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Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
In the past decade, advances in precision oncology have resulted in an increased demand for predictive assays that enable the selection and stratification of patients for treatment.
The possibility of digitizing whole-slide images of tissue has led to the advent of artificial intelligence (AI) and machine learning tools in digital pathology, which enable mining of subvisual morphometric phenotypes and might, ultimately, improve patient management.
Artificial Intelligence and Computer Augmented Vision Laboratory
The Artificial Intelligence and Computer Augmented Vision Laboratory at Bascom Palmer Eye Institute is at the forefront of critical technological innovations at the intersection of ophthalmology research and the emerging fields of artificial intelligence and spatial computing, including the use of novel artificial intelligence algorithms to autonomously diagnose and correct for vision loss using Augmented Reality digital glasses as well as the autonomous diagnosis of eye diseases with very high prevalence using optical coherence tomography.
Based on each user's unique vision loss, customized corrective images of the scene being viewed by the user are displayed on the digital glasses in real time using autonomous algorithmic software, to augment and enhance vision for patients with prevalent eye diseases such as glaucoma, age-related macular degeneration, diabetic retinopathy, strokes and retinitis pigmentosa.
Artificial Intelligence Could Improve Health Care for All — Unless it Doesn’t
You could be forgiven for thinking that AI will soon replace human physicians based on headlines such as “The AI Doctor Will See You Now,” “Your Future Doctor May Not Be Human,” and “This AI Just Beat Human Doctors on a Clinical Exam.” But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of AI services working alongside human clinicians.
If AI works as promised, it could democratize health care by boosting access for underserved communities and lowering costs — a boon in the United States, which ranks poorly on many health measures despite an average annual health care cost of $10,739 per person.
And in many countries with national physician shortages, such as China where overcrowded urban hospitals’ outpatient departments may see up to 10,000 people per day, such technologies don’t need perfect accuracy to prove helpful.
But critics point out that all that promise could vanish if the rush to implement AI tramples patient privacy rights, overlooks biases and limitations, or fails to deploy services in a way that improves health outcomes for most people.
Unlike computer programs that rigidly follow rules written by humans, both machine learning and deep learning algorithms can look at a dataset, learn from it, and make new predictions.
But trouble arose when the machine learning model tried to predict the case for asthma sufferers, who are high risk because their preexisting breathing difficulties make them vulnerable to pneumonia.
Deep-learning predictions can also fail if they encounter unusual data points, such as unique medical cases, for the first time, or when they learn peculiar patterns in specific datasets that do not generalize well to new medical cases.
In February, the journal Nature Medicine published a study from researchers based in San Diego and Guangzhou, China that showed promise in diagnosing many common childhood diseases based on the electronic health records of more than 567,000 children.
“What we are continuously trying to do here is get back to our goal of giving people access to technologies, but we’re also realizing that our current methods don’t quite work well,” says Bakul Patel, director for digital health at the FDA.
“That’s why we need to look at a holistic approach of the whole product life cycle.” In addition to issues surrounding access, privacy, and regulations, it also isn’t clear just who stands to benefit the most from AI health care services.
There are already health care disparities: According to the World Bank and the World Health Organization, half of the globe’s population lacks access to essential health care services and nearly 100 million people are pushed into extreme poverty by health care expenses.
Still, so far, many proposed AI applications are focused on improving the current standard of care rather than spreading affordable health care around, Cohen says: “Democratizing what we already have would be a much bigger bang for your buck than improving what we have in many areas.”
But it’s unclear if patients and health care systems supplemented by taxpayer dollars would benefit, or if more money would simply flow to the tech companies, health care providers, and insurers.
That speaks to the bigger systemic issue of the U.S. health insurers using a fee-for-service model that often rewards physicians and hospitals for adding tests and medical procedures, even when they aren’t needed.
In his 2019 book “Deep Medicine,” Eric Topol, director and founder of the Scripps Research Translational Institute, talks about creating essentially a supercharged medical Siri — an AI assistant to take notes about the interactions between doctors and their patients, enter those notes in electronic health records, and remind physicians to ask about relevant parts of the patient’s history.
“My aspiration is that we decompress the work of doctors and get rid of their data clerk role, help patients take on more responsibility, and key up the data so it doesn’t take so long to review things,” Topol says.
Family physicians in particular often spend more than half of their working days entering data into electronic health records — a main factor behind physical and emotional burnout, which has dire consequences, including patient deaths.
The implementation of electronic health records has already created a patchwork system spread among hundreds of private vendors that mainly succeeds in isolating patient data and makes it inaccessible to both physicians and patients.
As doctors wait for their AI helpers, crowdsourcing projects like Human Dx “could definitely lead to improved diagnostics or even improved recommendations for therapy,” says Topol, who coauthored a 2018 study on a similar platform called Medscape Consult.
How hospitals are using AI to save their sickest patients and curb 'alarm fatigue'
From interpreting CT scans to diagnosing eye disease, artificial intelligence is taking on medical tasks once reserved for only highly trained medical specialists — and in many cases outperforming its human counterparts.
Doctors who have used the new systems say AI may be better at responding to the vast trove of medical data collected from ICU patients — and may help save patients who are teetering between life and death.
From 2012 to 2014, researchers tested a “smart” electronic medical record system — sort of a precursor to true AI — across 15 ICUs in the U.S. and found that it radically transformed them.
The death rate fell more than 12 percent, meaning patients whose treatment involved the system were 58 percent less likely to die in the ICU.
For the most part, medical AI systems are designed to sit quietly in the background of the hospitals' computer systems, diligently tracking vital sign monitors and then sending doctors a text message or other notification at the first sign of trouble.
'We're at a point of being able to predict the likelihood of a cardiac arrest in 70 percent of occasions, five minutes before the event occurs,' Laussen said, adding that Toronto's Hospital for Sick Children should have an AI system for heart attack prediction up and running within the next two years.
Christopher Barton, an emergency medicine doctor at the University of California-San Francisco Medical Center who has championed the hospital's AI initiative, said that in addition to improving medical care for patients, AI systems make life in the ICU less chaotic for doctors and nurses.
As a bonus, AI models can hunt for meaningful patterns among massive databases of electronic medical records — absorbing far more data than a human would ever be able to review.
- On 16. januar 2021
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