New AI tool doubled efficiency in busy university radiology department
Creative artificial intelligence (AI) solutions are being developed to address critical staffing shortages in radiology that could help with similar shortages in overworked pathology and clinical laboratories as well.
In a recent clinical study at 11-hospital Northwestern Medicine, researchers developed a new generative AI radiology tool to assist radiologists that demonstrates high accuracy and efficiency rates when working with multiple types of imaging scans.
For the study, approximately 24,000 radiology reports were analyzed and then compared for clinical accuracy with and without the AI tool. The tool evaluates an entire scan and generates a report that is 95% complete and personalized to each patient. A template based on that report is then provided to radiologists for review, according to a Northwestern Medicine Feinberg School of Medicine news release.
The study reported an average 15.5% increase in radiograph efficiency without compromising accuracy. Some radiologists even produced gains as high as 40%. The radiology reports were scrutinized during a five-month period last year and enabled radiologists to improve the time it took to return a diagnosis.
“This is, to my knowledge, the first use of AI that demonstrably improves productivity, especially in healthcare. Even in other fields, I haven’t seen anything close to a 40% boost,” said the study’s senior author Mozziyar Etemadi, MD, PhD, assistant professor of anesthesiology and biomedical engineering at Northwestern University McCormick School of Engineering, in the news release. (Photo copyright: Northwestern University.)
Doubled Efficiency for One Radiology Team
“For me and my colleagues, it’s not an exaggeration to say that it doubled our efficiency. It’s such a tremendous advantage and force multiplier,” said study co-author Samir Abboud, MD, emergency radiology in the department of radiology at Northwestern Medicine, in the news release.
“Having a draft report available, even before it is viewed by the radiologist, offers a simple, actionable datapoint that can be quickly and efficiently acted upon” added study senior author Mozziyar Etemadi, MD, PhD, assistant professor of anesthesiology and biomedical engineering at Northwestern University McCormick School of Engineering, in the news release. “This is completely different than traditional triage systems, which need to meticulously be trained one by one on each and every diagnosis.”
The AI tool can also alert radiologists to life-threatening conditions.
“On any given day in the ER, we might have 100 images to review, and we don’t know which one holds a diagnosis that could save a life,” Abboud said. “This technology helps us triage faster—so we catch the most urgent cases sooner and get patients to treatment quicker.”
Relying on In-house Data
Engineers at Northwestern developed the AI model using clinical data within the university’s own network, emphasizing that such tools can be created without assistance from other organizations.
“Our study shows that building custom AI models is well within reach of a typical health system, without reliance on expensive and opaque third-party tools like ChatGPT,” Etemadi noted.
The Journal of the American College of Radiology states the supply of radiologists is expected to increase by approximately 26% over the next 30 years. However, the need for radiologists is expected to grow between 17% and 27% over the same period. Becker’s Hospital Review reports there will be a shortage of up to 42,000 radiologists in the US by 2033.
Some health organizations are using a mixed model of permanent employees and contracted radiologists to meet the increasing demand for services. Others are also looking at options such as internal training programs, better benefits for workers, teleradiology, and remote radiologists to fulfill radiology needs.
“You still need a radiologist as the gold standard,” Abboud said. “Medicine changes constantly—new drugs, new devices, new diagnoses—and we have to make sure the AI keeps up. Our role becomes ensuring every interpretation is right for the patient.”
Can pathology practices and clinical laboratories learn from radiology’s situation? Development of AI solutions for those fields would likely have similar effects on workloads and overworked personnel.
Exploring the benefits of AI may be one way of helping meet clinical laboratory and pathology practice staff shortages.
Dettwyler is set to retire at age 92 after a long career helping clinical laboratories with their coding and billing systems
When William Dettwyler, MT, began working in a clinical laboratory, Harry Truman was president of the United States and scientists had not yet discovered the structure of DNA. Now, as he approaches his 92nd birthday in March, he is finally ready to retire from a career that has spanned more than seven decades, from bench work as a medical laboratory technician (MLT) to assisting labs with their medical coding and medical billing challenges.
Along the way, one of his coding innovations helped the State of Oregon save substantial sums in its Medicaid program. He also helped many medical laboratories increase reimbursement by correcting their coding mistakes. This from someone who left school after eighth grade to help on his family’s farm in rural Oregon.
In an exclusive interview with Dark Daily, Dettwyler discusses his long career and offered pointers for labs on improving their coding and reimbursement procedures.
