Tim Smith joined the MJH Life Sciences team as an assistant editor for HCPLive in August 2022. He graduated from UC Berkeley with a degree in political science, working in multimedia journalism as a staff writer prior to joining MJH. In his spare time, he enjoys reading, watching TV, listening to podcasts, and rock climbing. You can contact him at 

One of the fastest-evolving topics in today’s world is artificial intelligence (AI) and its practical application in fields such as medicine.

Discussions of the use of AI tools and of machine learning advancements have been present in the broader cultural conversation for decades.1 Yet when OpenAI launched its ChatGPT model in late 2022, the rapid and widespread adoption of AI into everyday life became, in a very small timeframe, a reality for those with Internet access.

While conversations about AI’s use in the medical field are not new, machine learning’s practical implementation in dermatology specifically is a comparatively new topic. Using clinical and dermatoscopic data and images for AI models to help diagnose such disorders as skin cancer, psoriasis, and atopic dermatitis has become more widely studied.

Yet, despite its apparent potential in medicine and other fields, there is an increasing skepticism of AI among many in the US.

 According to polls, more Americans believe AI will worsen problem-solving skills as opposed to improving them. To many, concerns over its use in fields such as medicine boil down to fears regarding overreliance on such tools and regarding the impersonality of AI.

While many believe AI will be beneficial for clinicians in fields such as dermatology, with benefits to patients looking for melanoma and other skin disorders being touted, the topic of concern over reliance on AI in dermatology is not often highlighted. If an objective view of the costs versus benefits is to be obtained, then an exploration of concerns over machine learning may be necessary.

, AI downsides are explored with 3 experts, all of whom were interviewed about their views and their own research on the topic. The HCPLive team spoke with Vinod Easwaran Nambudiri, MD, MBA, MPH, of Brigham and Women's Hospital, Lia E. Gracey Maniar, MD, PhD, an assistant professor of Medicine at the Department of Internal Medicine of Dell Medical School, and Renata Block, MMS, PA-C, a physician associate (PA) and instructor at Rush University.

This feature explores differing views on AI in dermatology, with a particular focus on its effects on training dermatologists and other clinicians, shedding light on an important, evolving topic in the medical field.

As this technology promises new efficiencies and diagnostic support for clinicians, AI’s rapid integration into the field of dermatology has left clinicians and educators scrambling to keep up. The benefits of its use are becoming widely recognized, however, and there is no denying the potential of AI in dermatologic care.

Renata Block, MMS, PA-C 
Credit: Rush University

“I think AI, or artificial intelligence, is such an important topic because we have to understand what it is and what it does,” Block explained.

 “I think if you become savvy with understanding the process of how it's developed and what it's intended to do, then you're going to know, ‘This is going to not replace me, but it's going to help augment my decision-making process in the clinic.’”

Generative AI and advanced data synthesis techniques were described in 1 study as enabling the formation of high-quality, representative, and diverse training data for AI models in dermatology.

 The benefits of large foundational models’ reduction of the necessity of massive datasets was also cited. Improvements in diagnostic accuracy are also among the most important long-term goals with AI’s implementation.

In a previous feature by HCPLive, Harald Kittler, MD, a professor of dermatology for the Medical University of Vienna, had discussed his team’s findings published in Nature Medicine on the accuracy of AI results which had been incorporated into human decision-making criteria.

 Kittler noted his team’s data suggested a 12% improvement in accurate skin cancer diagnoses made by dermatologists.

Additional research has come out demonstrating a variety of benefits. In a study, the improvements in the quality of skin disease photographs, resulting from an AI decision support tool’s use, along with a machine learning algorithm, were highlighted.

 A systematic review led investigators to acknowledge AI’s accuracy in identifying and classifying pigmented lesions in patients with skin of color.

However, despite the wide variety of positive data acknowledged by AI and machine learning proponents, there remain cracks in the facade.

Clinician Views on AI Accuracy and Helpfulness

Dermatologists’ views on such technological advancements have also not yet been widely explored. In 1 study, investigators surveyed dermatologists and found 77.3% of those asked agreed that AI would be a positive influence on dermatology.

 Additionally, 79.8% of survey respondents agreed that AI should be a part of medical training. AI in medical training was explored in a JAMA opinion piece authored in part by Nambudiri and Gracey Maniar.

Vinod Nambudiri, MD, MBA 
Credit: Brigham and Women's Hospital

“I'm cautiously optimistic that this could help in certain aspects for dermatology residency training,” Nambudiri said in an interview. “I think certainly there's always something that we could improve upon if there is an opportunity to really tailor curriculum, since all residents are not the same, some may struggle in certain areas, and to be able to identify and potentially tailor curricula to help support that resident.”

Gracey Maniar echoed Nambudiri’s cautious optimism on the technology’s implementation.

“I'm cautiously optimistic that this could help in certain aspects for dermatology residency training,” Gracey Maniar explained. “I think, certainly, there's always something that we could improve upon. If there is an opportunity to really tailor the curriculum, since not all residents are the same, and some may struggle in certain areas.”

 at the Society of Dermatology Physician Associates (SDPA) Fall Conference in Texas, Renata Block was slightly more optimistic in her discussion of AI’s use in dermatology.

