In the decade ahead, in the 2020s, AI will surpass people and doctors in diagnosis and predicting medical outcomes. AI is promising to see patterns in images that will correlate with conditions much faster and more accurately than doctors and medical technicians. AI is starting to expertly diagnose disease in medical images and scans at a startling pace.
The complexity and rise of data in healthcare means that artificial intelligence will increasingly be applied within the field. Several types of AI are already being employed by payers and providers of care and life sciences companies. AI is also starting to be used in pharma drug combinations and exploratory combinations and will drive a new era of biotechnology.
In the 2030s AI will be involved in most if not all your medical interventions. Think about all the various areas where artificial intelligence will transform healthcare in the years ahead.
The key categories of applications involve:
- Treatment recommendations
- Patient engagement and adherence
- Administrative activities
Although there are many instances in which AI can perform healthcare tasks as well or better than humans, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Ethical issues in the application of AI to healthcare are also going to be regulated eventually.
The patient centric and personalized care world will eventually be AI regulated. This will necessarily mean while there will still be a nursing shortage, part of healthcare will be effectively automated.
According to the latest paper published in the Future Healthcare Journal, AI will be increasingly applied within the healthcare field. Especially in the early 2020s health diagnosis will be transformed. Eventually a human-AI hybrid system will give way to more AI being involved.
Of course as with all things with AI, these incredible breakthroughs will continue to perpetuate inequality in access to care (Wired).
The American Association of Ophthalmologists has shown enthusiasm for AI tools, which it says promise to help improve standards of care. In 2015, misdiagnosing illness and medical error accounted for 10% of all US deaths. We know AI will transform patient centric care, early diagnosis, patient prediction and how medical intervention are processed.
Artificial intelligence (AI) and related technologies are increasingly prevalent in business and society and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organizations.
This will mean companies that get involved in the intersection of AI and medicine will have the potential to become very lucrative. This will be a significant cash cow for the likes of Google, Apple, Amazon and Microsoft, among others.
Google’s DeepMind here has the greatest path to profitability. Apple and Amazon will transform EMR and digital telemedicine in general. AI being more involved in our healthcare is the brave new world of data and algorithms involved in our bodies. In many instances, Artificial intelligence is on a par with human experts when it comes to making medical diagnoses based on images, a review in 2019 has found.
There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease. Today, algorithms are already outperforming radiologists at spotting malignant tumors, and guiding researchers in how to construct cohorts for costly clinical trials. In the near future, this will extend to many other healthcare diagnoses and the ability to find the best course of treatment.
The healthcare industry is not a leader in innovation but will be completely transformed by AI, IoT and the Cloud and new behaviors of consumers post pandemic. The potential for artificial intelligence in healthcare has caused excitement, with advocates saying it will ease the strain on resources, free up time for doctor-patient interactions and even aid the development of tailored treatment. AI will be used to make healthcare more affordable for all.
A study published this week by The Lancet Digital Health compared the performance of deep learning—a form of artificial intelligence—in detecting diseases from medical imaging versus that of healthcare professionals, using a sample of studies carried out between 2012 and 2019.
The study found that in the past few years, AI has become more accurate at identifying disease in these images and has become a more viable source of diagnostic information. BigTech companies won’t just come for banking, they will come for healthcare simultaneously. Eventually you’ll be choosing a corporation to store your data with, both financial and health wise.
One burgeoning application is the use of AI in interpreting medical images – a field that relies on deep learning, a sophisticated form of machine learning in which a series of labelled images are fed into algorithms that pick out features within them and learn how to classify similar images. AI here will continue to drive new discoveries in early diagnosis of a wide range of conditions.
While today the machine learning systems are not to replace doctors or make absolute decisions in a patient’s treatment, in how many years does this change? It won’t take a generation. Avoiding medical errors, lowering healthcare costs and improving early diagnosis is key to providing better care for all.
A Canadian biotech company, Deep Genomics, has been experimenting with machine learning and drug development for the past 5 years. They are not alone, just one among a new crowd racing for the right applications.
A recent study published in the Journal of the National Cancer Institute shows that the AI system has achieved a breast cancer detection accuracy comparable to an average breast radiologist. Eventually when you telemedicine a company, you won’t be dealing with a human but an AI avatar. They will already know why you are calling.
In 2020, it sounds like science fiction to some while questions remain about how such deep learning systems measure up to human skills. Now researchers say they have conducted the first comprehensive review of published studies on the issue, and found humans and machines are on a par. In a few years time, there won’t be a debate about who is ahead or what is more efficient.
While as of 2019, there is a massive hype over AI in medicine that obscures the lamentable quality of almost all evaluation studies, we have to assume that artificial intelligence as applied to healthcare will progress rapidly since it’s such big business. Deep learning might find its fruition in human medical data and interventions.
DeepMind in 2019 said its model is able to accurately predict that a patient will develop AKI “within a clinically actionable window” up to 48 hours in advance. Apple for easy storage of our medical data, Amazon for interventions and Google for predictions in healthcare seems to be the way it will pan out currently. Healthcare is typically ten to sixteen percent of GDP (for most western nations) and as such, AI will be a profit hound here.
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