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AI in Healthcare: Symposium Insights

For years, artificial intelligence (AI) has been growing behind the scenes of our lives. Starting off as modifications of not‑so‑simple algorithms, early large language models could barely string a few words together, much like early vision systems that struggled to distinguish a lamppost from a cat in digital images. More recently AI has not just grown but proliferated—like Darwin’s finches in the Galapagos—into nearly every niche available in the digital world.

AI has infiltrated into daily life personally and professionally for many, and while modern healthcare has historically been hesitant to adapt to new technologies, Raghav Mani, director of Digital Health at Nvidia, pointed out that healthcare is adopting AI at three times the rate of other industries. Clearly, there is a lot to discuss, which is why The New York Academy of Sciences and the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine at Mount Sinai co-hosted the 3rd annual “New Wave of AI in Healthcare,” a two-day symposium on May 12 and 13 with the goal of opening discourse between researchers, clinicians, industry leaders and other interested parties on all topics related to AI and healthcare.

Day one

The first day opened with a lightning round of welcome remarks from organizers expressing their personal experience with AI in healthcare research and practice. While some, like Nicholas Dirks, PhD, president and CEO of The New York Academy of Sciences shared concerns about how to maintain human involvement in AI use, he also expressed awe stating that “The pace of progress is breathtaking.”

Others were more practical in their assessments. Lisa Stump, chief digital information officer at Mount Sinai Health System asserted, “The future is not something we enter, it’s something we create.” Similarly, Brendan G. Carr, MD, CEO, Mount Sinai Health System, described AI as a “new partner” to aid clinicians in synthesizing the vast and growing clinical data. Girish N. Nadkarni, MD, a nephrologist and practicing clinician at Icahn School of Medicine at Mount Sinai summarized the whole event before the first talk even began: “The real question is not IF AI will transform healthcare, but HOW.”

The keynote presentation leading day one’s discussions endeavored to answer that very question. With his talk entitled, “Harnessing the power of Platform Thinking to Transform Healthcare,” John Halamka, MD, president of the Mayo Clinic Platform, spent 30 minutes exploring the power of data while questioning how AI is and should be used to analyze the varied data currently available, but cautioned that this is no simple task when considering the sources of data and potential restrictions on data use. He spoke about practical applications of AI data analysis that have and can be done, including in drug discovery. He also pointed out that AI can fill gaps in the healthcare workforce.

The day continued with four talks exploring different aspects of AI model use in healthcare. Marina Sirota, PhD, professor at the University of California, San Francisco spoke about how clinical data can be used for predictive medicine. Others, including Mani and Jonathan Carlson, PhD, vice president and managing director of Microsoft Heath Futures, discussed how AI agents and models can be used as part of hospital and clinician toolkits at multiple levels—not just as data analysis engines, but also to aid in synthesizing patient data and diagnostic support. Rounding out the discussion, Azra Bihorac, MD, senior associate dean for research at the University of Florida described how AI models need to be validated just like any other tool. She also pointed out that while AI is continuously improving in its ability to assess problems and suggest the next best course of action, human input is vital for collaborative success.

Panel discussion moderated by Robert Freeman, DNP. Panelists from left to right: Pierre Elias, MD, Karen Wong, MD and Alexander Fedotov, PhD

The final talks for day one focused on how AI can be used directly with patient care situations. Following their individual talks on how AI can be integrated into electronic health records (EHR), combining models to develop new insights, or reimagining diagnosis ability to improve diagnostic equity, the final three speakers engaged in a dynamic, and sometimes heated panel discussion. Karen Wong, MD, a physician at Epic, Alexander Fedotov, PhD, director of AI digital precision health at AstraZeneca and Pierre Elias, MD, assistant professor at Columbia University Irving Medical Center each shared their thoughts on how AI will be used in the near future. While they were all in agreement that AI cannot replace clinicians, they also recognized that AI will be a disruptive force, but it’s up to clinicians to take responsibility to use the technology as appropriate but to rely on their intuition and judgement as trained professionals. When opining on the future of AI use in healthcare five years from now, Fedotov stated, “I would still want to see humans at the helm of all the decision maker processes.”

Day two

While the first day laid the foundations for AI use in healthcare spanning bench to bedside, the second day of the symposium included more discussion and criticism of AI on the logistic level.

