Tag: artificial intelligence

Cathay’s AI Underwriting

Posted on by Jake Fishman

Cathay Life Insurance will use Lunit’s INSIGHT CXR AI solution to identify abnormalities in its applicants’ chest X-rays, potentially modernizing a manual underwriting process and uncovering a new non-clinical market for AI vendors.

Lunit INSIGHT CXR will be integrated into Cathay’s underwriting workflow, with the goals of enhancing its radiologists’ accuracy and efficiency, while improving Cathay’s underwriting decisions. 

Lunit and Cathay have reason to be optimistic about this endeavor, given that their initial proof of concept study found that INSIGHT CXR:

  • Improved Cathay’s radiologists’ reading accuracy by 20%
  • Reduced the radiologists’ overall reading time by up to 90%

Those improvements could have a significant labor impact, considering that Cathay’s rads review 30,000 CXRs every year. They might have an even greater business impact, noting the important role that underwriting accuracy has on policy profitability.

Lunit’s part of the announcement largely focused on its expansion beyond clinical settings, revealing plans to “become the driving force of digital innovation in the global insurance market” and to further expand its business into “various sectors outside the hospital setting.”

The Takeaway

Even if life insurers only require CXRs for a small percentage of their applicants (older people, higher value policies), they still review hundreds of thousands of CXRs each year. That makes insurers an intriguing new market segment for AI vendors, and makes you wonder what other non-clinical AI use cases might exist. However, it might also make radiologists who are still skeptical about AI concerned.

AI Experiences & Expectations

Posted on by Jake Fishman

The European Society of Radiology just published new insights into how imaging AI is being used across Europe and how the region’s radiologists view this emerging technology.

The Survey – The ESR reached out to 27,700 European radiologists in January 2022 with a survey regarding their experiences and perspectives on imaging AI, receiving responses from just 690 rads.

Early Adopters – 276 the 690 respondents (40%) had clinical experience using imaging AI, with the majority of these AI users:

  • Working at academic and regional hospitals (52% & 37% – only 11% at practices)
  • Leveraging AI for interpretation support, case prioritization, and post-processing (51.5%, 40%, 28.6%)

AI Experiences – The radiologists who do use AI revealed a mix of positive and negative experiences:

  • Most found diagnostic AI’s output reliable (75.7%)
  • Few experienced technical difficulties integrating AI into their workflow (17.8%)
  • The majority found AI prioritization tools to be “very helpful” or “moderately helpful” for reducing staff workload (23.4% & 62.2%)
  • However, far fewer reported that diagnostic AI tools reduced staff workload (22.7% Yes, 69.8% No)

Adoption Barriers – Most coverage of this study will likely focus on the fact that only 92 of the surveyed rads (13.3%) plan to acquire AI in the future, while 363 don’t intend to acquire AI (52.6%). The radiologists who don’t plan to adopt AI (including those who’ve never used AI) based their opinions on:

  • AI’s lack of added value (44.4%)
  • AI not performing as well as advertised (26.4%)
  • AI adding too much work (22.9%)
  • And “no reason” (6.3%)

US Context – These results are in the same ballpark as the ACR’s 2020 US-based survey (33.5% using AI, only 20% of non-users planned to adopt within 5 years), although 2020 feels like a long time ago.

The Takeaway

Even if this ESR survey might leave you asking more questions (What about AI’s impact on patient care? How often is AI actually being used? How do opinions differ between AI users and non-users?), more than anything it confirms what many of us already know… We’re still very early in AI’s evolution, and there’s still plenty of performance and perception barriers that AI has to overcome.

Burdenless Incidental AI

Posted on by Jake Fishman

A team of IBM Watson Health researchers developed an interesting image and text-based AI system that could significantly improve incidental lung nodule detection, without being “overly burdensome” for radiologists. That seems like a clinical and workflow win-win for any incidental AI system, and makes this study worth a deeper look.

Watson Health’s R&D-stage AI system automatically detects potential lung nodules in chest and abdominal CTs, and then analyzes the text in corresponding radiology reports to confirm whether they mention lung nodules. In clinical practice, the system would flag exams with potentially missed nodules for radiologist review.

The researchers used the AI system to analyze 32k CTs sourced from three health systems in the US and UK. They then had radiologists review the 415 studies that the AI system flagged for potentially missed pulmonary nodules, finding that it:

  • Caught 100 exams containing at least one missed nodule
  • Flagged 315 exams that didn’t feature nodules (false positives)
  • Achieved a 24% overall positive predictive value
  • Produced just a 1% false positive rate

The AI system’s combined ability to detect missed pulmonology nodules while “minimizing” radiologists’ re-reading labor was enough to make the authors optimistic about this type of AI. They specifically suggested that it could be a valuable addition to Quality Assurance programs, improving patient care while avoiding the healthcare and litigation costs that can come from missed findings.

