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Across all medical disciplines, artificial intelligence and machine learning will transform medicine beyond most people’s imagination. Algorithms that help evaluate radiological images are just the beginning. AI could become an indispensable tool in all branches of medicine.
Bloggen tar sig friheten att saxa valda delar av den rapporten.Fetningar är bloggens egna.
From virtual assistants in the living room to intelligent investment algorithms and from software-based traffic control to autonomous driving – there is no escaping artificial intelligence (AI) and machine learning.
The topic is also currently being addressed at the political level.
China presented its Next Generation Artificial Intelligence Development Plan as far back as July 2017.
In March 2018, French President Emmanuel Macron announced that his government would invest a total of €1.5 billion in AI by 2022.
In April 2018, the British government launched its £1 billion AI Sector Deal policy paper.
In May, it was Sweden’s turn. The German government unveiled its AI strategy at the end of 2018.
At the European level, the European Commission formulated a European AI Strategy in April 2018, setting out the draft European AI ethics guidelines a year later.
Practical uses for AI thanks to increased computing power
The advance of AI can be seen in medicine, as well. From prevention and screening to diagnosis, therapy, and disease management, innovative companies and technology-oriented medical institutions have been developing, testing, and – increasingly – implementing intelligent algorithms in virtually every branch of medical care. At the same time, regulators are turning their attention to the topic. In February 2019, the U.S. Food and Drug Administration (FDA) released a discussion paper regarding the licensing of AI applications for medicine. It is not concerned with ‘simple’ applications that are trained and re-trained during updates – such solutions already exist and have regulatory approval. Rather, the new FDA initiative concerns more sophisticated AI systems that learn in real time and are constantly changing their algorithms, requiring them to be regulated differently from traditional software solutions.AI to make inroads into labs
In other diagnostic disciplines, self-learning algorithms could lead to far-reaching change in the next few years. Right at the forefront is pathology, which produces enormous datasets that have not yet been fully evaluated. “The first step will definitely be an increase in efficiency thanks to AI,” underlines Professor Frederick Klauschen from the Institute of Pathology at Charité in Berlin, Germany, at an event of the Federal Association of German Pathologists. Pathologists think it likely that algorithms will quite soon, for instance, take over the task of counting cell nuclei.
This would free them and their colleagues up
for more complex tasks.
The first step will definitely be an increase in efficiency thanks to AI. Professor Frederick Klauschen, Institute of Pathology, Charité, Berlin, Germany
In
the medium term, Klauschen can also imagine algorithms being used for
quite different evaluations, including analyses that are difficult
without AI. For instance, there are indications that self-learning
algorithms are better than humans at recognizing complex biomarker
patterns in cancer patients. These patterns could then be used in
precision medicine instead of single parameters to predict which
patients will respond to immunotherapies.
With regard to biomarkers, AI algorithms are also likely to trigger some developments in laboratory medicine – the third major diagnostic field aside from pathology and radiology. In a Siemens Healthineers survey of 200 clinical laboratory executives, seven out of ten respondents said AI would move into in vitro diagnostics (IVD) over the next four years. As many as nine out of ten were convinced that AI would have a significant long-term impact on healthcare. Every second respondent had already been using AI applications in laboratory medicine.
With regard to biomarkers, AI algorithms are also likely to trigger some developments in laboratory medicine – the third major diagnostic field aside from pathology and radiology. In a Siemens Healthineers survey of 200 clinical laboratory executives, seven out of ten respondents said AI would move into in vitro diagnostics (IVD) over the next four years. As many as nine out of ten were convinced that AI would have a significant long-term impact on healthcare. Every second respondent had already been using AI applications in laboratory medicine.
Will diagnostic specialists become redundant?
In laboratory work, there
is an interest in algorithms that support operational processes.
For example, in cross-lab monitoring of diagnostic systems, AI can detect problems before failures occur, allowing for proactive maintenance schedules. On the clinical side, algorithms are suited to diagnostic decision-making in laboratory medicine and also, similar to pathology, to predictive analytics based on complex biomarker patterns.
One particularly promising use is the holistic analysis of diagnostic information, in which algorithms collate data from the laboratory, electronic patient record, imaging, and sometimes pathology. Bram Stieltjes of University Hospital Basel sees this AI-supported ‘interdisciplinarity’ as one of the ways in which algorithms directly impact clinical practice in the diagnostic fields: “It’s possible that the roles of radiologist, pathologist, and laboratory physician will cease to be separate in the future. Perhaps we will become total integrators of diagnostic information, working together more closely in integrated diagnostic departments to bring together all the pieces of the diagnostic puzzle as quickly as possible.”