Back in the 1980s, when he began his consulting work for labs, “they were very poor at billing,” he recalled. “Hospital billing staff didn’t understand lab coding. Reference laboratories didn’t do a good job of picking the right codes or even billing all the codes. Up until around the 1970s, hospitals didn’t even have to bill individual lab procedures with CPT codes. They billed with a revenue center code for all their lab services.”
These days “people are much more sophisticated,” he notes. “There are fewer coding problems compared to what it was in the 1980s and 1990s up to the 2010s.” However, he says he still has a handful of clients who call on his expertise.
“It was not unusual to go to a large university medical center and in three days tell the CFO on my exit review that the following year their lab would bring in about a half million more in revenue, just from my coding review. But I did not reveal to them that I had only gone to the eighth grade in a little one room school and was the lone graduate in my eighth-grade class,” wrote William Dettwyler, MT (above), owner of Codus Medicus in Salem, Ore., in an article he penned for Medical Laboratory Observer. For 75 years Dettwyler worked in the clinical laboratory industry. For much of that time he helped labs all over America improve their coding and reimbursement systems. (Photo copyright: LinkedIn.)
How It All Began
Dettwyler got his first taste of lab work in the early 1950s as a teenager washing glassware for a medical laboratory technician at a local medical practice. A few years later he completed an MLT program at Oregon Institute of Technology in Klamath Falls and landed his first lab tech job at a clinic in Portland.
His entry to consulting came in the early 1970s while he was working for a medical group in Salem. “I was helping the accounting personnel with their billing and noticed that Medicaid was not paying for a common test for syphilis that I was performing,” he recalled. “I contacted Medicaid, and they told me they didn’t understand laboratory procedures.”
After that, “they started to call me frequently with laboratory questions,” he said. “It wasn’t long before they asked me to help them on a part-time basis.” He also assisted with questions related to radiology.
By 1976, Dettwyler was devoting 35 hours a week to assisting the state Medicaid agency while still working as a lab tech.
Simple Hack Ends Overpayments
One of his career highlights came around 1981, when he discovered that the agency was overpaying for some pathology and radiology procedures by as much as 200%.
“Pathologists and radiologists are paid based on whether they are performing the complete procedure—the technical component and the professional component—or just the professional component, where they interpret the results,” he explained.
When billing for just the professional component, the physicians would add two digits to the standard code, so it might come in as 88305-26. However, the state’s computer system could only accommodate a five-digit code, so the state was paying as if the providers had done everything.
“The computer techs said the software couldn’t handle a seven-digit number in a five-digit box, so I devised a way for the computer to read the equivalent of seven digits,” he recalled.
His solution was to modify the codes so that the last digit was an alphabetic character. Instead of billing for code 88305-26, the physicians would bill for 8830F, and the state would pay them correctly.
Around that time, Dettwyler also began assisting a Medicare office in Portland. This forced him to cut back on his work as a lab tech. But he still worked around 60 hours a week.
“For most of my life, I’ve worked three jobs,” he said. “Work is my hobby.” He also had a large family to support—by 1976, he and his wife had 10 kids.
Transition to Lab Consulting
In 1986, the state was facing a budget shortfall and cut its Medicaid consultants, so Dettwyler decided to seek consulting work with labs while continuing to work at the bench.
“I really liked the coding because I had very little competition,” he said. “But I wanted to keep working in the laboratory mainly to understand the problems.”
While working for the state, Dettwyler attended coding seminars and workshops. He noticed that labs were losing revenue due to poor billing practices. “They didn’t understand all the coding complexities, so they really hungered for this kind of assistance.”
But first, he had to find clients. So he partnered with another lab tech who was offering similar consulting services.
Business picked up after Dettwyler contributed an article to the trade publication Medical Laboratory Observer about his process, which he calls “procedure code verification and post payment analysis.”
“That went like gangbusters,” he said. “We started getting calls from all over the country.”
Dettwyler later split from his partner and went to work on his own.
“I would sit down with the person who was responsible for coding, usually the lab or radiology manager,” he explained. “We would go over the chargemaster and cover every procedure to make sure the code and units were correct. When I was done, I would give them a report of what codes we changed and why we changed them.”
Beginning in 1989, he signed on as a contractor for another consultancy, Health Systems Concepts on the East Coast, where he remained until 2019.
Advice to the Current Generation
What is Dettwyler’s advice for someone who wants to follow in his footsteps and assist labs with their coding? “I wouldn’t recommend it now,” he said. “There’s less need for that kind of assistance than in the past.”
