6 recent studies exploring AI in healthcare

6 recent studies exploring AI in healthcare

Artificial intelligence holds great potential to transform healthcare for the better — including reducing workflow inefficiencies, predicting health outcomes and speeding up diagnoses — so researchers have been piloting more studies exploring the technology in the past decade.

Six key AI studies that have been published recently:

1. “Comparison of chest radiograph interpretations by artificial intelligence algorithm vs. radiology residents“: The research team found no statistical significance in sensitivity between the way the algorithm performed and the way the radiology residents did, but specificity and positive predictive value were statistically higher for the algorithm.
2. “Development of a dynamic diagnosis grading system for infertility using machine learning“: This study established an infertility scoring system based on the health records of 60, 648 couples going through the in vitro fertilization process, finding its overall stability test result to be 96 percent.
3. “Effect of integrating machine learning mortality estimates with behavioral nudges to clinicians on serious illness conversations among patients with cancer“: Researchers discovered the AI intervention led to a statistically significant increase in serious illness conversations from approximately 1 to 5 percent of all patient encounters and from approximately 4 to 15 percent of encounters involving patients with a high predicted mortality risk.
4. “Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction“: This study established a method to predict the transportability of AI models to expedite such technology’s adoption at hospital sites.
5. “Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values“: Researchers developed an inexpensive model for the prediction of sepsis class, finding it to have statistically high prognostic and predictive capabilities.
6. “Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet“: The research team found the AI system they designed to detect rib fractures and segmentation in CT scans performed more efficiently than radiologists.

More articles on artificial intelligence:
4 studies on AI’s potential to identify early-stage dementia
Houston Methodist develops AI-powered breast cancer risk calculator
CMS to pay physicians to use stroke, diabetes complication detection algorithms

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Published at Tue, 10 Nov 2020 23:26:15 +0000

The Breadth Of Healthcare Applications Of Artificial Intelligence Even Includes Physical Therapy

This column keeps returning to the healthcare industry because it is so much more complex and varied than so many others. Artificial intelligence (AI) coverage has focused on radiology, has moved to the operating theater, and has been discussed in the back office. Insurance and pharma fraud are arenas where AI risk analysis is useful. Now, along comes another area that is amenable to AI solutions. It’s something many people think of as secondary, but is really a critical part of healthcare: physical therapy.

As someone who, many years ago, had an intriguing car crash, and who, not as many years ago, also proved he wasn’t as young as he thought he was, by blowing out a knee, I’m someone who is very aware of the need for physical therapy (PT). The basics of PT seem very simple: design therapies that cause repeated motions of damaged body parts, analyze that motion, then provide feedback to the patient and the medical community in order to help both improve. It’s the capture and analysis of impact (yes, pun intended) of that motion which can prove complex.

Human physical therapists can see a lot of movement, but it’s impossible for them to capture all the necessary information. SWORD Health is a company focused on this unique healthcare segment. As they are a young company, they are focusing on a few key therapy areas. “The hip, knee, lower back, shoulder, wrist and neck comprise more than 90 percent of all musculoskeletal issues in the U.S.,” said Virgilio Bento, CEO, SWORD Health. “Rehabilitating them remotely requires a technology that can learn and expand.”

One Way Around Gender Bias In Testing

One intriguing area that supports a separate call out section is the oft problematic issue of bias in testing. We know that visual neural networks have had problems identifying women of color. We know that, outside of AI, many drug trials don’t include children, pregnant women, and other demographics who will need those drugs. Physical therapy is a healthcare sector that can avoid those problems.

There is already a body of PT information on the wide variety of demographics who receive PT. The ability to track far more information and to analyze it with demographic information (even anonymized for privacy), means that treatments can start with far more segmentation based on available information and then been quickly tuned on an individual basis based on direct, specific results. Starting with patterns based on more detailed segmentation and then transforming treatment on a case-by-case basis removes the bias issues that may be inherent in other areas of medicine or even in the minds of some medical personnel.

AI In The Real World Means Integration With Other Technologies

As has been regularly mentioned, AI is a tool, not a solution. The company isn’t only working with machine learning. They make sensors to capture the information, with the kinematics being sent to the system via wireless communications. Then multiple techniques can be used to address the data. A mixture of deep learning and statistical linear regression is used to understand the progress of the therapies. Changing the therapy can then also be semi-automated, with the system suggesting changes. That doesn’t need deep learning, as choosing the therapies is a rules based process.

As with all areas of healthcare dealing with patients, in the United State the FDA requires clearance of both new and updated appliances. The difference between hardware and AI is readily apparent with how each part is handled on change. When a hardware component is changed, detailed specifications can be sent to the FDA for fairly quick analysis and approval. The regulatory agency is still early in its analysis on how to manage AI, especially neural networks, so the process can be slower than with hardware.

AI is still a grey area, primarily through the fault of AI companies. While they like to talk about the black box that is a neural network, for instance, they know their layers, they know the nodes, the code and the weightings. While some of the inference is still not easily explicable, there is far more companies could provide to regulatory agencies if it were mandated.

In the lack of such transparency, expect for at least near-term job security for humans. They must remain in the loop, both as oversight for the AI and as a legal cover to say the AI is not making a prognosis but is providing the humans with options.

Deep learning and other machine learning techniques have an important place in healthcare, but it must be incorporated into the full patient treatment process, along with other technology. Unlike a deep learning system cranking along on its own in a research facility, investigating potential new drugs, AI must play well with other technology and processes the closer to patients it resides. Physical therapy is an excellent aspect of the needed growth, as it is a regular and visible part of patient treatment that includes humans, hardware and software interacting within a regulatory framework to improve patient outcomes.

Published at Tue, 10 Nov 2020 20:37:30 +0000