 She echoed the views of many clinicians who view the technology’s potential relatively optimistically, describing it as essentially another tool helping to augment work.

“I think with anything new, there's always hesitancy because we don't understand it,” Block said. “That's why I thought it was such an important topic to discuss, because I want my colleagues to understand AI, understand how to approach AI, and then feel comfortable utilizing it and honing in on the importance that it's not there to replace us. It's there to augment our practice. I throw out that I think artificial is kind of the wrong terminology to use. It's really augmented intelligence.”

Biased Datasets, Hallucinations, and Patient Security

The downsides of AI technology’s use in medicine and in dermatology are becoming increasingly acknowledged. For example, despite the aforementioned systematic review’s positive findings, the study’s investigators also highlighted significant discrepancies in the number of models developed in populations with skin of color. This was particularly true of patients with Fitzpatrick type IV-VI.

“In our current medical education paradigm or platform, we know that for many years, the representation of a diversity of skin tones in dermatology educational materials has been lacking,” Nambudiri noted. “...I think it's incumbent upon us as physicians, as dermatologists, as the clinicians that take the best care of skin, hair, nails, etc, for us to be representing a wide swath of the clinical spectrum. Every skin tone, every hair type, etc., really needs to be built into these learning data sets such that the AI platforms or tools that we're using can help us in the care of all of our patients.”

AI does not know what it does not know, Nambudiri explained, and dermatology clinicians must build accessible and wide-ranging resources to enhance diagnostic abilities. Issues related to diversity in clinical research are 1 of many problems in this space, with many concerned about the accuracy of image detection and classification without diverse datasets.

Lia E. Gracey Maniar, MD, PhD
Credit: Dell Medical School

“I think it's a really important area to highlight, because the hard part is, is with what we want our tools to be representative of the patients that we see, and right now, they're not,” Gracey Maniar expressed in her interview. “And some of the tools aren't even disclosing how they're trained, either proprietary data sets, or they don't break down by skin tone how their data was trained. So, it's a little bit of a black box, and it's pretty opaque for us to even be able to evaluate how their tools will perform for all populations.”

Additionally, other limitations have been highlighted in recent studies. These include image quality variations, liability concerns, interpretability concerns, acceptance among patients, liability, multidisciplinary coordination challenges, data bias risks, and the potential loss of clinical skills.

In her presentation on the topic of AI featured at the 2025 SDPA Fall Conference, Block pointed out another key issue observed among clinicians and everyday users of models such as ChatGPT: AI ‘hallucinations.’

“We are training these models to detect cancer by using images or predicting outcomes based on historical data, but this is very expensive and very time-consuming,” Block said. “This is a great way to integrate AI into any tool, really, because there's also unsupervised, and that's where we can get in a lot of trouble. Because the algorithm finds patterns on its own. There are no predicted outcomes. And applications include grouping patients with similar symptoms or genetic markers. However, this is very difficult to validate, which can be a huge issue.”

Many machine learning models face the issue of hallucinations. This phrase refers to models’ generation of content not based on real or existing data but rather produced by a model's extrapolation or creative interpretation of training data.

 This has also been experienced among ChatGPT users who, when feeding questions into the machine learning model, occasionally receive incorrect answers.

“[There's] a lot of bias that can happen, and even hallucinations; they're going to start making things up,” Block said. “That is why it's important that you read what the AI does to make sure that it's accurate. But the other issue is transparency and, of course, privacy and security, which can be easily compromised.”

In her interview on the topic, Gracey Maniar stressed her view on the need for a healthy amount of skepticism regarding this technology and its use in dermatology. She expressed the continued value of textbooks, primary sources, and verification of information ascertained via large language models (LLMs) or generative AI tools like ChatGPT.

“Part of that is getting a foundation on what questions we should be asking,” Gracey Maniar explained. “Understanding just because you're typing in a question, for instance, into Gemini, that what you're getting spit back may not be the real truth, and that you need to actually verify with primary sources.”

The Erosion of Core Clinical Skills and Human Contact

One of the most central concerns related to AI’s widespread implementation in medicine is the erosion of skills among regular users. There are concerns over machine learning’s impact on the ability to learn and navigate the complexities of medical training. This topic was explored in Gracey Maniar and Nambudiri’s JAMA paper, wherein challenges in dermatology residency were highlighted.

Dermatology residents’ scholarly work is already being affected by the use of LLMs to provide background research, to help write drafts of manuscripts and abstracts, and to develop slides for educational presentations.13 Such models have also been implemented to generate simulated questions and prepare medical trainees for their standardized examinations.

“Documentation pervades our lives every day in healthcare, and I think it is just as big a source of burnout,” Gracey Maniar said. “...I think it could actually help with the documentation burden and the production pressure that a lot of us feel having to get through so many notes. On the other hand, one of the items we brought up for concern was that we still need residents to learn how to write good notes.”

