The most striking technical contribution comes from Japan, where Nageishi and Kano tested an AI-based throat camera for influenza screening under a framework they call Biosampling Alternative Diagnostic Imaging, or BADI. The idea is deceptively simple: instead of asking patients to travel to a clinic for a swab, a connected camera images the throat at home and a machine learning model reads the visual signs of infection. The authors argue that remote biosampling can only succeed if four conditions are met simultaneously: the technology must be available, diagnostically accurate, usable by non-experts, and integrated into existing medical systems. Their feasibility analysis suggests AI-driven imaging could meaningfully reduce the burden of in-clinic testing, expanding what remote care can legitimately diagnose rather than merely triage.

Neurology is getting its own remote upgrade. Pless and colleagues validated a smartphone platform that monitors cognitive function in people with multiple sclerosis through adaptive game-based tasks. Because the games adjust to each user and are played repeatedly in everyday settings, the system captures ecologically valid data that clinic visits miss, allowing longitudinal tracking of subtle cognitive drift outside the hospital. For a disease whose progression is notoriously difficult to measure between appointments, continuous smartphone-based assessment could reshape how clinicians detect deterioration and adjust treatment.

Cardiac rehabilitation offers one of the clearest quantitative wins in the collection. Harpula and colleagues studied patients recovering from acute myocardial infarction enrolled in a coordinated rehabilitation program and found that integrating a dedicated mobile application called AHP-KOS lifted program adherence from 67.4 percent to 91.7 percent while shortening the duration of onsite visits. Adherence is the perennial weak point of cardiac rehab, and a jump of more than twenty percentage points from an app-mediated workflow is the kind of result that hospital administrators notice. The study underscores that mobile health tools are not just patient conveniences; they can restructure institutional workflows themselves.

Underneath the headline applications, the collection also probes the unglamorous plumbing of virtual care. In an opinion piece, Khashu argues that the telehealth check-in process is an overlooked determinant of care quality, plagued by incomplete forms, unclear instructions, poor communication, duplicate data entry, and digital literacy gaps. His proposed remedies are data-driven: AI-powered reminders, patient portals, predictive analytics, chatbots, and real-time updates. Streamlining check-in, he contends, would reduce no-shows, raise patient satisfaction, optimize staffing, and improve data accuracy — the difference between a telehealth service that scales and one that stalls.

What patients actually want from virtual care is quantified in a discrete choice experiment by Chen and colleagues, who examined China’s fast-growing Internet hospital framework. Patients prioritized shorter waiting times, payment through medical insurance, and continuity with their original diagnosing physician. Although respondents favored high-quality care from tertiary hospitals and liked video consultations, the primary drivers of adoption were speed and cost reduction. That finding matters for policymakers: sustainable online outpatient services may depend less on cutting-edge features than on waiting rooms that are short and bills that are covered.

Mental health monitoring is also going remote and real-time. Pei and colleagues built a multimodal stress detection system combining electroencephalography with peripheral physiological signals. Eleven participants underwent validated social and mental stress protocols, and machine learning models classified stress from short signal windows with 98.1 percent accuracy for stress type and 97.8 percent for stress level using just four seconds of data. The brevity of the effective signal is the technical headline — real-time, wearable-grade stress assessment becomes plausible when four-second recordings suffice, opening a path to early mental health intervention before crises escalate.

Ageing in place is another frontier. Yang and colleagues proposed a wireless sensor network system for older adults in China’s sports and wellness towns, combining continuous health monitoring with smart home assistance. The platform collects daily activity and physiological data for behavior analysis and preliminary health assessment, running over a hybrid Sub-G and Wi-Fi network optimized for efficient transmission. In parallel, Yan and colleagues surveyed rural older adults in Henan Province and found that perceived usefulness, awareness, and accessibility drive willingness to use internet-based home care services, while digital literacy and infrastructure gaps remain the chief barriers. Together, the two studies sketch both the engineering blueprint and the social reality of remote eldercare.

Adoption economics emerge from Pakistan, where Saifullah and colleagues applied structural equation modeling to identify which service quality characteristics predict doctors’ intentions to use telehealth. Medical service quality, affordability, information quality, waiting time, and safety all emerged as significant predictors, and actual utilization showed a substantial positive impact on sustainable development. The authors argue that a well-structured telehealth model could revolutionize healthcare infrastructure and accessibility in developing nations — a claim the data on physician intent at least supports directionally.