The Takeaway

Watson Health’s new AI system adds to incidental AI’s growing momentum, joining a number of research and clinical-stage solutions that emerged in the last two years. However, this system’s ability to cross-reference radiology report text and apparent ability to minimize false positives are relatively unique. 

Even if most incidental AI tools aren’t ready for everyday clinical use, and their potential to increase re-read labor might be alarming to some rads, these solutions’ ability to catch earlier stage diseases and minimize the impact of diagnostic “misses” could earn the attention of a wide range of healthcare stakeholders going forward.

The Case for Algorithmic Audits

Posted on by Jake Fishman

A new Lancet Digital Health study could have become one of the many “AI rivals radiologists” papers that we see each week, but it instead served as an important lesson that traditional performance tests might not prove that AI models are actually safe for clinical use.

The Model – The team developed their proximal femoral fracture detection DL model using 45.7k frontal X-rays performed at Australia’s Royal Adelaide Hospital (w/ 4,861 fractures).

The Validation – They then tested it against a 4,577-exam internal set (w/ 640 fractures), 400 of which were also interpreted by five radiologists (w/ 200 fractures), and against an 81-image external validation set from Stanford.

The Results – All three tests produced results that a typical study might have viewed as evidence of high-performance: 

  • The model outperformed the five radiologists (0.994 vs. 0.969 AUCs)
  • It beat the best performing radiologist’s sensitivity (95.5% vs. 94.5%) and specificity (99.5% vs 97.5%)
  • It generalized well with the external Stanford data (0.980 AUC)

The Audit – Despite the strong results, a follow-up audit revealed that the model might make some predictions for the wrong reasons, suggesting that it is unsafe for clinical deployment:

  • One false negative X-ray included an extremely displaced fracture that human radiologists would catch
  • X-rays featuring abnormal bones or joints had a 50% false negative rate, far higher than the reader set’s overall false negative rate (2.5%)
  • Salience maps showed that AI decisions were almost never based on the outer region of the femoral neck, even with images where that region was clinically relevant (but it still often made the right diagnosis)
  • The model scored a high AUC with the Stanford data, but showed a substantial model operating point shift

The Case for Auditing – Although the study might have not started with this goal, it ended up becoming an argument for more sophisticated preclinical auditing. It even led to a separate paper outlining their algorithmic auditing process, which among other things suggested that AI users and developers should co-own audits.

The Takeaway

Auditing generally isn’t the most exciting topic in any field, but this study shows that it’s exceptionally important for imaging AI. It also suggests that audits might be necessary for achieving the most exciting parts of AI, like improving outcomes and efficiency, earning clinician trust, and increasing adoption.A new Lancet Digital Health study could have become one of the many “AI rivals radiologists” papers that we see each week, but it instead served as an important lesson that traditional performance tests might not prove that AI models are actually safe for clinical use.

Radiology’s AI ROI Mismatch

Posted on by Jake Fishman

A thought-provoking JACR editorial by Emory’s Hari Trivedi MD suggests that AI’s slow adoption rate has little to do with its quality or clinical benefits, and a lot to do with radiology’s misaligned incentives.

After interviewing 25 clinical and industry leaders, the radiology professor and co-director of Emory’s HITI Lab detailed the following economic mismatches:

  • Private Practices value AI that improves radiologist productivity, allowing them to increase reading volumes without equivalent increases in headcount. That makes triage or productivity-focused AI valuable, but gives them no economic justification to purchase AI that catches incidentals, ensures follow-ups, or reduces unnecessary biopsies.
  • Academic centers or hospitals that own radiology groups have far more to gain from AI products that detect incidental/missed findings and then drive internal admissions, referrals, and procedures. That means their highest-ROI AI solutions often drive revenue outside of the radiology department, while creating more radiologist labor.
  • Community hospital emergency departments value AI that allows them to discharge or treat emergency patients faster, although this often doesn’t economically benefit their radiology departments or partner practices.
  • Payor/provider health systems (e.g. the VA, Intermountain, Kaiser) can be open to a broad range of AI, but they especially value AI that reduces costs by avoiding unnecessary tests or catching early signs of diseases.


The Takeaway

People tend to paint imaging AI with a wide brush (AI is… all good, all bad, a job stealer, or the future) and we’ve seen a similar approach to AI adoption barrier editorials (AI just needs… trust, reimbursements, integration, better accuracy, or the killer app). However, even if each of these adoption barriers are solved, it’s hard to see how AI could achieve widespread adoption if the groups paying for AI aren’t economically benefiting from it.

Because of that, Dr. Trivedi encourages vendors to develop AI that provides “returns” to the same groups that make the “investments.” That might mean that few AI products achieve widespread adoption on their own, but a diverse group of specialized AI products achieve widespread use across all radiology settings.