Gracey Maniar and Nambudiri both stressed the importance of the thought process and learning resulting from taking strong notes during residency training.

“I think part of it is not just getting words on paper, on what we did that day, but it's also that writing can help you think through a differential diagnosis,” Gracey Maniar added. “There's a little bit of an art of medicine, and how you're communicating with other specialists, with the primary care physician who may be reading your note. So, we want to make sure that our residents still retain or learn and develop those skills.”

In his interview, Nambudiri noted the importance of dermatology residents requiring the ability to override AI and other technologies when they are incorrect. He pointed out an example of overreliance on technology and its impact.

“An example that happened to me recently is I had a patient for whom I ordered a radiology test, a CAT scan, and the interpretation was generated through the assistance of dictation software,” Nambudiri explained. “It’s a very first generation of sort of speech-to-text processing tool…In one part of the report, it made a statement appeared to be inconsistent with something later on in the final overall impression. At one point, it said something had changed. At the end, it said it was unchanged. Those are two completely contradictory things that cannot be true both at once.”

Nambudiri described having had to message the radiologist who read the study. This, he expressed, is a classic example of overreliance upon technology in medicine. Additionally, both clinicians pointed out the importance of the human element to medicine and the impersonality of AI tools and telemedicine practices.

“A lot of the time, what we hear from patients and in the literature is that it's people just want to have that empathy factor, and I don't think that is easily replaceable,” Gracey Maniar said. “For instance, we are trying to get a survey and a qualitative study submitted where we asked a federally qualified health center to get people's attitudes towards AI and healthcare, both from patients at a safety net clinic, the physicians, the non-physician providers, and the staff who serve them. One of the big concerns was losing that human factor.

The connection between the patient and the clinician, Gracey Maniar highlighted, is something concerning many patients with fears regarding technology’s increasing use in medicine.

“I think one of the things that seemed to be at risk was more of the electronic communications right now,” Gracey Maniar explained. “At most institutions, it's still a human who is responding to those granted. At some institutions, they're piloting or actively using some of the chatbots or the automated suggested responses, for instance, to portal messages. Since a lot of physicians are getting inundated with the portal messages that are coming, I think the primary physician input into some of those portal messages could start to be overshadowed a bit.”

Addressing Concerns Over AI in the Future

Despite many of these potential downsides to AI’s implementation, all 3 experts noted their own cautious optimism with regard to many of the technological innovations impacting medicine and dermatology in particular. Looking ahead, each of the speakers noted the need for these tools to have regulation and to be more understood, rather than feared.

During his discussion, Nambudiri still described himself as a proponent of AI utilization for ambient documentation. He highlighted his own use of an AI-based scribe for years, expressing the importance of always considering ways to tweak one’s efficiency with documentation using AI-based technologies.

“One of the things that our own institution is thinking through is how to integrate this into residency training,” Nambudiri said. “One of the models that has become quite popular is sort of a graduated introduction of these AI tools for documentation, meaning that they may not be available, say, day one of your residency training, perhaps, but over the course of a few months. That's really meant to allow this idea of understanding and knowing how a clinical note can be most effectively or impactfully structured as an individual, before you're relying on a non-human tool to be doing it for you.”

Nambudiri stressed what he describes as a tremendous opportunity to learn using such tools. Similar to Block, Nambudiri noted the potential value in dermatology training of using certain tools to augment rather than replace work.

“Actually doing a note yourself, if you have the AI recording also available in parallel, and seeing what would come out differently [is helpful], or using the AI tool to generate a draft of a note, and then you, as a physician, can go in and enter it,” Nambudiri said. “It’s actually a teachable moment for us as faculty to be thinking through what is actually critical, and noting what we probably need to be doing more of as physicians.”

In her session at SDPA, Block highlighted the importance of patient safety with regard to AI tools and their use in the medical field. Nevertheless, Block also expressed her belief in the potential for AI to augment work done by clinicians in a manner that leads to positive outcomes.

“...We're taking the human aspect of it and putting it into machine learning,” Block said in her interview. “We're marrying those two things together to give us an objective view of what we're trying to do. So, it's not making that decision for us; it's augmenting our decision. What I want people to walk away with when they hear my lecture is really getting a foundational understanding of how AI can be integrated into our workflow and how they could utilize it to provide the best patient care.”

Addressing some of the more common downsides of AI’s implementation in clinical and educational settings in dermatology, Block highlighted the importance of clinicians understanding regulatory processes and ensuring they are relying on a tool that is US Food and Drug Administration (FDA)-approved, has gone through rigorous research, and has the necessary guardrails up for patient safety.

Gracey Maniar also tempered her statements on concerns over AI with several positive views related to these technologies and their application in dermatology education.

“I also think [we’re] seeing efficiencies help with day-to-day doldrums and clinic operations that can weigh us down,” Gracey Maniar said. “I think having AI help generate patient education materials, since a lot of this should be geared somewhere between a fourth-grade level of education, that's something that we are not necessarily great at as physicians. I think that is something that could help move that along as well, too.”