However, he does find that labs still run into problems. The greatest need, he says, is in molecular diagnostics, due to the complexity of the procedures.
In addition, labs are sometimes confused by coding for therapeutic drug monitoring, in which a doctor is gauging a patient’s reaction to a therapy versus screening for substance abuse. “Those issues are often misunderstood,” he said.
Microbiology also poses coding challenges, he noted, because of the steps required to identify the pathogen and determine antibiotic susceptibility. “It requires quite a bit of additional coding,” he said. “Some labs don’t understand that they can’t just bill a code for culture and sensitivity. They have to bill for the individual portions.”
Labs that work with reference labs also have to be careful to verify codes for specific procedures. “I’ll review the codes used by reference labs and, surprisingly, they’re not always correct. Reference labs sometimes get it wrong.”
If someone does want to become a coding expert, Dettwyler suggests that “they should first have experience as a lab tech, especially in microbiology, because of the additional coding. And they should try to work with somebody who is already doing it. Then, they should work with the billing department to learn how it operates.”
He also advises clinical laboratory managers to follow the latest developments in the field by reading lab publications such as The Dark Report. “You have to do that to keep current,” he said.
Despite never completing high school, Dettwyler eventually received his GED and an associate degree. “But the degrees didn’t really help me,” he said. “Much of it was on-the-job training and keeping my eyes open and listening.”
Research results call into question the safety and dependability of using artificial intelligence in medical diagnosis, a development that should be watched by clinical laboratory scientists
ChatGPT, an artificial intelligence (AI) chatbot that returns answers to written prompts, has been tested and found wanting by researchers at the University of Florida College of Medicine (UF Health) who looked into how well it could answer typical patient questions on urology. Not good enough according to the researchers who conducted the study.
AI is quickly becoming a powerful new tool in diagnosis and medical research. Some digital pathologists and radiologists use it for data analysis and to speed up diagnostic modality readings. It’s even been said that AI will improve how physicians treat disease. But with all new discoveries there comes controversy, and that’s certainly the case with AI in healthcare.
Many voices in opposition to AI’s use in clinical medicine claim the technology is too new and cannot be trusted with patients’ health. Now, UF Health’s study seems to have confirmed that belief—at least with ChatGPT.
The study revealed that answers ChatGPT provided “fell short of the standard expected of physicians,” according to a UF Health new release, which called ChatGPT’s answers “flawed.”
The questions posed were considered to be common medical questions that patients would ask during a visit to a urologist.
The researchers believes their study is the first of its kind to focus on AI and the urology specialty and which “highlights the risk of asking AI engines for medical information even as they grow in accuracy and conversational ability,” UF Health noted in the news release.
“I am not discouraging people from using chatbots,” said Russell S. Terry, MD (above), an assistant professor in the UF College of Medicine’s department of urology and the study’s senior author, in a UF Health news release. “But don’t treat what you see as the final answer. Chatbots are not a substitute for a doctor.” Pathologists and clinical laboratory managers will want to monitor how developers improve the performance of chatbots and other applications using artificial intelligence. (Photo copyright: University of Florida.)
UF Health ChatGPT Study Details
UF Health’s study featured 13 of the most queried topics from patients to their urologists during office visits. The researchers asked ChatGPT each question three times “since ChatGPT can formulate different answers to identical queries,” they noted in the news release.
The urological conditions the questions covered included:
The researchers then “evaluated the answers based on guidelines produced by the three leading professional groups for urologists in the United States, Canada, and Europe, including the American Urological Association (URA). Five UF Health urologists independently assessed the appropriateness of the chatbot’s answers using standardized methods,” UF Health noted.
Notable was that many of the results were inaccurate. According to UF Health, only 60% of responses were deemed appropriate from the 39 evaluated responses. Outside of those results, the researchers noted in their Urology paper, “[ChatGPT] misinterprets clinical care guidelines, dismisses important contextual information, conceals its sources, and provides inappropriate references.”
When asked, for the most part ChatGPT was not able to accurately provide the sources it referenced for its answers. Apparently, the chatbot was not programmed to provide such sources, the UF Health news release stated.
“It provided sources that were either completely made up or completely irrelevant,” Terry noted in the new release. “Transparency is important so patients can assess what they’re being told.”
