Hospitals in 38 states confirmed patient infections of the dangerous, drug-resistant fungus
Rapidly spreading Candida auris fungus is once again showing up in hospitals throughout the United States, with multiple cases confirmed in Georgia and Florida. Hospital laboratories and pathology departments are encouraged to take advantage of CDC resources to help in the diagnosis of this deadly pathogen.
Candida auris (C. auris) spreads between patients in hospital settings, is resistant to anti-fungal medications, and can cause severe illness, according to the Centers for Disease Control and Prevention (CDC). Tracking data from CDC’s National Notifiable Diseases Surveillance System found 4,514 new clinical cases of C. auris in the US in 2023.
“The number of clinical cases has continued to increase since the first US case was reported in 2016,” said the CDC of past outbreaks of C. auris. “Based on information from a limited number of patients, 30–60% of people with C. auris infections have died. However, many of these people had other serious illnesses that also increased their risk of death.” The fungus has been spreading at a high rate from 2016-2023 with several cases cropping up recently in Georgia.
According to representatives from the Georgia Department of Public Health, “the state has seen over 1,300 cases as of the end of February,” WJCL reported.
The Hill reports a significant recent increase in the spread of the fungus in all but 12 states. Though the number of cases in each state remains small, the overall percentage of increased cases is large and growing.
And a study conducted at Jackson Health System in Miami, Fla., and published in the American Journal of Infection Control, found that “The volumes of clinical cultures with C. auris have rapidly increased, accompanied by an expansion in the sources of infection.”
“If you get infected with this pathogen that’s resistant to any treatment, there’s no treatment we can give you to help combat it. You’re all on your own,” Melissa Nolan, PhD, associate professor of epidemiology and biostatistics at the Arnold School of Public Health, University of South Carolina, told Nexstar. (Photo copyright: University of South Carolina.)
CDC Recommendations
The deadly fungus was first detected in 2016 in US hospitals, and the number of cases in hospital patients has grown every year based on CDC data from 2023. Invasive medical procedures can provide a gateway for C. auris to infect patients, and the immunosuppressed nature of these patients can lead to further complications.
Invasive procedures that could expose a patient to C. auris include the placing of breathing and feeding tubes, and the insertion of vein or urinary catheters.
“We’ve had four people at one time on and off over the past few months, and in years past, it was unusual to have one or even two people with Candida auris in our hospital,” Timothy Connelly, MD, told WJCL about the spread of the fungus at Memorial Health in Savannah, Ga.
Cases have also rapidly increased in Miami according to the Jackson Health System study. The researchers found that, “The volumes of clinical cultures increased every year and infection sources expanded.”
The CDC considers C. auris “an urgent antimicrobial resistance threat” based on the severe risk an infected patient can face. “The rapid rise and geographic spread of cases is concerning and emphasizes the need for continued surveillance, expanded lab capacity, quicker diagnostic tests, and adherence to proven infection prevention and control,” said Meghan Lyman, MD, in a CDC news release.
Fungal Infection is Difficult to Treat and Diagnose
C. auris has been shown to be resistant to antifungal medications, making it an acute threat to ill patients. And since it tends to infect already sick patients, it can be difficult to detect because symptoms of infection can be generic, such as fever or chills.
The fungus is also adept at surviving on hospital surfaces.
“It’s really good at just being, generally speaking, in the environment,” Melissa Nolan, PhD, associate professor of epidemiology and biostatistics at the Arnold School of Public Health, University of South Carolina, told Nexstar. “So, if you have it on a patient’s bed for example, on the railing, and you go to wipe everything down, if in whatever way maybe a couple of pathogens didn’t get cleared, then they’re becoming resistant. And so over time, they can kind of grow and populate in that hospital environment.”
CDC Resources to Help Identify C. auris
C. auris also can be misidentified with other candida species fungi. The CDC recommends identification using a diagnostic device “based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF).” The CDC also recommends using supplemental MALDI-TOF databases and molecular methods to help distinguish C. auris from other candida.
Prompt clinical laboratory diagnosis is extremely important to stem outbreaks as they become more frequent in hospital settings. The CDC offers resources for hospital pathology departments to aid in screening and detection.
“I think we need to do a better job of predicting,” Nolan told Nexstar. “Moving forward [we need] more funding to support quality surveillance of these potential infectious strains so that we can know in advance, and we can do a better job of stopping disease spread before it becomes a problem.”
According to the CDC, the fungus typically spreads in hospital settings and is not known to affect healthy people.