However, Gracey Maniar noted the unknowns in this area, highlighting the lack of longer-term data and the lack of understanding over ways diagnostic apps can be helpful or hurtful.

“I'm a little cautiously pessimistic, I would say, as far as scholarly work goes,” Gracey Maniar said. “I'd really like to see our trainees using their own creativity and see the problems that we face day-to-day in clinic, and be able to generate research questions and research projects on their own to address the problems that our field faces. It's so easy to plug that into ChatGPT and say, ‘What should we be studying?’ or, ‘Write an abstract for me.’ I would love to still see that creativity and intentional thought work go into those types of areas by our trainees.”

AI and machine learning models have undoubtedly shifted the paradigm dramatically in the medical field, and dermatology is no exception. As technologies evolve and new methods of problem-solving are developed, the potential for growth and for diminished creativity both remain at the center of the world’s attention.

Hirani R, Noruzi K, Etienne M, et al. Artificial Intelligence and Healthcare: A Journey through History, Present Innovations, and Future Possibilities. Life (Basel). 2024 Apr 26;14(5):557. doi: 10.3390/life14050557. PMID: 38792579; PMCID: PMC11122160.

Balch D. Artificial Intelligence: The Rise of ChatGPT and Its Implications. Faculty Focus. August 25, 2023. Accessed December 5, 2025. 

https://www.facultyfocus.com/articles/teaching-with-technology-articles/artificial-intelligence-the-rise-of-chatgpt-and-its-implications/

Stein, JP., Messingschlager, T., Gnambs, T. et al. Attitudes towards AI: measurement and associations with personality. Sci Rep 14, 2909 (2024). 

https://doi.org/10.1038/s41598-024-53335-2

Kennedy B, Yam E, Fuentes J, et al. How Americans View AI and Its Impact on People and Society. Pew Research Center. September 17, 2025. Accessed December 5, 2025. 

https://www.pewresearch.org/science/2025/09/17/how-americans-view-ai-and-its-impact-on-people-and-society/

Bohr A, Memarzadeh K. The rise of artificial intelligence in healthcare applications. Artificial Intelligence in Healthcare. 2020:25–60. doi: 10.1016/B978-0-12-818438-7.00002-2. Epub 2020 Jun 26. PMCID: PMC7325854.

De A, Sarda A, Gupta S, Das S. Use of Artificial Intelligence in Dermatology. Indian J Dermatol. 2020 Sep-Oct;65(5):352-357. doi: 10.4103/ijd.IJD_418_20. PMID: 33165383; PMCID: PMC7640800.

Block R. How to Better Use AI and Machine Learning in Dermatology, With Renata Block, MMS, PA-C. HCPLive. November 8, 2025. Accessed December 5, 2025. 

https://www.hcplive.com/view/how-to-better-use-ai-machine-learning-dermatology-renata-block-mms-pa-c

Sengupta D. Artificial Intelligence in Diagnostic Dermatology: Challenges and the Way Forward. Indian Dermatol Online J. 2023 Oct 17;14(6):782-787. doi: 10.4103/idoj.idoj_462_23. PMID: 38099026; PMCID: PMC10718130.

Barata C, Rotemberg V, Kittler H, et al. A reinforcement learning model for AI-based decision support in skin cancer. Nat Med. 2023 Aug;29(8):1941-1946. doi: 10.1038/s41591-023-02475-5. Epub 2023 Jul 27. PMID: 37501017; PMCID: PMC10427421.

Vodrahalli K, Ko J, Daneshjou R, et al. Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality. JAMA Dermatol. 2023 May 1;159(5):496-503. doi: 10.1001/jamadermatol.2023.0091. PMID: 36920380; PMCID: PMC10018405.

Liu Y, Primiero CA, Betz-Stablein B, et al. Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review. Dermatology. 2023;239(4):499-513. doi: 10.1159/000530225. Epub 2023 Mar 21. PMID: 36944317; PMCID: PMC10407827.

Polesie S, Gillstedt M, Kittler H, et al. Attitudes towards artificial intelligence within dermatology: an international online survey. Br J Dermatol. 2020;183(1):159-161. doi:10.1111/bjd.18875.

Gracey LE, Nambudiri VE, Maderal AD, et al. Challenges and Opportunities for AI in Dermatology Residency. JAMA Dermatol. 2025;161(11):1103–1104. doi:10.1001/jamadermatol.2025.3366.

Gholizadeh, N., Rokni, G.R. and Babaei, M. (2024), Advantages and Disadvantages of Using AI in Dermatology. Dermatological Reviews, 5: e248. https://doi.org/10.1002/der2.248.

Fliorent R, Fardman B, Heath C, et al. Artificial intelligence in dermatology: advancements and challenges in skin of color. Int J Dermatol. 2024 Apr;63(4):455-461. doi: 10.1111/ijd.17076. Epub 2024 Mar 6. PMID: 38444331.

