanding, data modeling, and feature engineering when applying machine learning in healthcare.
artificial intelligence in health care. how it is different from traditional techniques. growth of artificial intelligence. how hospitals are taping artificial intelligence to mange corona virus. pros and cons of artificial intelligence.
AI in pharmacy: Revolutionizing HealthcareDarvan Shvan
Explore the revolutionary impact of AI in pharmacy on Skillshare! Dive into the synergy of technology and healthcare, discovering AI's role in drug discovery, personalized medicine, and telepharmacy. Uncover how predictive analytics enhances patient outcomes, while addressing ethical considerations and future trends.
The State of Artificial Intelligence in Sales and Marketing Gabe Larsen
Artificial Intelligence vs the Sales Rep is the new debate that’s heating up on social networks today. Is artificial intelligence going to replace the role of the sales development representative and all that it entails?
Are buyers really looking to interact with systems, rather than people?
Will an AI ever be able to master complex human interactions such as business transactions?
At what point in the buyer’s journey is a salesperson absolutely necessary?
AI is all the hype these days, with some believing it can do everything better than humans -- even selling. At the same time, trends show that every decently sized sales organization is betting on sales development representatives to fill their pipeline with hot leads.
In a recent survey created in collaboration with AA-ISP, Tenbound and Vengreso, InsideSales.com asked sales leaders whether AI will replace sales development reps-- and an overwhelming majority of 78% said “NO.”
What’s your opinion? Will AI end up replacing some sales development roles, or will it enable and support them, changing the way they do their day-to-day interaction?
Join the discussion to learn:
> How is AI being used in Sales
> What are the sales and marketing trends in AI for 2019
> Sneak preview of the 2019 State of AI Research
AI in Healthcare | Future of Smart Hospitals Renee Yao
In this talk, I specifically talk about how NVIDIA healthcare AI software and hardware were used to support healthcare AI startups' innovation. Three startups featured: Caption Health, Artisight, and Hyperfine. Audience: healthcare systems CXOs.
The document discusses health IT and smart hospitals. It provides biographical information about the speaker, including their medical education and research interests in health IT for quality of care, social media, IT management, security and privacy. The outline indicates the talk will cover the road to digitizing healthcare, what constitutes a "smart hospital", and moving toward a smart hospital.
Artificial intelligence in health care by Islam salama " Saimo#BoOm "Dr-Islam Salama
A Lecture about basics and concepts of Artificial Intelligence in health care & there applications
محاضرة عامة حول الذكاء الإصطناعي وأساسياته في الرعاية الصحية والطبية وتطبيقاته
Searching for chemical information using PubChemSunghwan Kim
Presented at the 257th American Chemical Society (ACS) National Meeting in Orlando, FL (April 1, 2019). [CHED 303]
==== Abstract ====
PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database, which provides information on a broad range of chemical entities, including small molecules, lipids, carbohydrates, and (chemically-modified) amino acid and nucleic acid sequences (including siRNA and miRNA). With three million unique users per month at peak, PubChem is ranked as one of the most visited chemistry websites in the world. A substantial number of PubChem users are between ages 18 and 24, who are likely to be undergraduate or graduate students at academic institutions. Therefore, PubChem has a great potential as an online resource for chemical education. In this talk, we will present “PubChem Search”, a new web interface that allows users to quickly find desired chemical information. This interface supports chemical name search as well as various types of chemical structure search, including identity/similarity search, superstructure/substructure search, and molecular search. Using PubChem Search, it is also possible to search for journal articles or patent documents that mention a given chemical. The hits returned from a search can be downloaded to local machines or further refined or analyzed in conjunction with other PubChem tools and services. In this presentation, we will demonstrate how the PubChem Search interface can be used to search beyond google for chemical information of interest.
Artificial intelligence has wide applications in medical imaging for analyzing images. Deep learning is commonly used for tasks like automated detection, segmentation, and classification of lesions or abnormalities from medical images. While AI systems have achieved high accuracy in some narrow tasks like nodule detection, integrating AI safely into clinical practice poses challenges regarding data privacy, system transparency, and regulatory approval. Overall AI has potential to improve healthcare by making imaging interpretation more efficient and accurate, but careful management of technology and change is needed.
AI Basic, AI vs Machine Learning vs Deep Learning, AI Applications, Top 50 AI Game Changer Solutions, Advanced Analytics, Conversational Bots, Financial Services, Healthcare, Insurance, Manufacturing, Quality & Security, Retail, Social Impact, and Transportation & Logistics
The document discusses how artificial intelligence can help address challenges posed by infectious diseases. It describes how AI uses past disease data to predict outbreaks, and how algorithms created from behavioral and epidemiological data can help target prevention efforts. The document also outlines several successes of AI in predicting disease outbreaks like dengue fever in advance. Overall, the document advocates that AI has great potential to help monitor infectious diseases and facilitate more proactive public health responses if its tools are developed and applied effectively.