Artificial intelligence tools for radiology, clinical laboratory, and pathology diagnostics continue to advance and improve
Researchers in Germany have developed a fully automated, artificial intelligence (AI) tool that improves the diagnosis of prostate cancer. Developed by mediaire, a company that creates AI-based tools for radiologists, the software reduces clinical workloads and could be beneficial in counteracting issues associated with variability in magnetic resonance imaging (MRI) reporting. This is another example of AI’s growth in the clinical diagnostic industry, including clinical laboratory and pathology medicine.
The software, called mdprostate, has received the mandatory certification mark (CE or European Conformity) for products sold within the European Economic Area (EEA). It is now commercially available in those countries and was recently incorporated into the picture archiving and communications system (PACS) of some healthcare organizations and applied to a group of patients who had undergone a multiparametric prostate MRI (mpMRI).
The goal was to compare the overall performance of mdprostate against radiologists who executed the initial interpretations of the mpMRIs, according to Health Imaging.
“Mdprostate is intended to support radiologists by automating time-consuming processes and improving the objectivity of diagnosis through data quantification,” said Tonia Michaely, chief of staff at mediaire, in a news release.
“By providing objective assessments and standardizing lesion detection and classification, AI has the potential to augment radiologists’ performance throughout the PCa [prostate cancer] diagnostic pathway,” Nadine Bayerl, Dr. med., a radiologist with the Institute of Radiology at University Hospital Erlangen and corresponding author of the mediaire study, told Health Imaging. (Photo copyright: University Hospital Erlangen.)
Scoring Cancer Risk
To perform the comparison, a team of researchers applied the AI tool to 123 prostate MRI exams followed by systematic and targeted biopsies. The software was instructed to automatically segment the prostrate, calculate prostate volume, and classify lesions per the Prostate Imaging Reporting and Data System (PI-RADS).
PI-RADS, according to the America College of Radiology, is a reporting method that indicates how likely a lesion is to be clinically significant cancer on a score of one to five:
PI-RADS 1: very low (clinically significant cancer is highly unlikely to be present).
PI-RADS 2: low (clinically significant cancer is unlikely to be present).
PI-RADS 3: intermediate (the presence of clinically significant cancer is equivocal).
PI-RADS 4: high (clinically significant cancer is likely to be present).
PI-RADS 5: very high (clinically significant cancer is highly likely to be present).
For PI-RADS scores greater than two, mdprostate generated 100% sensitivity and dismissed all cancers for lesions that were below that threshold. For PI-RADS scores of four or higher, the AI tool yielded 85.5% sensitivity and specificity of 63.2% for clinically significant cancers.
Deep Learning in Diagnostic Pathway
“In practical terms, these results indicate that when a case falls below the PI-RADS ≥ 2 cutoff, clinicians can rule out malignancy with a high degree of confidence,” the authors explained in the European Journal of Radiology. “This capability is particularly valuable in clinical decision-making, as it allows for the safe avoidance of unnecessary biopsies or further invasive procedures in these patients.”
“Recent advances in deep learning algorithms, facilitated by larger labeled datasets, improved computing hardware, and refined training techniques, have led to several studies highlighting the diagnostic value of deep learning algorithms in prostate imaging,” radiologist Nadine Bayerl, Dr. med., Institute of Radiology at University Hospital Erlangen and corresponding author of the study, told Health Imaging.
The software “demonstrated high diagnostic performance in identifying and grading prostate lesions, with results comparable to those reported in meta-analyses of expert readers using PI-RADS,” the researchers noted in their published study.
“Its ability to standardize evaluations and potentially reduce variability underscores its potential as a valuable adjunct in the prostate cancer diagnostic pathway. The high accuracy of mdprostate, particularly in ruling out prostate cancers, highlights its clinical utility by reducing workload and enhancing patient outcomes,” they concluded.
AI in Clinical Laboratories and Pathology
Dark Daily has frequently covered AI’s expanding role in clinical laboratory testing and pathology diagnostics. At the recent Executive War College, a dozen sessions explored its growth in the industry. During one session, Sam Terese, CEO and president at Alverno Laboratories said, “AI is allowing us to drive our business. It is really resonating that we need to use AI in the future.”
Members who could not attend the 2025 Executive War College can order audio recordings of these valuable sessions by clicking here.
At least a dozen sessions at the 2025 Executive War College explored artificial intelligence use in clinical labs
Although not explicitly stated, it was clear at the 2025 Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management conference that artificial intelligence must be a path forward for labs to control costs in an unfavorable economic environment.
Even though the technology is largely unproven in clinical lab settings, the potential of artificial intelligence (AI) in labs is enough reason for laboratory leaders to explore it further.
“AI is allowing us to drive our business,” said Sam Terese, CEO and president at Alverno Laboratories, during a general session at the Executive War College. “It is really resonating that we need to use AI in the future.”