Hatem R, Simmons B, Thornton JE. A Call to Address AI "Hallucinations" and How Healthcare Professionals Can Mitigate Their Risks. Cureus. 2023 Sep 5;15(9):e44720. doi: 10.7759/cureus.44720. PMID: 37809168; PMCID: PMC10552880.

Articles

Artificial intelligence (AI) is rapidly integrating into dermatology, offering potential benefits such as improved diagnostic accuracy and efficiency. However, concerns persist regarding overreliance, biased datasets, and the erosion of clinical skills. Experts express cautious optimism, emphasizing AI's role in augmenting rather than replacing human decision-making. Challenges include ensuring diverse training datasets, addressing AI "hallucinations," and maintaining patient security. The need for regulation and understanding of AI tools is crucial. Despite potential downsides, AI's integration into dermatology holds promise for enhancing clinical practice and education.

Explore the evolving role of AI in dermatology, balancing its benefits with concerns over accuracy, bias, and the human touch in patient care.

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Atopic Dermatitis condition center page, audiences can find a variety of coverage mediums for medical news and updates, including articles, video interviews, podcasts, and breaking news on dermatitis research, treatment, and drug development.

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This Year In Medicine

AI in Dermatology: Discussing Downsides to the Adoption of Artificial Intelligence 

Abigail Brooks is an assistant managing editor for 

She joined MJH Life Sciences in August 2023 shortly after graduating from Monmouth University where she earned her BA in Communication with a concentration in Public Relations/Journalism and later an MA in Interactive Digital Media. She enjoys traveling, running, and reading books. She can be reached at abrooks@mjhlifesciences.com.

The US Food and Drug Administration has approved etuvetidigene autotemcel (Waskyra), the first cell-based gene therapy for the treatment of Wiskott-Aldrich syndrome (WAS).

As described in a December 9, 2025, release from the Agency, etuvetidigene autotemcel is indicated for pediatric patients ≥ 6 months of age and adults with WAS who have a mutation in the 

 gene and for whom hematopoietic stem cell transplantation (HSCT) is appropriate and no suitable human leukocyte antigen (HLA)-matched related stem cell donor is available.

“Today’s approval is a transformative milestone for patients with Wiskott-Aldrich syndrome, offering the first FDA-approved gene therapy that uses the patient's own genetically corrected hematopoietic stem cells to treat the disease,” Vinay Prasad, MD, MPH, Chief Medical and Scientific Officer and Director of the FDA’s Center for Biologics Evaluation and Research, said in a statement. “The FDA continues to exercise flexibility in the regulatory approach for rare diseases by considering all available data sources, including as appropriate data from expanded access programs, to facilitate the advancement of life-changing treatments while ensuring scientific requirements are satisfied.”

WAS is a rare, X-linked, life-threatening inborn error of immunity and platelet disorder caused by WAS protein (WASP)-encoding gene mutations. It is characterized by thrombocytopenia, bleeding events, recurrent and severe infections, eczema, and increased risk of immune dysregulation and malignancy. While allogeneic HSCT can be curative, it is still hampered by limited donor availability and potential complications.

Etuvetidigene autotemcel consists of the patient’s own hematopoietic stem cells (HSCs), which have been genetically modified to include functional copies of the WAS gene. Following reduced-intensity conditioning, the gene-corrected cells are infused intravenously to restore blood cell production. Etuvetidigene autotemcel restores functional WAS protein expression in affected cells, addressing the underlying cause of the disease.

Of note, during the review of etuvetidigene autotemcel, the FDA exercised appropriate regulatory flexibility across rare disease considerations, clinical trial design, mechanism of action, and chemistry, manufacturing and controls (CMC), enabling approval and timely access to the product while carefully balancing preapproval data requirements with post-market commitments. The FDA permitted the use of relevant manufacturing and quality data submitted to this BLA from a similar, approved product that was justified to be adequately representative of etuvetidigene autotemcel for these purposes.

Its safety and effectiveness was assessed in 2 open-label, single-arm, multinational clinical studies and an expanded access program totaling 27 patients with severe WAS, which demonstrate substantial and sustained clinical benefit for patients with severe WAS, with significant reductions in the primary disease manifestations that drive morbidity and mortality.

The rate of severe infections decreased by 93% in the 6 to 18 months post-treatment period compared to the rate 12 months before treatment. Similarly, moderate and severe bleeding events were reduced by 60% in the first 12 months post-treatment compared to the year prior to treatment. Of note, most patients did not report moderate to severe bleeding after 4 years post treatment.

As described by the FDA, the most common side effects associated with treatment include rash, respiratory tract infection, febrile neutropenia, catheter related infection, vomiting, diarrhea, liver injury, and petechiae.

“Today’s approval addresses the urgent need in the WAS community, where patients have described living 'a life of terrifying worry and fear' without any approved therapies available,” Vijay Kumar, MD, Acting Director of the CBER Office of Therapeutic Products, said in a statement. “This action marks significant progress in the development of much-needed treatment options for patients affected by this debilitating and life-threatening disease, enabling them to engage in everyday activities such as going to school or participating in sports.”