Fireside chat between Girish N. Nadkarni, MD and Dave A. Chokshi, MD

The day began with a keynote fireside chat between Nadkarni and Dave A. Chokshi, MD, a physician and professor at City University of New York, and former NYC health commissioner. He spoke about his leadership experiences, sharing many anecdotes of his time as a public health advocate and communicator during the COVID-19 pandemic. When questioned on the importance of communication considering the state of healthcare and declining trust of the public—especially with the increased use of AI, which has the potential of adding layers of feelings of abandonment, surveillance, and impersonalization—Chokshi pointed out that “It makes relationships even more important that we know then are.” He stressed that a his job, as a clinician, is to build trust with patients, and make sure that they return for care. While he envisions AI being transformative to healthcare in the next few years, he cautioned that listening and integrating feedback from front line users, clinical staff and patients, will be vital.

The morning continued with talks exploring AI’s use in research and learning in healthcare. Joshua C. Denny, MD, CEO of NIH All of Us Research, delivered a detailed summary of the progress and of the All of Us project. Despite recent funding concerns and cuts, the project scope remains on track, and researchers world-wide are utilizing the data derived from this project and how the project leads are working to establish parameters and modules for researchers to more easily implement AI in their data analysis. Andrew Gruen, PhD, standards lead at MLCommons, then spoke animatedly about the importance of establishing standards and benchmarks for AI use in researcher and healthcare settings. He spoke candidly on the need to not just train AI but to have external evaluation and validation of AI models.

Panel discussion moderated by Girish N. Nadkarni, MD. From left to right: Karandeep Singh, MD, Girish N. Nadkarni, MD, and Vardit Ravitsky, PhD

The symposium concluded with multiple discussions on the interactions between AI and humans—not just as a tool, but by viewing the use of AI in the broader scale. Karandeep Singh, MD, executive director for health innovation at the University of California, San Diego explored various opinions of clincians and patients on the use of AI, while pointing out that the use of AI in healthcare settings should be thoughtfully considered before implantation. Meanwhile, Vardit Ravitsky, PhD, president and CEO of The Hastings Center for Bioethics, discussed the ethics behind AI use as a direct to patient setting, specifically as a patient-used chatbot. In a debate following their respective talks, the two delved deeply into the risks associated with AI use, both on the patient side with chatbots and with scribe technologies used by clinicians and patients. They often agreed on the need for transparency in AI usage, but specific AI applications, like uses of AI robots in the home to combat loneliness in the elderly resulted in disagreements.

The final talk presented by Tanzeem Choudhury, PhD, chief of health innovation at Cornell Tech, brought many previously discussed topics together. Her research explores how AI can be used in treatment of mental health, describing how AI can be used in multiple aspects of mental health therapy from recording physiological symptoms with wearables to using chatbots for various functions. She cautioned that while these tools may eventually be transformative, the current state of AI use in mental health is still growing.

The closing remarks by Alexander Charney, MD, PhD, professor at Icahn School of Medicine at Mount Sinai summarized the event well. He shared that throughout the symposium he imagined what clinicians and researchers from 100 years ago and from 100 years in the future would think about the current state of healthcare and about the challenges being faced now with how to incorporate AI. He said, “We aren’t the first group of human beings to deal with powerful technology and figuring out how we’re going to use it to change society.” He hopes that the people from the past would see that we understand and respect the past and learn from it being rigorous in our research and testing, while the people from the future will look on us with pride at our fearless and tenacity in the face of new technology. He hopes that both groups would see that we “tried to do the right thing.” He ended saying that he does see all of that here along with passion and coming together of everyone at the meeting.

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Decoding Anti–Substance Use Public Service Announcements: Content Analysis Grounded in the Elaboration Likelihood Model and Extended Parallel Process Model