The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes as well as improve patient outcomes.
AI analyzes data throughout a healthcare system to mine, automate and predict processes. Some of the use cases are :
1. Early Diagnosis of diseases
2. Improved clinical trial processes
3. Mental health apps etc.
This document provides an overview of artificial intelligence and its applications in healthcare. It begins with definitions of AI and machine learning. It then reviews the history of AI from ancient times to recent developments. Current uses of AI in healthcare discussed include predictive analytics, disease detection via pattern recognition, patient self-monitoring, and scheduling. Barriers to the adoption of AI in healthcare and future applications are also mentioned.
Artificial intelligence is being used in healthcare in several ways: to detect diabetic retinopathy from retinal images, enable low-dose CT scans with improved image quality, and analyze chest CT scans and patient data to rapidly detect COVID-19. Startups are also applying AI to portable retinal imaging devices and AI-powered robots are being used to screen for COVID-19 in hospitals. Going forward, AI systems across hospitals will share aggregated clinical data to continuously learn and identify new medical patterns that can improve diagnosis and treatment.
Wenn Maschinen Menschen bewerten: To-dos für TeilhabeKonrad Lischka
Politischer und gesellschaftlicher Handlungsbedarf bei maschinellen Entscheidungen / Prozessen algorithmischer Entscheidungsfindung.
Gehalten bei der Konferenz "Das ist Netzpolitik!", 1.9.2017
Perspectives on integrating the ICF into the Learning Healthcare System presented at the rehaKIND congress in Düsseldorf, Germany on February 5th 2015.
Pressemitteilung des AOK-Bundesverbandes: AOK-Umfrage zur digitalen Gesundhe...AOK-Bundesverband
Mehr als die Hälfte der Menschen in Deutschland tut sich schwer damit, Gesundheitsinformationen im Internet zu finden, zu verstehen, und für sich zu nutzen. Das zeigt eine Studie der AOK zur digitalen Gesundheitskompetenz, die am Dienstag (8. Dezember) veröffentlicht wurde. Das Institut Skopos hat im Auftrag des AOK-Bundesverbandes bundesweit 8500 Frauen und Männer zwischen 18 und 75 Jahren befragt. Der Vorstandsvorsitzende Martin Litsch forderte angesichts der Ergebnisse "verlässliche und leicht verständliche Informationsangebote im Netz".
Als Anbieter oder Dienstleister im Markt für Medizintechnik suchen Sie nach einer Strategie, um die Herausforderungen der Zukunft, beispielsweise die digitale Revolution oder einen sich durch das Auftreten neuer Player intensivierenden Wettbewerb, erfolgreich meistern zu können? In unserem Market Foresight stellen wir Ihnen die wichtigsten Trends und Technologien vor, die den Markt für Medizintechnik in den nächsten fünf bis fünfzehn Jahren prägen werden. Außerdem zeigen wir Ihnen anhand von fünf exemplarischen Visionskandidaten, wie ein MedTech-Unternehmen sich ausrichten könnte, um Zukunftschancen zu nutzen und mit neuen Geschäftsmodellen weiterhin erfolgreich im Markt zu bestehen.
Market Foresight Medizintechnik 2030 - Trends, Herausforderungen, Visionskand...FutureManagementGroup AG
Als Anbieter oder Dienstleister im Markt für Medizintechnik suchen Sie nach einer Strategie, um die Herausforderungen der Zukunft, beispielsweise die digitale Revolution oder einen sich durch das Auftreten neuer Player intensivierenden Wettbewerb, erfolgreich meistern zu können? In unserem Market Foresight stellen wir Ihnen die wichtigsten Trends und Technologien vor, die den Markt für Medizintechnik in den nächsten fünf bis fünfzehn Jahren prägen werden. Außerdem zeigen wir Ihnen anhand von fünf exemplarischen Visionskandidaten, wie ein MedTech-Unternehmen sich ausrichten könnte, um Zukunftschancen zu nutzen und mit neuen Geschäftsmodellen weiterhin erfolgreich im Markt zu bestehen.
Big Data verändert auch die Medizin. Diego Kuonen, Professor für Datenwissenschaft, weiss, wie.