FDA. FDA Approves First Gene Therapy Treatment for Wiskott-Aldrich Syndrome. December 9, 20225. Accessed December 9, 2025. 

https://www.fda.gov/news-events/press-announcements/fda-approves-first-gene-therapy-treatment-wiskott-aldrich-syndrome

Ferrua F, Cenciarelli S, Giannelli S, et al. Lentiviral hematopoietic stem and progenitor cell gene therapy with Etuvetidigene autotemcel for the treatment of Wiskott-Aldrich Syndrome. 

The FDA has approved etuvetidigene autotemcel (Waskyra), the first cell-based gene therapy for Wiskott-Aldrich syndrome (WAS), targeting patients with WAS gene mutations lacking suitable HLA-matched donors. This therapy uses genetically corrected hematopoietic stem cells to restore blood cell production and functional WAS protein expression. Clinical studies showed significant reductions in severe infections and bleeding events post-treatment. The FDA's regulatory flexibility facilitated approval, balancing preapproval data with post-market commitments. Common side effects include rash, infections, and liver injury, addressing a critical need for WAS patients.

The approval makes etuvetidigene autotemcel (Waskyra) the first cell-based gene therapy for the treatment of Wiskott-Aldrich syndrome.

Hematology

FDA News

FDA Approves Etuvetidigene Autotemcel (Waskyra) For Wiskott-Aldrich Syndrome

Tracy George, MD, is a professor at the University of Utah and vice chair of clinical affairs at the University of New Mexico.

Bezuclastinib Exhibits Superior Disease Biomarker Reduction in NonAdvSM, With Tracy George, MD

A primary analysis of the phase 3 SUMMIT trial has shown that patients with 

non-advanced systemic mastocytosis (NonAdvSM)

 who received treatment with bezuclastinib exhibited improvements in symptom burden and biomarkers of disease compared to placebo.

67th American Society of Hematology (ASH) Annual Meeting and Exposition

 by Tracy George, MD, professor at the University of Utah and vice chair of clinical affairs at the University of New Mexico, these data support the use of bezuclastinib to reduce NonAdvSM disease burden, thereby taking advantage of its possibly disease-modifying impact.

“This is really exciting work, because not only do patients with non-advanced systemic mastocytosis respond clinically to bezuclastinib, but we are actually seeing disease modification in the bone marrow,” George told 

 in an exclusive interview. “Bone marrow mast cells are removed from the bone marrow, change their shape to become normal, and they normalize their immunophenotype. And so, you go from bone marrows which are hypercellular with dense mast cell aggregates to bone marrows which are normally cellular and essentially look normal after treatment with bezuclastinib.”

George’s presentation was based on the registration-directed part 2 of the SUMMIT trial. Investigators included patients with NonAdvSM who had inadequate symptom control despite best supportive care (BSC) mediations. The primary endpoint was 24-week mean change from baseline in Mastocytosis Symptom Severity Daily Diary (MS2D2) total symptom score (TSS), which is a patient-reported outcome measure of symptom severity in NonAdvSM.

A total of 179 patients were included in part 2 of SUMMIT; of these, 119 received bezuclastinib once daily plus BSC, while 60 received placebo plus BSC. The median age was 51 (range, 23-78) years, and mean baseline MS2D2 TSS was 55.6 (standard deviation [SD], 19.8). Additionally, 82% of patients had indolent systemic mastocytosis, 11% had bone marrow mastocytosis, and 7% had smoldering systemic mastocytosis.

At week 24, investigators saw significantly greater symptom improvement among patients treated with bezuclastinib compared to those receiving placebo (least squares mean MS2D2 TSS change: -24.3; 95% CI, -27.6 to -21.1 vs. -15.4; 95% CI, -19.6 to -11.2; 

 = .0001). A ≥50% reduction was also achieved in serum tryptase in 87.4% of bezuclastinib-treated patients versus 0% on placebo (

Investigators also saw more patients receiving bezuclastinib achieved ≥50% reductions in KIT D816V variant allele frequency, serum tryptase, bone marrow MCs (

 = .01), and ≥30% reduction in MS2D2 TSS (

Additionally, most treatment-emergent adverse events were low grade and reversible. The most common in any treatment group were hair color changes (69.5% in bezuclastinib vs 5% in placebo), altered taste (23.7% vs 0%), nausea (22% vs 13.3%), increased ALT/AST (22% vs 6.6%), headache (17.8% vs 11.7%), alopecia (11.9% vs 3.3%), and increased ALP (10.2% vs 3.3%).

Ultimately, George and colleagues determined that bezuclastinib exhibited statistically and clinically significant improvements in both symptom burden and disease biomarkers compared to placebo in patients with NonAdvSM.

“What’s exciting is that these are the results from this pivotal SUMMIT trial, which was treating patients with non-advanced systemic mastocytosis,” George said. “What I’m really looking forward to is our results from the APEX trial, which is treating patients with advanced systemic mastocytosis with bezuclastinib. I expect it’s going to be exciting; I’m hoping to see similar results from that trial.”