Further, “Only 7 (54%) of 13 topics and 21 (54%) of 39 responses met the BD [Brief DISCERN] cut-off score of ≥16 to denote good-quality content,” the researchers wrote in their paper. BD is a validated healthcare information assessment questionnaire that “provides users with a valid and reliable way of assessing the quality of written information on treatment choices for a health problem,” according to the DISCERN website.
ChatGPT often “omitted key details or incorrectly processed their meaning, as it did by not recognizing the importance of pain from scar tissue in Peyronie’s disease. As a result … the AI provided an improper treatment recommendation,” the UF Health study paper noted.
Is Using ChatGPT for Medical Advice Dangerous to Patients?
Terry noted that the chatbot performed better in some areas over others, such as infertility, overactive bladder, and hypogonadism. However, frequently recurring UTIs in women was one topic of questions for which ChatGPT consistently gave incorrect results.
“One of the more dangerous characteristics of chatbots is that they can answer a patient’s inquiry with all the confidence of a veteran physician, even when completely wrong,” UF Health reported.
“In only one of the evaluated responses did the AI note it ‘cannot give medical advice’ … The chatbot recommended consulting with a doctor or medical adviser in only 62% of its responses,” UF Health noted.
For their part, ChatGPT’s developers “tell users the chatbot can provide bad information and warn users after logging in that ChatGPT ‘is not intended to give advice,’” UF Health added.
Future of Chatbots in Healthcare
In UF Health’s Urology paper, the researchers state, “Chatbot models hold great promise, but users should be cautious when interpreting healthcare-related advice from existing AI models. Additional training and modifications are needed before these AI models will be ready for reliable use by patients and providers.”
UF Health conducted its study in February 2023. Thus, the news release points out, results could be different now due to ChatGPT updates. Nevertheless, Terry urges users to get second opinions from their doctors.
“It’s always a good thing when patients take ownership of their healthcare and do research to get information on their own,” he said in the news release. “But just as when you use Google, don’t accept anything at face value without checking with your healthcare provider.”
That’s always good advice. Still, UF Health notes that “While this and other chatbots warn users that the programs are a work in progress, physicians believe some people will undoubtedly still rely on them.” Time will tell whether trusting AI for medical advice turns out well for those patients.
The study reported above is a useful warning to clinical laboratory managers and pathologists that current technologies used in ChatGPT, and similar AI-powered solutions, have not yet achieved the accuracy and reliability of trained medical diagnosticians when answering common questions about different health conditions asked by patients.
It’s not just radiology. Gen Z residents will be matching in pathology and other specialties, and that means clinical laboratories should be ready to adapt their recruiting and training to Gen Z’s unique characteristics
It’s a big event in medical schools across the nation when it is time for residency programs to match residency candidates with first-year and second-year post-graduate training positions. But this year has a special twist because—for example in radiology—this is the first class of Generation Z (Gen Z) residency candidates to be matched with radiology residency programs.
In their abstract, the authors wrote, “This year, the radiology community will experience the beginning of a generational change by matching its first class of Generation Z residents. To best welcome and embrace the changing face of the radiology workforce, this Viewpoint highlights the values that this next generation will bring, how radiologists can improve the way they teach the next generation, and the positive impact that Generation Z will have on the specialty and the way radiologists care for patients.”
Members of Gen Z are now entering the workforce in large numbers. To recruit high-quality candidates from this generation, healthcare employers—including clinical laboratories and pathology practices—may have to adapt the way they interact with and train these individuals.
Gen Z is generally described as individuals who were born between 1995 and 2012. Also known as “Zoomers,” the demographic comprises approximately 25% of the current population of the United States. They are extremely diverse, tend to be very socially conscious, and can easily adapt to rapid changes in communications and education, according to the AJR paper.
Although the paper deals with radiology, this type of information can also be valuable to clinical laboratories as Gen Z pathologists are poised to enter clinical practice in growing numbers. This marks the beginning of the professional laboratory careers of Zoomers, while Millennials move up into higher levels of lab management, the oldest Gen Xers near retirement age, and Baby Boomers retire out of the profession.
“Gen Z employees bring unique values, expectations, and perspectives to their jobs,” said Paul McDonald (above), Senior Executive Director at staffing firm Robert Half in a news release. “They’ve grown up in economically turbulent times, and many of their characteristics and motivations reflect that.” Thus, clinical laboratories may have to develop methods for recruiting and training Gen Z staff that match the unique characteristics of Gen Z candidates. (Photo copyright: LinkedIn.)