Clinical laboratory leaders should constantly ask themselves whether there is an AI solution to a problem, advised Sam Terese (above), CEO and president at Alverno Laboratories. Terese spoke at the 2025 Executive War College. (Photo copyright: LabX.)
‘Not a Lot of Trust’ in AI from Patients
Terese acknowledged that patients do not yet feel comfortable with the technology. “When you throw AI and healthcare together, from the public’s perspective, there’s not a lot of trust,” he said.
That said, Alverno is committed to increased use of AI in clinical labs in 2025, including for:
Terese urged laboratory owners and executives to not underestimate how quickly AI adoption could spread within the clinical lab industry. Digital pathology took half a century to evolve into its current state, but “AI took five years. The timeline is moving rapidly,” he observed.
Don’t Move Blindly Forward with AI, Experts Warn
At least a dozen sessions at this week’s Executive War College addressed an aspect of AI in labs.
Lab leaders must advise their staff to use AI with systems or processes that can tolerate mistakes because AI will get things wrong, Cecchini added.
“I treat AI like an eager intern where you have to check everything it does,” he said.
Presenter Ankit Ranjan, PhD, founder of AI company Sample Healthcare, agreed with that sentiment. He suggested that clinical laboratories should consider AI as a copilot until its algorithms can prove to lab staff that conclusions or predictions are accurate. The long game for AI in labs is not to cut a few staff from the budget but instead act as a revenue driver.
“Inserting AI into end-to-end processes is what really addresses problems,” Ranjan said.
Watch for much more coverage about the state of AI in clinical laboratories in upcoming issues of The Dark Report. If you’re not a subscriber, it’s a great time to take a free trial of our business intelligence briefings.
30th edition of the conference returns to New Orleans this week, bringing together diagnostic lab executives and innovators
Medical laboratory leaders and executives, along with diagnostic innovators from across the country, are convening in New Orleans this week for the 30th annual Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management.
Given the political, regulatory, and financial upheaval occurring in the US, this year’s Executive War College gathering offers a timely opportunity for lab leaders to discuss important strategies and network with sellers.
Featuring 85 sessions across two days, attendees will delve into key topics such as revenue growth strategies, regulatory updates, AI integration, workforce development, and evolving payer dynamics.
With clinical laboratories playing an increasingly pivotal role in value-based care and patient outcomes, the 2025 conference agenda reflects a growing need for labs to operate not just as diagnostic services but as critical business units driving healthcare innovation.
Robert Michel (above), founder of the Executive War College and outgoing editor-in-chief of Dark Daily, will lead a closing session on Wednesday looking at common themes that emerged during the Executive War College. “It’s important that lab leaders take note of what they learned—whether it was during a session, networking reception, or chance meeting with a peer—before heading back to their organizations,” he said. (Photo copyright: LabX.)
Event Will Illustrate Paths Forward for Lab Industry
Robert Michel, founder of the Executive War College and outgoing editor-in-chief of Dark Daily, previously noted that smart laboratory leaders are viewing financial, staffing, and operational pressures as opportunities to move ahead.
“This path forward is informed by two longstanding precepts recognized by innovative managers,” Michel said. “One precept is ‘Change creates new winners and losers.’ The other precept is ‘Change creates opportunity.’ Savvy lab leaders recognize the powerful truths in each precept.”
The general sessions kick off Tuesday morning with a keynote address from Michel titled, “Healthcare at a Tipping Point: Why Lab Opportunities and Challenges in Coming Years Will Be Different than Those of the Past 30 Years.” The keynote will synthesize nationwide trends, setting the stage for two days of in-depth discussion.
Other general sessions on Tuesday will explore the continued move towards precision medicine, how to take innovative steps to improve lab operations, and ways to elevate the value of laboratory services.
Agenda Features More than One Dozen AI-Themed Sessions
Artificial intelligence (AI) will be another hot topic this year, particularly as labs grapple with how to harness a technology that just in the past year seems to have proliferated exponentially.
This year’s Executive War College will devote more than a dozen sessions to AI discussions, as experts from both the technology and pathology sectors dissect AI’s current capabilities, legal aspects, and financial implications.
Another major focus for 2025 is the regulatory environment. Several sessions will provide updates on the latest CLIA inspection deficiencies, where regulators stand on current concerns, and the future of laboratory developed test oversight given a federal court’s recent decision to vacate the Food and Drug Administration’s final rule on LDTs.
Wide Swath of Laboratory Influencers Expected
Nearly 1,000 attendees, speakers, and vendor representatives are expected at the Executive War College, including C-level executives, pathologists, lab directors, and business development leaders.
Watch Dark Daily this week for further updates from New Orleans, including coverage of the opening day’s general sessions and a wrap-up of what lab leaders learned during the event.