Editor's Note: George reports disclosures with Incyte, Celgene/BMS, Beckman Coulter, Cogent Biosciences, and Blueprint Medicines.

George T, Boggs N, Patel J, et al. The effect of bezuclastinib on the pathobiology of mastocytosis: Changes in BM mast cells, tryptase, and KIT p.D816V variant allele frequency from the pivotal SUMMIT trial. Abstract presented at the 67th American Society of Hematology Annual Meeting. Orlando, FL. December 6-9, 2025.

Rein L, Boggs N, Bose P, et al. Efficacy and safety results from the primary analysis of the Pivotal Summit Trial: Bezuclastinib in adults with non-advanced systemic mastocytosis. Blood. 2025;146(Supplement 1):80-80. 

Videos

George highlights bezuclastinib’s effect on bone marrow systemic mastocytosis, emphasizing the drug’s potential in reducing symptom burden.

Conference

 conference coverage page features articles, videos, and expert-led live coverage from major medical meetings throughout the year.

There is a potential causal link between 

 and depression, according to new findings, highlighting the value of mental health screening among patients with the skin disease.

Such findings on the association between mental health and psoriasis resulted from a recently published analysis by investigators such as Xixing Ma, MD, from the Second Hospital of Hebei Medical University (HBMU). In this analysis, Ma and coauthors pointed to prior speculation suggesting inflammation may function as a pathophysiological association between psoriasis and depression.

Most current studies on the topic, however, were described as observational research-based. They also highlighted such studies’ limited sample sizes and consequent susceptibility to potential confounding variables or reverse causality.

“Our study represents the first comprehensive investigation using MR methodology to establish the causal relationship between PSO and depression, addressing a critical gap in the current literature and potentially informing more targeted therapeutic strategies,” Ma and colleagues wrote.

 “Therefore, our study aims to explore the causal relationship between PSO and depression using a two-sample MR approach.”

The investigative team set out to evaluate psoriasis's potential causal connection with depression, applying a 2-sample Mendelian randomization (MR) framework to explore the subject. Their study was conducted in accordance with the 3 fundamental MR assumptions, the first of which being the instrumental genetic variants must show a significant link with the exposure (psoriasis). Additionally, Ma et al noted they must be independent of confounding variables that could affect both exposure and outcome, and they must influence the mental health outcome exclusively via exposure rather than through alternative biological pathways, thereby avoiding horizontal pleiotropy.

To assess the genetic correlation between psoriasis and depression, Ma and coauthors analyzed Z-score–based summary statistics from several genome-wide association studies (GWAS). These studies were done in European cohorts. Only GWAS datasets that met the investigative team's predefined criteria were highlighted, including consistent ancestral backgrounds, larger sample numbers, and a lack of overlap between the psoriasis and depression samples. These datasets were included to minimize bias and strengthen any causal inference.

Ma and colleagues utilized genetic variants as instrumental variables within their MR design, with the inverse variance weighted (IVW) method functioning as the principal analytical approach. Additionally, the investigative team supplemented this with MR Steiger testing for the purposes of confirming causality's direction. It was also supplemented with colocalization analyses as the team sought to explore whether both traits share any underlying causal variants.

These data demonstrated a link between genetic liability to psoriasis and an elevated depression risk (OR 1.35, 95% CI 1.14–1.59, 

 = .004). This association identified by Ma et al was shown to have been consistent across several complementary MR approaches. These approaches included MR-Egger and MR-PRESSO. In findings resultinf from directionality testing via MR Steiger, a causal influence of psoriasis on depression was identified. Additionally, the investigators' colocalization analysis pointed to rs12189871 as a shared causal locus implicated in both of the phenotypes.

Growing interest in any links between psoriasis and mental health outcomes like depression has prompted a variety of previous investigations, and Ma and colleagues' findings contribute additional evidence supporting a causal relationship between the inflammatory skin disease and depression. While the underlying biological mechanisms were still described by the investigators as not yet elucidated, previous data has indicated that genetic variation involving the HLA-C region may heighten a patient's susceptibility to depressive symptoms.

“Future research should focus on elucidating the underlying biological mechanisms, particularly the role of shared inflammatory pathways and specific genetic loci such as those within the HLA region identified in our study,” they concluded.

Guo Bingshen, Bai Yanzhi, Ma Xixing, et al. Genetic Susceptibility to Psoriasis Increases Risk of Depression: Evidence From a Mendelian Randomization Study, International Journal of Clinical Practice, 2025, 9238266, 8 pages, 2025. 

Bell KA, Balogh EA, Feldman SR. An update on the impact of depression on the treatment of psoriasis. Expert Opin Pharmacother. 2021 Apr;22(6):695-703. 

Recent findings suggest a potential causal link between psoriasis and depression, emphasizing the importance of mental health screening in psoriasis patients. A study led by Xixing Ma utilized a two-sample Mendelian randomization (MR) approach to explore this relationship, addressing limitations of previous observational studies. The research identified a genetic correlation between psoriasis and increased depression risk, with rs12189871 as a shared causal locus. While the biological mechanisms remain unclear, the study highlights the need for further research into shared inflammatory pathways and genetic loci, particularly within the HLA region.