Why is that? “Simply because most mistakes happen in non-technical areas, and AI can reduce errors,” says Forsting. As an example, the radiologist cites algorithm-based applications that use voice, facial expression, and posture to make a tentative diagnosis of depression. These algorithms are increasingly being tested in clinical trials. They could be very useful, for instance in situations where a doctor who is not a psychiatrist sees a patient who is supposedly suffering from a purely physical ailment.
For example, in cross-lab monitoring of diagnostic systems, AI can detect problems before failures occur, allowing for proactive maintenance schedules. On the clinical side, algorithms are suited to diagnostic decision-making in laboratory medicine and also, similar to pathology, to predictive analytics based on complex biomarker patterns.
One particularly promising use is the holistic analysis of diagnostic information, in which algorithms collate data from the laboratory, electronic patient record, imaging, and sometimes pathology. Bram Stieltjes of University Hospital Basel sees this AI-supported ‘interdisciplinarity’ as one of the ways in which algorithms directly impact clinical practice in the diagnostic fields: “It’s possible that the roles of radiologist, pathologist, and laboratory physician will cease to be separate in the future. Perhaps we will become total integrators of diagnostic information, working together more closely in integrated diagnostic departments to bring together all the pieces of the diagnostic puzzle as quickly as possible.”
AI and narrative-based medicine
Radiology, pathology, and laboratory medicine – these are all very technical fields that are already highly digitized in many areas. It comes as no surprise that this is where a lot of discussions about medical AI are taking place. Michael Forsting, who is responsible for the annual Emerging Technologies in Medicine (ETIM) congress – which addresses medical AI applications far beyond radiology – is convinced this is a passing phenomenon: “In the long run, the technical disciplines will change much less than narrative-based and clinical medicine.”Why is that? “Simply because most mistakes happen in non-technical areas, and AI can reduce errors,” says Forsting. As an example, the radiologist cites algorithm-based applications that use voice, facial expression, and posture to make a tentative diagnosis of depression. These algorithms are increasingly being tested in clinical trials. They could be very useful, for instance in situations where a doctor who is not a psychiatrist sees a patient who is supposedly suffering from a purely physical ailment.
“AI can bring about huge improvements in narrative-based and clinical medicine,” Forsting is convinced.
Min kommentar
Som de flesta av er säkerligen listat ut är bloggens tro att PHI`s digitala teknik nyckeln till dess framtida framgångar. Ja,inte enbart att tekniken är digital utan HoloMonitor´s alla möjligheter.Den digitala mikroskopin är på stark frammarsch är bloggens prediction och har stor chans att bli navet i framtidens labb. Naturligtvis gäller det även för andra konkurrerande digitala mikroskopitillverkare.Med fler aktörer desto mognare marknad och med det större acceptans och intresse att byta ut gammal teknik och investera i ny,framtidens.
Varför undertecknad är så säker på PHI´s teknik finns det många anledningar till. Jag tar de 3 främsta.
1. HoloMonitor har visat sig vara användbar för forskare inom alltfler områden. Vi har sett bevis på detta genom alla forskningsrapporter som publicerats. Cancer,immunologi,obesity,stamcellsforskning,nanoteknikforskning,reproducering (spermatoza),blodsjukdomar (malaria,zika mfl)...mfl som jag säkert missat.
Vi har dock glömt bort möjligheten för andra än forskarna.Nämligen läkarkåren.HoloMonitor går alldeles utmärkt att använda som diagnosinstrument.Med rätt ägare utrustad med de rätta försäljningskanalerna finns här en marknad som heter duga.Bloggen har tidigare skrivit om HoloMonitor som ett diagnosinstrument så in och leta bland inläggen.Annars kan man läsa denna forskningsrapport (Holography: The Usefulness of Digital Holographic Microscopy for Clinical Diagnostics) från kända HoloMonitoranvändare.
2. Framtidsprognoser som visar att världens labb går mot mer automatisering, att man kommer sköta labbandet digitalt då det ger tidsvinster (personal binder inte upp sig med manuellt arbete) som att resultaten man får snabbt levereras till nästa instans via den digitala vägen.
3. Slutligen den informationsbas HoloMonitor medger.Vi får alltfler tecken på att AI håller på att bli accepterat rent generellt (se Siemens artikel) och att medicinteknikbranschen specifikt är mycket intresserade av vad AI kan göra för dess slutkunder. När väl "någon" större aktör listar ut att HoloMonitor med hjälp av AI kan ge värdefull information utöver den man får idag och att det går att tjäna pengar på denna informationsbank börjar man se dess potential utöver praktiskt användande,labbande.
Som bloggen tidigare vibbat om utesluter undertecknad inte andra slags aktörer än ex ThermoFisher komma knackandes på VD´s dörr.
Mvh the99
Ps. Apropå Siemens så vet ni väl att PHI har en Siemenschef som en av de större aktieägarna? Ds
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