Der Artikel ist online verfügbar unter https://www.1815.ch/news/newsletter/wb/diego-kuonen/
In der Textarbeit wird erläutert, wie Arbeit und Leben in Zukunft aussehen werden. Beschrieben wird nicht nur die Entwicklung in Deutschland, sondern in ganz Europa. Dabei wird einiges auch skeptisch bzw. kritisch betrachtet. Die Künstliche Intelligenz spielt eine sehr große Rolle in unserer Arbeit und es wird ebenfalls auf die Gefahren bzw. Problematiken dieser hingewiesen.
Status
Es gibt kaum eine Branche, die von den Möglichkeiten
der digitalen Kommunikation in den nächsten Jahren
so profitieren wird wie die Gesundheitsbranche. Ob
häusliche Überwachung oder aktive Selbstkontrolle, ob
vernetzte Gesundheitsakten und datengestützte Diagnosen
oder einfach nur Informationsaustausch und sozialer
Dialog zu Krankheitsbildern – die Liste der Anwendungen
und Maßnahmen wird immer länger. So gibt es
bereits über 40.000 Apps im medizinischen Bereich für
Smartphones und Tablets, angefangen von Beratungs- und
Telemedizindiensten, über einfache Monitoring- und
Erinnerungsfunktionen, bis hin zu professionellen Anwendungen für Ärzte und medizinisches Personal. Auch
die medizinischen Geräte selbst erweitern ihr Leistungsspektrum um den mobilen Datentransfer und tragen somit zur Effizienzsteigerung bei der Behandlung bei.
Ähnlich wie Künstliche Intelligenz in der Medizin: Wo stehen wir – wo geht es hin? (20)
Zur Relevanz von Spiritualität bei Krebserkrankungen – Einsichten aus Seelsor...Vito Mediavilla
Dr. theol. Thomas Wild, Geschäftsleiter des AWS, Institut für Aus- und Weiterbildung in Seelsorge, Spiritual Care und Pastoralpsychologie, Universität Bern. 20. Treffen der GIST-Gruppe Schweiz
GIST-Chirurgie: Wann ist beim GIST das Messer den „Pillen“ überlegen?Vito Mediavilla
Prof. Dr. Dr. h.c. Markus Weber (Stadtspital Triemli, Zürich) spricht von 3 chirurgischen Ausgangslagen und belegt diese mit konkreten Fallbeispielen aus seiner Praxis. Vortrag beim 16. Treffen der GIST-Gruppe Schweiz 2019.
Quality of Life / Palliative Care: Was bietet die palliative Medizin in der S...Vito Mediavilla
Dr. Raoul Pinter (Kantonsspital St. Gallen) betont, dass er den Begriff ‚palliative care’ dem Begriff ‚palliative medicine’ vorzieht. Nach seinem Verständnis geht es um den Schutz von kranken Menschen. Vortrag beim 16. Treffen der GIST-Gruppe Schweiz 2019.
PD Dr. Dr. Matthias Matter (Universitätsspital Basel) betont, dass das Wissen über genetische Veränderungen stark zugenommen hat, u.a. wegen besserer technischer Geräte (NGS). Vortrag beim 16. Treffen der GIST-Gruppe Schweiz 2019.
Auch wir sind Sternenstaub – was uns die Raumsonde Rosetta über unsere Herkun...Vito Mediavilla
Prof. Dr. Kathrin Altwegg (Weltraumforscherin, Projektleiterin „Kometensonde Rosetta“, Universität Bern) berichtet von der Mission Rosetta, an der seit 1993 in der ESA gearbeitet und die 2004 gestartet wurde. Vortrag beim 16. Treffen der GIST-Gruppe Schweiz 2019.
Auch wir sind Sternenstaub – was uns die Raumsonde Rosetta über unsere Herkun...
Künstliche Intelligenz in der Medizin: Wo stehen wir – wo geht es hin?
1. Künstliche Intelligenz in der Medizin:
Wo stehen wir - wo geht es hin?
Claudia Witt, MD, MBA
Co-Direktorin und Professorin
Digital Society Initiative
Universität Zürich
20. Treffen der GIST-Gruppe Schweiz, Verein zur Unterstützung von Betroffenen mit GIST
We shape the digital future
The UZH Digital Society Initiative
2. Themen des Vortrags
Was macht die DSI ?
Was ist Künstliche Intelligenz (KI)?
Was ist heute schon möglich?
Wie kann die Zukunft aussehen?
3. DSI Netzwerk:
> 900 Mitglieder
> 200 Professor:innen
Diverse Disziplinen, ein Thema:
Digitalisierung
Kompetenzzentrum der UZH zur Digitalisierung
4. Die DSI fokussiert auf drei Kernthemen
Forschung Bildung Öffentlichkeitsarbeit
«Digitale Transformation bedeutet, dass sämtliche Lebensvollzüge
in einem Informationssystem abgebildet werden»
7. Künstliche Intelligenz (KI)
Was ist KI?