Potential Causal Relationship Identified Between Depression, Psoriasis

VAYHIT3: Ianalumab Reduces Bleeding, Depletes B Cells in Immune Thrombocytopenia, With Philip Choi, MBBS, PhD

Secondary analyses of the phase 2 VAYHIT3 trial support the efficacy profile of ianalumab, including rapid and profound B-cell depletion and reductions in bleeding events in heavily pretreated adults with 

 by Philip Choi, MBBS, PhD, an associate professor at the Australian National University College of Science and Medicine.

“VEHIT-3 is very much a proof-of-mechanism study. It wasn’t designed or powered to show superiority over other therapies. It was conducted in patients with severe, refractory ITP. These were predominantly older patients compared with typical first- or second-line ITP studies,” Choi said in an interview with 

. “Despite that, and despite heavy prior immunosuppression, a meaningful proportion of patients derived significant benefit with very little toxicity. That’s the key message: BAFF receptor inhibitors may have an important role even for patients with very severe refractory ITP.”

Ianalumab, a fully human IgG1 monoclonal antibody, is being investigated for its potential to treat various B cell-driven autoimmune diseases. The drug binds the BAFF receptor and depletes B cells through antibody-dependent cellular cytotoxicity while concurrently blocking BAFF.

Primary analysis from VAYHIT3, a phase 2 open-label, single-arm study, was released earlier this year with a primary endpoint, confirmed platelet response (ConfR). The current analysis focuses on secondary outcomes, including the depth and duration of B-cell depletion, immunoglobulin trends, bleeding events, and safety. Responses assessed in the study included stable response (SR) and complete response (CR).

The study enrolled adults with primary ITP and a platelet count <30 G/L, previously treated with at least a corticosteroid (CS) and a thrombopoietin receptor agonist.

Upon analysis, Choi and colleagues observed rapid and potent B-cell depletion, with a median percent change from baseline of –97.6% before the 2nd infusion at week 5 and –99.9% before the 4th infusion at week 13. By the data cutoff, the median time to B-cell recovery was not estimable for investigators.

Subgroup analysis of ConfR rates at week 25 suggested benefit across all subgroups, including 40% for patients with 2 to 3 prior treatments, 67% for 4 to 5, and 33% for ≥6 prior therapies; 42% for patients aged 18 to <65 years and 50% for those ≥65; and 48% of female patients versus 40% of male patients. The proportion of patients achieving CR increased from 10% at Week 9 to 22% at week 25.

Investigators reported substantial reductions in both the number and severity of bleeding events. Post-baseline, all but 1 patient maintained IgG values ≥5 g/L. At study entry, 59% of patients reported bleeding events, with 44% grade 1. After the second infusion, bleeding events decreased substantially, and by week 25, 22% of evaluable patients reported any-grade bleeding, with 16% grade 1. No new safety signals emerged during longer follow-up.

“We look forward to biomarker analyses over the next 12–18 months to help identify which patients are most likely to benefit from BAFF receptor inhibition,” Choi said. “Another open question is whether B-cell reconstitution will eventually lead to loss of response in some patients. Long-term data are still needed.”

Editor’s Note: Choi reports relevant disclosures with Janssen Cilag, SOBI, Novartis, Amgen, and Sanofi.

Choi P, Bradbury C, Gonzalez-Lopez T, et al. Secondary analysis results from VAYHIT3, a phase 2 study of ianalumab in patients with primary immune thrombocytopenia previously treated with at least two lines of therapy. Abstract presented at the 67th American Society of Hematology Annual Meeting. Orlando, FL. December 6-9, 2025.

Novartis ianalumab significantly extends disease control in patients with immune thrombocytopenia with only four once-monthly doses. Novartis. Published 2025. Accessed December 9, 2025. 

https://www.novartis.com/news/media-releases/novartis-ianalumab-significantly-extends-disease-control-patients-immune-thrombocytopenia-only-four-once-monthly-doses

Data from VAYHIT3 presented at ASH 2025 suggest efficacy and safety of ianalumab in patients with primary immune thrombocytopenia. 


Chelsie Derman is an associate editor for 

who covers psychiatry, sleep medicine, geriatrics, migraine, and allergy. She joined MJH Life Sciences in September 2023 after graduating from The College of New Jersey with a bachelor’s degree in journalism and professional writing. In her free time, she enjoys creative writing and reading.

Study shows house dust mite SLIT therapy is safe in children with allergic asthma, though COVID-19–related drops in exacerbations impacted the primary endpoint.

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House Dust Mite SLIT Tablet Well Tolerated in Children With Allergic Asthma

Claudia Morris, MD, is a professor of pediatrics and emergency medicine at Emory University and a pediatric emergency medicine physician at Children's Health Care of Atlanta.

Morris discusses this latest trial in a long list of phase 3 studies which failed to evaluate arginine therapy’s value in vaso-occlusive pain episodes in SCD.

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