This follows class action lawsuits in multiple states against insurance companies that deny millions of healthcare claims each year
Artificial intelligence (AI) has become ubiquitous in many aspects of healthcare. But perhaps its most controversial use is in the payer denial-of-claims process. Multiple states are pursuing legislation that would limit or outright ban AI’s use without physician involvement.
Clinical laboratories experience payment denials at both the prior authorization stage when a doctor orders a lab test as well as when the claim is submitted for reimbursement. And many labs perform tests for which they know they will not be paid just to maintain the client account relationships with doctors.
Now, several states are taking measures to protect patients from what some say is a dangerous trend to use AI algorithms only to review and deny medical claims for critical healthcare and clinical laboratory testing. This will be of interest to lab managers and those in charge of their lab’s revenue.
“Physicians and patients already face daunting challenges in navigating medical insurers’ bureaucratic administrative processes,” said Arizona Medical Association (ArMA) President Nadeem Kazi, MD, in a news release. “Taking physicians’ clinical experience out of these processes entirely is a misguided step,” he added.
However, on March 13, the Arizona Senate’s Finance Committee altered the language in its version of the bill. In it, AI is not specifically mentioned.
Instead, the bill’s language now “requires a medical director or healthcare provider, before a healthcare insurer may deny a claim or issue a direct denial of a prior authorization, to individually review any denial that involves medical necessity or experimental status or that requires the use of medical judgment and prohibits the director or provider from relying solely on recommendations derived from any other source during the prior authorization denial or claim denial review.”
Presumably, “any other source” includes AI-driven software platforms used by payers for prior authorization denials and claims processing.
“While AI promises innovation for several areas of healthcare, the review and denial of medical insurance claims—some of which represent life-changing treatments and procedures—should be left to physicians who can make nuanced clinical judgments,” said Shelby Job, ArMA communications director, in a statement following that state’s passage of the House bill in February.
The bill is now being debated in the Arizona Senate. If the Senate passes its version, the two sides will need to reconcile their bills.
“Patients deserve healthcare delivered by humans with compassionate medical expertise, not pattern-based computer algorithms designed by insurance companies,” said ArMA President Nadeem Kazi, MD (above), in a news release. (Photo copyright: Arizona Medical Association.)
Multiple States Move to Limit Use of AI in Claims Denials
In an Arizona House of Representatives Committee on Commerce meeting, state Republican representative Julie Willoughby, who is also an ER nurse, said that “she hopes the bill will protect Arizonians from losing healthcare access due to AI interference,” NBC News reported following passage of the House bill.
“What we’re asking for in this is that any claims that are denied have a provider look them over for completeness to ensure that there isn’t anything that the AI algorithm may not have accounted for,” she said.
If signed into law, the bill will require a medical director at the insurance carrier in question to “individually review each claim or prior authorization before a healthcare insurer is able to deny a claim for that patient,” NBC News noted.
California passed similar legislation in September that would “ensure that a licensed physician supervises the use of AI decision-making tools when they are used to inform decisions to approve, modify, or deny requests by providers,” NBC News reported.
The author of the California bill, Democratic senator Josh Becker, JD, argued upon the bill’s passing that AI “should never replace the expertise and judgment of physicians,” adding, “An algorithm cannot fully understand a patient’s unique medical history or needs, and its misuse can lead to devastating consequences.”
And in Texas, a bill introduced by Republican senator Charles Schwertner, MD, states that AI “should not be used as the ‘sole basis of a decision to wholly or partly deny, delay, or modify healthcare services,’” NBC News reported.
In a statement, the Texas Coalition of Patients said the bill is “crucial in ensuring that life-altering healthcare decisions remain in the hands of medical professionals rather than Big Insurance’s automated systems.”
In all, 11 states have introduced legislation to “to push back on artificial intelligence use in reviewing medical claims,” according to NBC News.
In May 2023, The Dark Report explored payer claims denials, and it was acknowledged back then that automated systems were already reviewing claims.
And then there are the lawsuits. According to The Guardian, Cigna, Humana, and UnitedHealth all face class-action lawsuits concerning the use of AI to “deny lifesaving care.”
Can AI Coexist with Human-based Care?
Although at this time AI may not understand the nuanced complexities of healthcare claims, there seem to be plenty of uses for it in healthcare decision-making. It can analyze large sets of data for diagnosis, transcribe medical documents using automatic speech recognition, and streamline administrative tasks––all of which can help a workforce plagued by staff burnout and shortages, Los Angeles Pacific University noted.
And though its use in payer claims reviews and denials is being resisted, AI will likely continue to help doctors diagnose disease and make better treatment decisions. Nevertheless, clinical laboratory and pathology workers should be aware of how the tool is being used and keep an eye out for suspicious claims denials.
