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Robo-Docs: How Artificial Intelligence is Revolutionizing Healthcare

Robo-Docs: How Artificial Intelligence is Revolutionizing Healthcare - Electronic Health Records Get Smarter

Electronic health records (EHRs) are transforming into intelligent systems that leverage artificial intelligence to deliver better care. EHRs were initially just digital versions of paper charts, but AI is enabling them to become true partners for clinicians.

One way EHRs are getting smarter is through natural language processing. This allows the system to analyze unstructured physician notes and extract key data points. For example, natural language processing can identify when a patient was diagnosed with diabetes or prescribed a certain medication. This structured data makes it easier for doctors to review a patient's history.

EHRs can also leverage machine learning to detect patterns in data. This allows health systems to identify high-risk patients and intervene early before their condition worsens. Researchers at Stanford developed an algorithm that analyzes EHR data to predict patient mortality more accurately than traditional models. The algorithm is able to incorporate over 170,000 different variables compared to less than 10 used by conventional methods.

In addition, EHRs are starting to incorporate virtual assistants to make clinical documentation easier. For instance, Nuance's virtual assistant understands conversations between doctors and patients during exams and automatically populates the health record. This saves physicians time and ensures documentation accuracy.

EHRs are also using AI to aid clinical decision making. UK startup Babylon Health built an AI system called Babylon Assistant. It allows doctors to describe a patient's symptoms and get recommendations for possible diagnoses, tests and treatments. This augments a physician's own knowledge and results in faster, more accurate care.

John Halamka, CIO of Beth Israel Deaconess Medical Center in Boston remarked, "We're really redesigning the EHR from the ground up...to take advantage of all these new technologies by embedding artificial intelligence, embedding natural language processing."

Robo-Docs: How Artificial Intelligence is Revolutionizing Healthcare - AI Helps Detect Cancer Earlier

Artificial intelligence is proving adept at detecting cancer earlier and more accurately than human doctors. By analyzing medical images and data, AI systems can identify warning signs that radiologists sometimes miss. This allows cancer to be caught at earlier stages when treatment is more likely to be successful.

Researchers at Google trained an AI system to analyze breast mammograms. It reduced false positives by 5.7% and false negatives by 9.4% compared to human readers. This means fewer healthy women will undergo unnecessary biopsies while more women with cancer will get a timely diagnosis.

Enlitic, a San Francisco startup, developed an algorithm to detect lung nodules in CT scans. It achieved 50% higher accuracy than radiologists in a clinical study. In addition, Enlitic's AI was able to accurately classify benign and malignant tumors 98% of the time compared to only 65% for human doctors.

AI is also proving useful at reading pathology slides to determine if tissue is cancerous. CAMELYON16 was a competition to develop an algorithm that could evaluate slides for breast cancer metastasis. The winning AI system from Google Health achieved 92% accuracy whereas human pathologists scored an average of 73%.

AI's accuracy comes from its ability to analyze vast numbers of medical images and find patterns that humans cannot perceive. An AI system developed by Memorial Sloan Kettering was trained on over 30,000 slides from more than 10,000 patients. It learned to identify visual features associated with cancer at a granular level.

Heather Curry, an oncology manager at PAIGE, an AI healthcare startup comments: "One of the beauties of AI is that we can teach it to look for those patterns, even if we as humans can't articulate or don't understand what those features are."

Another advantage of AI systems is that they are consistent. Studies show radiologists' diagnostic accuracy will vary significantly depending on workload, fatigue and other factors. AI has no such inconsistencies.

Finally, AI excels at integrating data from diverse sources such as lab tests, medical history and multi-modal images. This allows more informed diagnoses than consulting images alone. For example, Enlitic combines its nodule detection algorithm with clinical data to make inferences about whether tumors are benign or malignant.

Robo-Docs: How Artificial Intelligence is Revolutionizing Healthcare - Algorithms Predict Epidemics and Outbreaks

Algorithms that can rapidly analyze disease data from around the world are proving adept at predicting outbreaks and epidemics earlier and more accurately than traditional public health methods. These predictive analytics tools allow quicker response times, potentially saving thousands of lives.