Background: Tobacco, alcohol, and illicit drug use continue to pose substantial public health challenges in China. Although public service announcements (PSAs) are widely used for prevention, little is known about how these messages are constructed or the extent to which they draw on established health communication theories. Objective: This exploratory study aimed to characterize the design features of anti–substance use PSAs in China, assess their use of constructs from the extended parallel process model (EPPM) and the elaboration likelihood model (ELM), and compare patterns across anti–substance use PSAs. Methods: We conducted a content analysis of 89 publicly available anti–substance use PSAs produced in mainland China. Messages were identified via major Chinese video platforms and institutional websites and then screened using predefined eligibility criteria. Variables captured message source, intended audience, framing, substance depiction, cultural appeals, and EPPM and ELM components. Frequencies and proportions were calculated, and tests were used to examine differences by PSA type. To account for multiple comparisons, values were adjusted using the Holm-Bonferroni correction. Results: Most PSAs did not identify a target audience (54/89, 60.7%), and public security departments were the most common sponsors (n=37, 41.2%), while none were sponsored by public health agencies. Theory use was selective: response efficacy (n=63, 70.8%) and perceived severity (n=55, 61.8%) appeared more often than self-efficacy (n=45, 50.6%) and perceived susceptibility (n=34, 38.2%); peripheral cues (n=79, 88.8%) were more common than central route cues (n=16, 18%). Differences across PSA types were observed in sponsorship, message features, and theoretical constructs. After adjustment for multiple comparisons, associations involving sponsoring organizations (public security departments and Chinese media) and perceived susceptibility remained statistically significant (all adjusted =.01). Antidrug PSAs were predominantly associated with public security sponsorship, whereas antialcohol and antitobacco PSAs were more frequently linked to Chinese media sources. Perceived susceptibility cues were more common in antismoking PSAs than in antidrug PSAs, while other differences in framing, substance cues, cultural appeals, and ELM or EPPM constructs were not statistically significant after adjustment. Conclusions: Anti–substance use PSAs in China were characterized by limited audience segmentation and uneven use of theory-based persuasive strategies. Observed differences across alcohol-, tobacco-, and drug-focused messages suggest that PSA design may be shaped not only by partial application of communication theory but also by institutional influences and substance-specific contexts. These findings highlight the need for more context-sensitive and theory-informed approaches to anti–substance use PSA design in China.
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Use of Commercially Available Large Language Models to Generate Information Leaflets on Post–Intensive Care Syndrome: Clinical Utility Assessment

Background: Patients and their families without medical knowledge may find professional health care information difficult to understand. The use of large language models (LLMs) to simplify and translate complex medical content holds promise for improving comprehension while reducing the burden on health care providers tasked with delivering explanations. Objective: This study aims to evaluate the quality of information leaflets generated using commercially available LLMs. Methods: Informational texts on post–intensive care syndrome were generated using 6 different LLMs and 4 prompt designs with varying levels of instructional guidance. Clinical practice guideline documents were uploaded and provided to the models as reference context, reflecting a pragmatic clinical scenario without model tuning or advanced retrieval pipelines. In total, 72 texts were generated (6 models × 4 prompts × 3 outputs). After excluding texts shorter than 500 characters (n=16) and those without explicit mention of post–intensive care syndrome (n=3), 53 texts remained. To enable balanced human evaluation across model-prompt combinations, the longest eligible response from each pair was selected (4 prompts × 4 models; n=16). Following independent expert review by 2 medical specialists, 7 texts were excluded, leaving 9 texts for final analysis. Ten individuals, including health care professionals and nonmedical personnel, assessed the texts using a 10-point Likert scale across multiple quality domains. An LLM-based parallel assessment was also conducted, and scores were compared across models and evaluator groups. Results: In the human evaluation of the selected 9 texts, the generated texts achieved an average score of 6.8 or higher across all evaluation criteria, with no potentially harmful content identified. The text generated by LLaMA 3 70B, using a step-by-step approach combined with text-augmented prompting based on clinical guidelines, received the highest overall score, whereas the lowest-rated text was produced using a simple prompt without text augmentation. Although no consistent trends were observed across LLMs or prompt engineering strategies, text-augmented prompting was generally associated with higher evaluation scores. Ratings differed between professional and nonprofessional evaluators. Given the feasibility-driven screening process and the resulting limited sample size, the findings should be interpreted as exploratory and descriptive rather than definitive estimates of overall model performance. Conclusions: Among the selected texts included in the final human evaluation, informational materials generated using commercially available LLMs were generally rated as acceptable by human evaluators, and none contained harmful content. These findings suggest that LLMs may support the development of patient-facing informational materials under feasibility-constrained conditions, although further validation with larger and more diverse samples is warranted.
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Cross-Dataset Evaluation of an Automated Video-Based Model for Detecting Tardive Dyskinesia Using the Clinician’s Tardive Inventory: Validation Study