Zoomers Like Digital and Artificial Intelligence Technology
One of the most unique aspects of Gen Z is that they have never lived in a world without the Internet and have little memory of life without smartphones. Zoomers grew up totally immersed in digital technology and tend to be comfortable using digital tools in their everyday life and in the workplace. They lean towards being very open to artificial intelligence (AI) and how it can assist humans in analysis and diagnostic methods.
“This group of professionals has grown up with technology available to them around the clock and is accustomed to constant learning,” said Paul McDonald, Senior Executive Director at staffing firm Robert Half in a news release. “Companies with a solid understanding of this generation’s values and preferences will be well prepared to create work environments that attract a new generation of employees and maximize their potential.”
According to the AJR paper, Zoomers learn best by doing, so employers should concentrate on interactive learning opportunities, such as simulations, virtual reality, and case-based methods for teaching the aspects of the job. They are likely to expect digital and blended resources as well as traditional approaches to learning their new job responsibilities.
The paper goes on to state that Gen Z members value diversity, equity, inclusivity, sustainability, civic engagement, and organizational transparency. Their social consciousness and diversity may yield a greater range of perspectives and problem-solving approaches which may bolster their sensitivity to patient-centered care.
“The oldest in Gen Z have already seen a recession and a war on terrorism. They’ve seen politics at its worst. And now they’ve seen a global pandemic and are about to see recession again,” said David Stillman, founder of GenGuru, a boutique management consulting firm that provides insights on how best to connect with Baby Boomers, Generation X, Millennials, and Gen Z, in an interview with the Society for Human Resource Management (SHRM). “They are survivors,” he added.
According to the SHRM, “Stillman says Millennials, who preceded Generation Z, were coddled by their parents. He maintains that Generation Z’s parents were more truthful, telling their offspring, ‘You’re going to have a really tough time out there, you have to work super hard,’ which he says created ‘the most competitive generation in the workforce since the Baby Boomers.’”
Gen Z Wants More than a Paycheck, They Want Purpose
The American Journal of Roentgenology paper also states that Gen Z members grew up in a rapidly changing world and tend to be resilient, adaptable, and flexible. They have experienced and witnessed many stressors and navigate these issues by focusing on mental health and a meaningful work-life balance. With respect to a profession, they are searching for more than just a paycheck, and they want a purposeful career where they feel a sense of belonging.
Increase information sharing and transparency to help alleviate fear and anxiety.
Incentivize them by showing them clear paths to career progression.
Make sure they know how their individual contributions matter to the organization.
Motivate them by giving them room for autonomy and experimentation.
Provide specific and constructive feedback.
Harness community and in-person interactions to boost professional collaborations.
Prioritize wellness and mental health.
“Be prepared to spend time with them face to face,” McDonald stated. “They want to be mentored and coached. If you coach them, you’re going to retain them.”
Preparing to Attract Gen Z to Clinical Laboratories
As Generation Z comes of age, more of them will be working in the medical professions. Clinical laboratories and anatomic pathology groups would be well advised to prepare their businesses by adjusting leadership, adapting recruiting efforts, and shifting marketing to attract Zoomers and remain relevant and successful in the future.
Although sweeping statements about individual generations may be limiting, understanding their unique insights, values, and backgrounds can be helpful in the workplace. With a large amount of Gen Z workers now transitioning from college into careers, it will be beneficial for clinical laboratory managers to recognize their unique characteristics to recruit and maintain talented workers more effectively.
Google designed the suite to ease radiologists’ workload and enable easy and secure sharing of critical medical imaging; technology may eventually be adapted to pathologists’ workflow
Clinical laboratory and pathology group leaders know that Google is doing extensive research and development in the field of cancer diagnostics. For several years, the Silicon Valley giant has been focused on digital imaging and the use of artificial intelligence (AI) algorithms and machine learning to detect cancer.
Now, Google Cloud has announced it is launching a new medical imaging suite for radiologists that is aimed at making healthcare data for the diagnosis and care of cancer patients more accessible. The new suite “promises to make medical imaging data more interoperable and useful by leveraging artificial intelligence,” according to MedCity News.
In a press release, medical technology company Hologic, and healthcare provider Hackensack Meridian Health in New Jersey, announced they were the first customers to use Google Cloud’s new suite of medical imaging products.
“Hackensack Meridian Health has begun using it to detect metastasis in prostate cancer patients earlier, and Hologic is using it to strengthen its diagnostic platform that screens women for cervical cancer,” MedCity News reported.