«Künstliche Intelligenz» bezeichnet den Versuch, Verstehen
und Lernen mit Software nachzubilden. Heutige Systeme
(«neuronale Netze») lernen ihre Fähigkeiten aus grossen
Datenmengen.
Was kann KI (heute):
KI-Systeme sind heute bereits in vielen Anwendungen
«versteckt»:
- Übersetzungs-Software («DeepL»)
- Suchmaschinen
- Erzeugung von Bildern und Musik
- Erzeugung von Texten («Chat-GPT»)
8. Beispiel Chat-GPT - Sprachmodell
Was? KI-gestütztes Sprachmodell, das in der Lage ist, menschenähnliche Antworten auf eine
Vielzahl von Fragen und Aufforderungen zu generieren. Deep-Learning-Algorithmus, der häufig
im Bereich der Verarbeitung natürlicher Sprache (NLP) eingesetzt wird.
Wie? Wiederholt Muster, die es in seinen Trainingsdaten (riesigerTextdatenbestand aus dem
Internet) gesehen hat. Hat ein breites Spektrum von Mustern und Beziehungen zwischen
Wörtern, Ausdrücken und Sätzen gelernt.
Limitationen? Bias, unsinnige oder irrelevante Antworten
11. KI-Medizin | Eine neue Realität vor der Haustür
Screening
Krankenkassen
bieten mobile
Apps zur
Überprüfung von
Symptomen an
(Filter vor dem
Arzt).
Vor der Erkrankung Krankheit Post-Krankheit
Prävention
Wearables-Daten und Daten von
Nicht-Gesundheitsgeräten werden den
Hausärzten vorgelegt.
Diagnose
KI-Software für
die Radiologie
unterstützt die
Bildanalyse und
-interpretation
oder führt sie
durch
Monitoring
Fernüberwachung ermöglichen eine frühere Entlassung der
Patienten aus der Klinik.
Therapie
Systeme zur
Unterstützung
klinischer
Entscheidungen
schlagen die
besten Behand-
lungsoptionen für
einen bestimmten
Patienten vor.
Modalität
Telemedizin
Nutzung von Tele-
kommunikations-
technologien für die
Ausführung medizi-
nischer Aufgaben
durch Kliniker.
Automatisierte
Medizin/Pflege
Bereitstellung medi-
zinischer Funktionen
ohne die Beteiligung
einer medizinischen
Fachkraft.
„Digitaler Zwilling“
Modellierung des Patienten für Zwecke von
Prävention, Diagnose und Therapie.
12. DSI Strategie-Lab | Ansatz
Vorbereitung Mini-Umfrage
Analyse und
Schreiben
März-April 2022
Strukturierter Ansatz,
Rekrutierung von
Experten aus klinischen,
akademischen und
anderen Bereichen
Ziel: Methodisches
Vorgehen
April-Mai 2022
Befragung von
Mitgliedern des
medizinischen
Lehrkörpers der UZH
(Professoren und
Assistenzprofessoren) zu
AI im klinischen Einsatz
Ziel: Überblick über
klinische KI-
Anwendungen in
Zürcher
Universitätsspitälern
Oktober 2022 - März
2023
Zweiter Workshop mit
Experten aus klinischen,
akademischen und
anderen Bereichen, um
über Fälle nachzudenken
und das Weißbuch
fertigzustellen
Ziel:
Empfehlungen
Materialien
Publikation
Fokusgruppen
August-September 2022
Auf der Grundlage von
Fällen, qualitativen
Fokusgruppen, die über
kritische Fragen von
Fallszenarien
nachdenken.
Ziel: Bürger-/PhD
Perspektiven sammeln
Erstellung von
Fällen
Juni 2022
Fallszenarien entlang der
Zeitachse (aktuell, +5-10
Jahre, +25 Jahre) der
medizinischen
Funktionen
• Prävention
• Diagnose und
Prognose
• Therapie und
Vorhersage
• Zuweisung von
Ressourcen
Ziel: Szenariobasierte
Fälle
Bevölkerungs-
Umfrage
März-Mai 2023
Befragung der
Bevölkerung
Ziel: Meinung der
Bevölkerung zu digitalen
Zwillingen /
«Validierung» der
Empfehlungen
13. Jetzt (Bezeichnungen sind fiktiv)
• Krebspräventionsakte (IKA): Daten aus der eigenen medizinischen Versorgung (z.B.