Creating a Cancer Screening Giant

Posted on by Jake Fishman

A few days after shocking the AI and imaging center industries with its acquisitions of Aidence and Quantib, RadNet’s Friday investor briefing revealed a far more ambitious AI-enabled cancer screening strategy than many might have imagined.

Expanding to Colon Cancer – RadNet will complete its AI screening platform by developing a homegrown colon cancer detection system, estimating that its four AI-based cancer detection solutions (breast, prostate, lung, colon) could screen for 70% of cancers that are imaging-detectable at early stages.

Population Detection – Once its AI platform is complete, RadNet plans to launch a strategy to expand cancer screening’s role in population health, while making prostate, lung, and colon cancer screening as mainstream as breast cancer screening.

Becoming an AI Vendor – RadNet revealed plans to launch an externally-focused AI business that will lead with its multi-cancer AI screening platform, but will also create opportunities for RadNet’s eRAD PACS/RIS software. There are plenty of players in the AI-based cancer detection arena, but RadNet’s unique multi-cancer platform, significant funding, and training data advantage would make it a formidable competitor.

Geographic Expansion – RadNet will leverage Aidence and Quantib’s European presence to expand its software business internationally, as well as into parts of the US where RadNet doesn’t own imaging centers (RadNet has centers in just 7 states).

Imaging Center Upsides – RadNet’s cancer screening AI strategy will of course benefit its core imaging center business. In addition to improving operational efficiency and driving more cancer screening volumes, RadNet believes that the unique benefits of its AI platform will drive more hospital system joint ventures.

AI Financials – The briefing also provided rare insights into AI vendor finances, revealing that DeepHealth has been running at a $4M-$5M annual loss and adding Aidence / Quantib might expand that loss to $10M- $12M (seems OK given RadNet’s $215M EBITDA). RadNet hopes its AI division will become cash flow neutral within the next few years as revenue from outside companies ramp up.

The Takeaway

RadNet has very big ambitions to become a global cancer screening leader and significantly expand cancer screening’s role in society. Changing society doesn’t come fast or easy, but a goal like that reveals how much emphasis RadNet is going to place on developing and distributing its AI cancer screening platform going forward.

IBM Sells Watson Health

Posted on by Jake Fishman

IBM is selling most of its Watson Health division to private equity firm Francisco Partners, creating a new standalone healthcare entity and giving both companies (IBM and the former Watson Health) a much-needed fresh start. 

The Details – Francisco Partners will acquire Watson Health’s data and analytics assets (including imaging) in a deal that’s rumored to be worth around $1B and scheduled to close in Q2 2022. IBM is keeping its core Watson AI tech and will continue to support its non-Watson healthcare clients.

Francisco’s Plans – Francisco Partners seems optimistic about its new healthcare company, revealing plans to maintain the current Watson Health leadership team and help the company “realize its full potential.” That’s not always what happens with PE acquisitions, but Francisco Partners has a history of growing healthcare companies (e.g. Availity, Capsule, GoodRx, Landmark Health) and there are a lot of upsides to Watson Health (good products, smart people, strong client list, a bargain M&A multiple, seems ideal for splitting up).

A Necessary Split – Like most Watson Health stories published over the last few years, news coverage of this acquisition overwhelmingly focused on Watson Health’s historical challenges. However, that approach seems lazy (or at least unoriginal) and misses the point that this split should be good news for both parties. IBM now has another $1B that it can use towards its prioritized hybrid cloud and AI platform strategy, and the new Watson Health company can return to growth mode after several years of declining corporate support.

Imaging Impact – IBM and Francisco Partners’ announcements didn’t place much focus on Watson Health’s imaging business, but it seems like the imaging group will also benefit from Francisco Partners’ increased support and by distancing itself from a brand that’s lost its shine. Even losing the core Watson AI tech should be ok, given that the Merge PACS team has increasingly shifted to a partner-focused AI strategy. That said, this acquisition’s true imaging impact will be determined by where the imaging group lands if/when Francisco Partners decides to eventually split up and sell Watson Health’s various units.

The Takeaway – The IBM Watson Health story is a solid reminder that expanding into healthcare is exceptionally hard, and it’s even harder when you wrap exaggerated marketing around early-stage technology and high-multiple acquisitions. Still, there’s plenty of value within the former Watson Health business, which now has an opportunity to show that value.

Right Diagnoses, Wrong Reasons

Posted on by Jake Fishman

An AJR study shared new evidence of how X-ray image labels influence deep learning decision making, while revealing one way developers can address this issue.

Confounding History – Although already well known by AI insiders, label and laterality-based AI shortcuts made headlines last year when they were blamed for many COVID algorithms’ poor real-world performance. 