One example is BlueDot, a Canadian health monitoring startup that uses natural language processing and machine learning to comb through 100,000 online sources daily in over 50 languages. On December 31, 2019, the algorithm flagged a cluster of unusual pneumonia cases in Wuhan, China. This was 9 days before the World Health Organization released its statement about the novel coronavirus. Early warning allows quarantines to be implemented sooner and medical supplies to be stockpiled in preparation.

During the Zika virus crisis, researchers at Boston Children's Hospital developed an AI algorithm to predict the spread of the disease. It mined data on population densities, travel patterns, climate, socioeconomics and more. The algorithm accurately forecasted the number and location of Zika cases as the epidemic progressed. This enabled public health departments to optimize the deployment of resources and containment strategies.

The U.S. Centers for Disease Control and Prevention uses a flu prediction system called FluSight. It aggregates data from Google, lab tests, electronic health records and weather forecasts. During the 2018-2019 flu season, FluSight beat traditional statistical models and was accurate about 9 weeks ahead of time. It predicted when and where the disease would peak, allowing hospitals and clinics to adequately prepare.

Algorithms are superior to older statistical techniques because they can ingest vastly more data sources and find associations that humans would never perceive. They are not limited to structured health data but can incorporate news reports, social media posts, airline ticket sales and more. Researchers at Johns Hopkins University developed an algorithm that analyzes Twitter posts to accurately estimate influenza rates 1-2 weeks ahead of the CDC.

Artificial intelligence is even helping predict animal-to-human virus transmission. Columbia University researchers built a model using AI and geospatial data to identify bats species most likely to pass coronaviruses to humans in Southeast Asia. This points to the highest-risk areas for conducting wildlife virus surveillance.

Of course, predictive algorithms are not foolproof. Garbage in, garbage out still applies. The quality of their forecasts depends on the quantity, diversity and veracity of the input data. Algorithms may also miss early warning signs if the data is biased or incomplete. Public health experts caution against over-reliance on AI predictions and stress that traditional epidemiology still plays a vital role.

Robo-Docs: How Artificial Intelligence is Revolutionizing Healthcare - Robots Assist in Surgeries

Surgical robots are becoming commonplace in operating rooms around the world. These high-tech systems have multiple arms that can maneuver with precision beyond human capability. Robots allow surgeons to operate in hard-to-reach areas through tiny incisions. This results in less pain, smaller scars, faster recovery times and lower risk of infection for patients compared to open surgery.

Intuitive Surgical's da Vinci system is the most widely used surgical robot. It has brought minimally invasive techniques to complex procedures like cardiac surgery that previously required open-chest operations. Dr. Douglas Boyd, a cardiothoracic surgeon at Presbyterian Hospital in Albuquerque, NM routinely operates on blocked coronary arteries using the da Vinci robot. He remarks that the system provides “a high-definition, magnified 3-D view” of the surgical site and instruments that can “move like a human hand but with a lot more precision.”

Another advantage of robotic surgery is that the controls filter out hand tremors. This allows finer manipulation of delicate tissues. Dr. Vipul Patel, a urologic robotic surgeon at the Florida Hospital Global Robotics Institute observes: "Instead of operating with ten-millimeter movements, I'm operating with movements of 100 microns." This results in greater accuracy and reduces accidental damage to surrounding nerves and blood vessels.

The da Vinci's fourth arm is equipped with a special endoscopic camera that provides vivid imagery from multiple angles inside the patient's body. Dr. Boyd notes this gives him “much better visualization of the anatomy” than conventional endoscopic cameras used in non-robotic laparoscopic surgery. The robot's enhanced optics and flexible instruments permit more complex operations through tiny incisions.

While surgical robots offer many benefits, they also have limitations. The systems are expensive, costing over $1 million plus several hundred thousand in annual maintenance fees. Extensive training is required to operate them effectively, with surgeons typically needing 100-200 cases to become adept. There is also a learning curve when transitioning from open to robotic techniques.