Krankengeschichte, Laborwerte, Bildgebung) aber auch Daten von so genannten «wearables» (z.B.
smart watches) werden zur personalisierten IKA zusammengeführt.
• Individuelle Empfehlungen zu Verbesserung des Risikos mit KI-Algorithmen, die auf den Daten
von IKA basieren.
• Forschungsdatenbank CanCon: IKA-Daten von erkranken Personen in anonymisierter Form, um
Therapien mit besten Erfolgsaussichten zu berechnen.
14. Nahe Zukunft (Bezeichnungen sind fiktiv)
IKA wird von 75% der Bevölkerung genutzt und Datenquellen wurden ausgeweitet und (Sensoren
im Alltag; Umweltdaten) werden einbezogen.
Chatbot mit IKA verknüpft gibt Tipps für den Alltag, angepasst an die entsprechende Situation mit
dem Ziel, Krebsrisiken zu minimieren.
CanCon enthält systematische Daten über Therapie-Erfolge; IKA-Daten können eingespeist
werden; KI-System erstellt eine Therapie-Empfehlung; von Ärzt:innen prüfen Empfehlung und
setzen sie um.
«Gesundheits-Coach» neuer Beruf als «Übersetzer»
Krankenversicherungen: IKA-Nutzer haben billigere Prämien.
15. Ferne Zukunft
Digitale Zwillinge: umfassende Simulationen des Körpers eines Menschen. Der digitale
Zwilling wird über Sensoren und andere Datenquellen laufend mit Daten des «echten»
Zwillings versorgt.
Der Einzelne kann simulieren, d.h. in die Zukunft schauen.
Ärzt*innen nutzen digitale Zwillinge für Therapieentscheidungen
17. Zusammenfassung «Digitaler Zwilling»
«Digitale Zwillings-Services» könnten folgende Funktionen erfüllen:
1. Sie unterstützen eine medizinische Diagnose (z.B., Identifikation von Parametern, die sich
für die Diagnose einer möglicherweise entstehenden Krankheit eignen).
2. Sie unterstützen die Prognose einer Krankheit (z.B., Vorhersage des möglichen Verlaufs
einer Krankheit abgestimmt auf individuellen Merkmale und Umgebungsfaktoren).
3. Sie unterstützen die Therapie einer Krankheit (z.B., Simulation der möglichen Wirkungen
verschiedener Therapien bei einer Person).
4. Sie unterstützen die Prävention von Erkrankungen und erlauben die Identifizierung
geeigneter Präventionsmassnahmen (z.B. Information einer Person, welche Folgen gewisse
Verhaltensweisen haben könnten).
18. Ziele, die man mit einem «Digitalen Zwilling» (und einem digitalisierten Gesundheitswesen
generell) erreichen will:
Die Bürgerinnen und Bürger entscheiden über Erzeugung, Datenquellen, Ausgestaltung,
Nutzungsart und Lebensdauer ihrer persönlichen Digitale-Zwillings-Services.
DSI Strategy Lab Empfehlungen zum Digitalen Zwilling
(Auswahl 1)
19. Ziele, die man mit einem «Digitalen Zwilling» (und einem digitalisierten Gesundheitswesen
generell) erreichen will:
Digitale-Zwillings-Services sind in die interprofessionellen Behandlungsteams integriert, in
denen die nötigen Kompetenzen vorliegen und die Verantwortlichkeiten geklärt sind.
Digitaler
Zwilling?
DSI Strategy Lab Empfehlungen zum Digitalen Zwilling
(Auswahl 2)
20. Ziele, die man mit einem «Digitalen Zwilling» (und einem digitalisierten Gesundheitswesen
generell) erreichen will:
Die Anbieter von Digitale-Zwillings-Services haben gemäss open data Prinzipien (offene
Standards, Interoperabilität) Zugang zu möglichst vielen anonymisierten Gesundheitsdaten.
DSI Strategy Lab Empfehlungen zum Digitalen Zwilling
(Auswahl 3)
21. Ziele, die man mit einem «Digitalen Zwilling» (und einem digitalisierten Gesundheitswesen
generell) erreichen will:
Der Staat sorgt für die Bereitstellung einer Dateninfrastruktur, mittels derer Bürgerinnen und
Bürger Datenquellen sämtlicher Lebensbereiche zusammenführen können.
DSI Strategy Lab Empfehlungen zum Digitalen Zwilling
(Auswahl 4)
22. Zusammenfassung
Die Medizin wird sich durch künstliche Intelligenz grundlegend verändern.
Patienten und Patientinnen brauchen Grundkompetenzen um informierte Entscheidungen zu
treffen.
We shape the digital future
The UZH Digital Society Initiative