“Google pioneered the use of AI and computer vision in Google Photos, Google Image Search, and Google Lens, and now we’re making our imaging expertise, tools, and technologies available for healthcare and life sciences enterprises,” said Alissa Hsu Lynch (above), Global Lead of Google Cloud’s MedTech Strategy and Solutions, in a press release. “Our Medical Imaging Suite shows what’s possible when tech and healthcare companies come together.” Clinical laboratory companies may find Google’s Medical Imaging Suite worth investigating. (Photo copyright: Influencive.)
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Easing the Burden on Radiologists
Clinical laboratory leaders and pathologists know that laboratory data drives most healthcare decision-making. And medical images make up 90% of all healthcare data, noted an article in Proceedings of the IEEE (Institute of Electrical and Electronics Engineers).
More importantly, medical images are growing in size and complexity. So, radiologists and medical researchers need a way to quickly interpret them and keep up with the increased workload, Google Cloud noted.
“The size and complexity of these images is huge, and, often, images stay sitting in data siloes across an organization,” said Alissa Hsu Lynch, Global Lead, MedTech Strategy and Solutions at Google, told MedCity News. “In order to make imaging data useful for AI, we have to address interoperability and standardization. This suite is designed to help healthcare organizations accelerate the development of AI so that they can enable faster, more accurate diagnosis and ease the burden for radiologists,” she added.
According to the press release, Google Cloud’s Medical Imaging Suite features include:
Imaging Storage: Easy and secure data exchange using the international DICOM (digital imaging and communications in medicine) standard for imaging. A fully managed, highly scalable, enterprise-grade development environment that includes automated DICOM de-identification. Seamless cloud data management via a cloud-native enterprise imaging PACS (picture archiving and communication system) in clinical use by radiologists.
Imaging Lab: AI-assisted annotation tools that help automate the highly manual and repetitive task of labeling medical images, and Google Cloud native integration with any DICOMweb viewer.
Imaging Datasets and Dashboards: Ability to view and search petabytes of imaging data to perform advanced analytics and create training datasets with zero operational overhead.
Imaging AI Pipelines: Accelerated development of AI pipelines to build scalable machine learning models, with 80% fewer lines of code required for custom modeling.
Imaging Deployment: Flexible options for cloud, on-prem (on-premises software), or edge deployment to allow organizations to meet diverse sovereignty, data security, and privacy requirements—while providing centralized management and policy enforcement with Google Distributed Cloud.
First Customers Deploy Suite
Hackensack Meridian Health hopes Google’s imaging suite will, eventually, enable the healthcare provider to predict factors affecting variance in prostate cancer outcomes.
“We are working toward building AI capabilities that will support image-based clinical diagnosis across a range of imaging and be an integral part of our clinical workflow,” said Sameer Sethi, Senior Vice President and Chief Data and Analytics Officer at Hackensack, in a news release.
The New Jersey healthcare network said in a statement that its work with Google Cloud includes use of AI and machine learning to enable notification of newborn congenital disorders and to predict sepsis risk in real-time.
Hologic, a medical technology company focused on women’s health, said its collaboration integrates Google Cloud AI with the company’s Genius Digital Diagnostics System.
“By complementing our expertise in diagnostics and AI with Google Cloud’s expertise in AI, we’re evolving our market-leading technologies to improve laboratory performance, healthcare provider decision making, and patient care,” said Michael Quick, Vice President of Research and Development and Innovation at Hologic, in the press release.
Hologic says its Genius Digital Diagnostics System combines AI with volumetric medical imaging to find pre-cancerous lesions and cancer cells. From a Pap test digital image, the system narrows “tens of thousands of cells down to an AI-generated gallery of the most diagnostically relevant,” according to the company website.
Hologic plans to work with Google Cloud on storage and “to improve diagnostic accuracy for those cancer images,” Hsu Lynch told MedCity News.
Medical image storage and sharing technologies like Google Cloud’s Medical Imaging Suite provide an opportunity for radiologists, researchers, and others to share critical image studies with anatomic pathologists and physicians providing care to cancer patients.
One key observation is that the primary function of this service that Google has begun to deploy is to aid in radiology workflow and productivity, and to improve the accuracy of cancer diagnoses by radiologists. Meanwhile, Google continues to employ pathologists within its medical imaging research and development teams.
Assuming that the first radiologists find the Google suite of tools effective in support of patient care, it may not be too long before Google moves to introduce an imaging suite of tools designed to aid the workflow of surgical pathologists as well.