<strong>Background:</strong> Tardive dyskinesia (TD) is a common, often underrecognized movement disorder resulting from long-term antipsychotic use, yet its detection in routine mental health care remains inconsistent despite the availability of structured rating scales. <strong>Objective:</strong> This study evaluated the performance of an artificial intelligence–powered, video-based model for detecting abnormal movements associated with TD using the Clinician’s Tardive Inventory (CTI) dataset. We compare automated assessments of videos from the CTI dataset with previously completed clinician-rated Abnormal Involuntary Movement Scale (AIMS) and CTI scores for the dataset’s videos to determine the model’s reliability and the accuracy of its assessment conclusions relative to expert raters. <strong>Methods:</strong> In total, 69 videos with corresponding AIMS and CTI ratings were analyzed using the visual transformer algorithm model called TDtect reported previously. The dataset included single-video assessments per participant, with varied instructions and movement types. The relationship between automated predictions and clinician ratings was assessed using Pearson correlation, and predictive accuracy was evaluated using area under the curve (AUC) metrics. <strong>Results:</strong> The model showed a strong correlation with AIMS total scores (<i>r</i>=0.717) and high diagnostic accuracy (AUC 0.854), which improved further at an optimized threshold (AUC 0.900). Performance differed across anatomical regions, with the tongue, lips, and jaw displaying the highest predictive reliability. Functional CTI components had weaker correlations (<i>r</i>=0.27-0.63), as expected due to the subjective nature of these measures. <strong>Conclusions:</strong> These findings provide preliminary evidence that an artificial intelligence–driven TD detection model can generalize across video protocols, suggesting potential for broader clinical applicability, although further validation is needed. Future refinements and fine-tuning are expected to enhance accuracy, particularly in predicting functional impact.
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Adoption of Digital Mental Health Interventions in National Health Service England, Scotland, and Wales: Freedom of Information Questionnaire Study

<strong>Background:</strong> Digital mental health interventions (DMHIs) have been widely promoted to improve access to mental health care within the UK National Health Service (NHS), particularly following the COVID-19 pandemic. In 2015, a total of 48 technologies were reportedly used in NHS services in England, but over the past decade, substantial changes to regulatory requirements, evidence standards, and procurement processes have reshaped the digital mental health landscape. There is limited clarity regarding which DMHIs are currently being formally procured and funded by NHS mental health services across the United Kingdom. <strong>Objective:</strong> This study aimed to identify and describe the DMHIs currently procured, contracted, or paid for by NHS mental health service providers in England, Scotland, and Wales for adult common mental health problems and to compare current procurement practices with findings reported in 2015. <strong>Methods:</strong> Freedom of Information requests were submitted to all NHS mental health trusts in England and all health boards in Scotland and Wales. Responses were collated and screened to provide an updated and extended record of which technologies are reportedly procured or paid for by services. <strong>Results:</strong> In total, 19 different DMHIs were identified as being procured across mental health service providers for adult common mental health problems at the time of data collection. This demonstrates a substantial reduction in the number of technologies being adopted into practice compared to the 48 reported in England in 2015. The findings reveal several key insights, including that only 2 technologies have remained in use for a decade, and they shed light on the types of technologies being selected and the variations in procurement practices among the 3 national health services. <strong>Conclusions:</strong> Despite the expansion of the digital mental health marketplace, the number of DMHIs formally procured by NHS mental health services has markedly decreased over the past decade. This consolidation may reflect increased selectivity and the adoption of higher-quality products, driven by strengthened regulatory oversight, evidence standards, and national guidance. Although these developments may enhance safety and quality assurance, they also raise important questions about innovation, market sustainability, and equitable access to digital mental health care. Ongoing monitoring of procurement practices is needed to inform policy, service design, and the future development of DMHIs.
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<![CDATA[Learn how common late-life mental illness is, and what new treatments mean for geriatric care.]]>
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Molecular Anchors Help Tumor Therapies Stay Longer on Cancer Cells

For cancer therapies to work, they need to stay in proximity to the target diseased tissues for long enough. To help with that challenge, a group of scientists, led by a team at University of California, San Francisco (UCSF), have developed a drug carrier that physically anchors itself to cancer cell membrane, which helps to improve drug retention and effectiveness. Full details are published in a new ACS Central Science paper titled “A Prodrug Strategy to Conditionally Trap Therapeutic Payloads for Improved Tumor Retention.”

“Retaining drugs within tumors is an often-overlooked dimension of drug development that nevertheless greatly impacts the therapeutic window and outcomes,” said Michael Evans, PhD, a professor in the department of radiology and biomedical imaging at UCSF and a corresponding author on the study. In fact, approaches that deliver cancer therapeutics to tumors but lack dedicated mechanisms to ensure tumor retention often lose efficacy within a few days of drug administration. 