The Study – Using 40k images from Stanford’s MURA dataset, the researchers trained three CNNs to detect abnormalities in upper extremity X-rays. They then tested the models for detection accuracy and used a heatmap tool to identify the parts of the images that the CNNs emphasized. As you might expect, labels played a major role in both accuracy and decision making.

  • The model trained on complete images (bones & labels) achieved an 0.844 AUC, but based 89% of its decisions on the radiographs’ laterality/labels.
  • The model trained without labels or laterality (only bones) detected abnormalities with a higher 0.857 AUC and attributed 91% of its decision to bone features.
  • The model trained with only laterality and labels (no bones) still achieved an 0.638 AUC, showing that AI interprets certain labels as a sign of abnormalities. 

The Takeaway – Labels are just about as common on X-rays as actual anatomy, and it turns out that they could have an even greater influence on AI decision making. Because of that, the authors urged AI developers to address confounding image features during the curation process (potentially by covering labels) and encouraged AI users to screen CNNs for these issues before clinical deployment.

The False Hope of Explainable AI

Posted on by Jake Fishman

Many folks view explainability as a crucial next step for AI, but a new Lancet paper from a team of AI heavyweights argues that explainability might do more harm than good in the short-term, and AI stakeholders would be better off increasing their focus on validation.

The Old Theory – For as long as we’ve been covering AI, really smart and well-intentioned people have warned about the “black-box” nature of AI decision making and forecasted that explainable AI will lead to more trust, less bias, and greater adoption.

The New Theory – These black-box concerns and explainable AI forecasts might be logical, but they aren’t currently realistic, especially for patient-level decision support. Here’s why:

  • Explainability methods describe how AI systems work, not how decisions are made
  • AI explanations can be unreliable and/or superficial
  • Most medical AI decisions are too complex to explain in an understandable way
  • Humans over-trust computers, so explanations can hurt their ability to catch AI mistakes
  • AI explainability methods (e.g heat maps) require human interpretation, risking confirmation bias
  • Explainable AI adds more potential error sources (AI tool + AI explanation + human interpretation)
  • Although we still can’t fully explain how acetaminophen works, we don’t question whether it works, because we’ve tested it extensively

The Explainability Alternative – Until suitable explainability methods emerge, the authors call for “rigorous internal and external validation of AI models” to make sure AI tools are consistently making the right recommendations. They also advised clinicians to remain cautious when referencing AI explanations and warned that policymakers should resist making explainability a requirement. 

Explability’s Short-Term Role – Explainability definitely still has a role in AI safety, as it’s “incredibly useful” for model troubleshooting and systems audits, which can improve model performance and identify failure modes or biases.

The Takeaway – It appears we might not be close enough to explainable AI to make it a part of short-term AI strategies, policies, or procedures. That might be hard to accept for the many people who view the need for AI explainability as undebatable, and it makes AI validation and testing more important than ever.

ImageBiopsy Lab & UCB’s AI Alliance

Posted on by Jake Fishman

Global pharmaceutical company UCB recently licensed its osteoporosis AI technology to MSK AI startup ImageBiopsy Lab, representing an interesting milestone for several emerging AI business models.

The UCB & ImageBiopsy Lab Alliance – ImageBiopsy Lab will use UCB’s BoneBot AI technology to develop and commercialize a tool that screens CT scans for “silent” spinal fractures to identify patients who should be receiving osteoporosis treatments. The new tool will launch by 2023 as part of ImageBiopsy Lab’s ZOO MSK platform.

UCB’s AI Angle – UCB produces an osteoporosis drug that would be prescribed far more often if detection rates improve (over 2/3 of vertebral fractures are currently undiagnosed). That’s why UCB developed and launched BoneBot AI in 2019 and it’s why the pharma giant is now working with ImageBiopsy Lab to bring it into clinical use.

The PharmaAI Trend – We’re seeing a growing trend of drug and device companies working with AI developers to help increase treatment demand. The list is getting pretty long, including quite a few PharmaAI alliances targeting lung cancer treatment (Aidence & AstraZeneca, Qure.ai & AstraZeneca, Huma & Bayer, Optellum & J&J) and a diverse set of AI alliances with medical device companies (Imbio & Olympus for emphysema, Aidoc & Inari for PE, Viz.ai & Medtronic for stroke).

The Population Health AI Trend – ImageBiopsy Lab’s BoneBot AI licensing is also a sign of AI’s growing momentum in population health, following increased interest from academia and major commercial efforts from Cleerly (cardiac screening) and Zebra Medical Vision (cardiac and osteoporosis screening… so far). This alliance also introduces a new type of population health AI beneficiary (pharma companies), in addition to risk holders and patients.

The Takeaway – ImageBiopsy Lab and UCB’s new alliance didn’t get a lot of media attention last week, but it tells an interesting story about how AI business models are evolving beyond triage, and how those changes are bringing some of healthcare’s biggest names into the imaging AI arena.

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