Robo-Docs: How Artificial Intelligence is Revolutionizing Healthcare - Wearables Monitor Patients Remotely

Wearable devices equipped with biometric sensors and wireless connectivity are allowing doctors to monitor patients outside of hospitals and clinics. These smart wearables track vital signs, activity levels, sleep patterns and more, generating data that provides insights about a person’s health and behavior. This remote patient monitoring allows issues to be detected early before they become acute. It also enables care to be tailored to each individual more effectively.

For patients with chronic conditions like diabetes, wearables are proving especially useful. Devices like the Freestyle Libre continuously measure blood glucose levels without the need for constant finger pricks. This data helps doctors better understand how food, exercise and medication impacts each patient’s glucose fluctuations. Medtronic’s Guardian Connect system goes a step further by combining a glucose sensor with an app that alerts patients and doctors if levels go too high or low. This allows prompt adjustments in diet, activity or insulin dosing.

Wearables that monitor heart rhythm such as the Apple Watch are able to detect atrial fibrillation, a common type of irregular heartbeat. This condition often has no obvious symptoms but can lead to blood clots, strokes and heart failure if left untreated. Remote ECG monitoring via wearables allows earlier diagnosis and treatment. The Apple Heart Study provided the Watch to over 400,000 participants and detected atrial fibrillation in 0.5% who did not know they had it.

For patients with chronic obstructive pulmonary disease (COPD), activity trackers can provide insights about changes in functional status. Steps per day, walking pace and heart rate variability reflect disease progression and effectiveness of therapy. Doctors review this data during telehealth visits to optimize care plans without an office visit.

Wearables are also being used for remote patient monitoring after hospital discharge. Sensors track vitals, movement, sleep and more as patients recover at home. If the data indicates potential issues, the care team is automatically notified to intervene early before the patient’s condition declines. Researchers found remote monitoring cut hospital readmissions by 51% within 30 days after discharge.

However, there are challenges to widespread adoption of wearables for patient monitoring. Currently there is poor integration of data from consumer devices into electronic health records. Data accuracy and privacy issues also need to be addressed. And not all patients are tech savvy enough to use wearable effectively.

Robo-Docs: How Artificial Intelligence is Revolutionizing Healthcare - AI Improves Drug Development

Artificial intelligence is accelerating and improving the process of discovering and testing new medications. Drug development traditionally takes 10-15 years from initial research to market approval, at a cost of over $2 billion per medication. AI techniques are helping slash the timeline and cost of bringing new pharmaceuticals to patients.

One way AI assists is by analyzing vast amounts of research data to identify promising drug candidates faster. There are estimated to be 1060 potential drug compounds. Testing them all through brute force is impossible. BenevolentAI developed an AI system that reviews scientific papers, clinical trial data, regulatory filings and more to pinpoint substances likely to be safe and effective. This allows researchers to focus on molecules with the highest chance of becoming viable drugs.

AI also analyzes chemical structures to predict how potential medications might interact with biomolecular targets. Companies like Exscientia and Insilico Medicine build 3D molecular models and simulate docking with proteins. This predicts binding affinity and activity without having to synthesize the agent and test it in a lab. AI models narrow down selections to the most potent compounds.

Robots equipped with AI are automating early research experiments to rapidly screen prospective drugs. Labs have traditionally relied on humans to manually conduct tests in petri dishes and well plates to assess compounds. AI robotic systems like those made by Transcriptic and Emerald Cloud Lab perform experiments round the clock, allowing thousands more tests than human researchers could handle. This results in faster lead optimization.

Once a drug candidate is identified, AI improves clinical trials through recruitment automation, quantitative analysis of biomarkers, and detecting adverse effects early. AI also helps design trials that are safer, shorter and have higher probability of success. This reduces costs.

Finally, AI analyzes data from sources like electronic health records, insurance claims and genomics to determine which patients are most likely to benefit from a medication. This allows drugs to be targeted to the appropriate populations and avoids prescribing ineffective treatments.