Previously, Evans and others designed drug delivery systems called restricted interaction peptides or RIPs that can deliver diverse therapeutic cargos including cytotoxins and radioisotopes. They work by changing shape when processed by disease-associated enzymes. These allow them to embed in cell membranes, tethering their drug payloads in place, promoting cellular uptake and improving effectiveness. Building on that work, the scientists engineered RIPs to interact with fibroblast activation protein, a serine protease that is prevalent in solid tumors and fibrosis. 

Imaging studies of cancer cell cultures showed that a fluorescently tagged RIP was rapidly taken up by the cells. Then when the scientists attached an anticancer drug, monomethyl auristatin E or MMAE, to the RIP, they found that the drug-peptide combination was as effective in killing cancer cells as the drug alone. Furthermore, when the drug-peptide combination was injected into mice with human cancers, it selectively targeted tumor tissue and was more effective at shrinking tumors than the unmodified drug with fewer side effects. The scientists observed similar results when they attached RIPs to radioactive copper isotopes which are commonly used in nuclear imaging and radiotherapy. 

The scientists expect to initiate Phase I clinical imaging studies of the RIP-radioactive copper isotope pairing in human cancer patients later in 2026 in collaboration with a company that is developing RIPs into therapeutics. 

 

The post Molecular Anchors Help Tumor Therapies Stay Longer on Cancer Cells appeared first on GEN – Genetic Engineering and Biotechnology News.

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A Retina’s Biological Age Can Predict Osteoporosis Risk

Researchers have found that a retina that is aging faster than usual can indicate a lower bone density and an increased risk of fractures due to osteoporosis. Published today in PLOS Digital Health, these findings set the basis for a novel diagnostic method for a condition that remains underdiagnosed due to a lack of accessible screening tools. 

“Osteoporosis is a common condition that weakens bones and raises the risk of fractures, especially in older adults,” write the study authors, who were led by Ching-Yu Cheng, MD, PhD, professor at the Duke-NUS Medical School and director of the Singapore Epidemiology of Eye Diseases (SEED) program. “However, many individuals are not diagnosed until after a fracture occurs, in part because the standard diagnostic test, dual-energy X-ray absorptiometry (DEXA), is not always readily accessible.”

A DEXA scan uses very low levels of X-ray radiation to measure a patient’s bone density—a major indicator of osteoporosis and fracture risk. However, it is a costly procedure requiring specialized equipment, and therefore typically only recommended for high-risk individuals with suspected fractures on X-rays or patients on long-term steroid therapy. This limits early detection in the broader population, with many people being diagnosed with osteoporosis only after experiencing a fracture. 

Worldwide, nearly 20% of the population is affected by osteoporosis. If left untreated, this condition increases the risk of major fractures, which can be life-threatening and represent a large economic burden for healthcare providers. This drives an urgent need for accessible and non-invasive screening methods that can replace traditional DEXA scans.

Cheng’s team investigated whether images from a patient’s retina could help identify those at a higher risk of developing osteoporosis. This idea stemmed from previous research indicating that the retina can reflect the body’s overall biological aging. 

The researchers developed a deep learning algorithm, known as RetiAGE, which calculates the probability of a person being older than 65 years based on images from their retina. They then investigated whether there was an association between RetiAGE results and bone mineral density (BMD) scores, as well as osteoporotic and hip fracture risk scores calculated using the fracture assessment tool (FRAX). 

Retinal images and DEXA measurements were obtained from 1,965 participants in the PopulatION HEalth and Eye Disease PRofilE in Elderly Singaporeans (PIONEER) study. In this patient population, older RetiAGE scores were linked to lower BMD scores and an increased risk of major osteoporotic and hip fractures. 

The ability of RetiAGE to predict the onset of osteoporosis was then evaluated in another 43,938 participants from a prospective UK Biobank cohort with retinal photographs and no osteoporosis at the time of taking these images. Higher RetiAGE scores, indicating accelerated retinal biological aging, were able to predict future osteoporosis onset even when adjusting for common osteoporosis risk factors as well as female-specific risks such as menopause and hormone replacement therapy. 

“These findings suggest that retinal biological aging may reflect broader aging processes related to skeletal health,” state the researchers. “Retinal imaging may therefore provide a simple, non-invasive, and accessible way to support opportunistic screening for osteoporosis risk.”

The post A Retina’s Biological Age Can Predict Osteoporosis Risk appeared first on Inside Precision Medicine.