Even clinical education has been pulled into the telehealth orbit. Khoza-Shangase reviewed how the COVID-19 pandemic accelerated virtual simulation, tele-supervision, and remote learning in audiology training in South Africa, improving accessibility and continuity of education while exposing persistent challenges in engagement, assessment, and infrastructure. The editorialists’ synthesis is measured: technological advancement alone is insufficient, and successful implementation depends on usability, accessibility, and alignment with the needs of both patients and providers. Looking forward, they call for integrating engineering innovation with rigorous clinical validation, human-centered design, and equitable deployment, with particular attention to bridging gaps in digital literacy and infrastructure so that emerging technologies do not deepen existing healthcare disparities. If the ten studies collected here are any guide, the future of telehealth will be decided less in laboratories than at the intersection of workflow design, payment policy, and the everyday digital confidence of patients.

Subject of Research: Telehealth and remote patient monitoring technologies for accessible healthcare delivery

Article Title: Editorial: Telehealth and remote monitoring solutions: advancing accessible and effective healthcare delivery

Article References: Nataraj, R., Dominguez-Morales, J. P., & Jagannath, M. (2026). Editorial: Telehealth and remote monitoring solutions: advancing accessible and effective healthcare delivery. Frontiers in Medical Engineering, 4, Article 1864948. https://doi.org/10.3389/fmede.2026.1864948

Image Credits: AI Generated

DOI: 10.3389/fmede.2026.1864948

Keywords: telehealth, remote patient monitoring, artificial intelligence, mobile health, digital health equity, wearable sensors, cardiac rehabilitation, multiple sclerosis, stress detection, smart homes, healthcare workflow, Internet hospitals

The study, led by Omeed Modiri of the David Geffen School of Medicine at UCLA together with colleagues at the Icahn School of Medicine at Mount Sinai, including senior author Jesse M. Lewin, surveyed members of the American College of Mohs Surgery, the professional home of physicians who perform Mohs micrographic surgery. This technique, considered the gold standard for many skin cancers, involves removing thin layers of tissue and examining each one under a microscope until no cancer cells remain. It is a discipline that demands precision, real-time pathological judgment, and careful reconstructive planning, making it an especially revealing setting in which to gauge how surgical specialists perceive AI.

The researchers distributed an anonymous twelve-question survey to ACMS members and analyzed the responses using Fisher’s exact tests, with a p value below 0.05 considered statistically significant. Thirty-six surveys were completed, a modest sample that the authors themselves flag as a key limitation alongside sampling and recall bias inherent to survey-based research. Even so, the results sketch a coherent picture of a specialty in transition. Forty-two percent of respondents reported having already used AI tools in some capacity, and sixty-seven percent of all participants said they were likely to incorporate AI into their clinical practice within two years. In other words, adoption is no longer a hypothetical question for Mohs surgeons; it is a matter of when and how.

The platforms these surgeons gravitate toward say a great deal about what they currently trust AI to do. ChatGPT dominated, used by ninety-six percent of those who reported AI experience, followed by OpenEvidence at thirty percent and Gemini at twenty-two percent. Custom AI models, the kind purpose-built for specific clinical tasks, saw only limited use. This pattern suggests that Mohs surgeons are relying on general-purpose large language models and medical literature search tools rather than bespoke surgical applications, treating AI today less as a scalpel and more as an assistant for information gathering and workflow support.

When asked about the benefits, respondents pointed overwhelmingly to efficiency rather than clinical prowess. Seventy-six percent identified increased efficiency as the most significant advantage of AI in dermatologic surgery, while sixty-two percent cited reduced administrative burden. These figures align with a broader theme in medical AI research: the technology’s most immediate and reliable value often lies in taming the paperwork, documentation, and coding tasks that consume clinician time, rather than in making high-stakes decisions. Prior work by some of the same authors, for example, evaluated how accurately ChatGPT and Gemini assign Current Procedural Terminology billing codes in dermatologic surgery, a quintessentially administrative application where errors are recoverable and oversight is straightforward.

The concerns, however, were sharper and more consistent than the optimism. Eighty-six percent of respondents worried about inaccurate or unreliable information, the highest of any concern measured in the survey. Fifty-four percent flagged patient safety and liability, and forty-nine percent feared the dehumanization of care. These numbers reveal a profession that understands exactly where the fault lines lie. Large language models can generate fluent, confident text that is nonetheless wrong, a phenomenon that becomes far more dangerous when the output informs where to cut tissue or how to reconstruct a face. Liability questions compound the technical ones, since it remains legally murky who bears responsibility when an AI-assisted recommendation contributes to a poor surgical outcome.

Nowhere is this tension more visible than in the gap between expectation and comfort. Seventy-two percent of participants believed AI will change the practice of dermatologic surgery over the next five to ten years, yet only thirty-nine percent reported feeling any degree of comfort with AI-assisted tools guiding surgical margins or repairs. The surgeons, in effect, are saying that transformation is coming whether they like it or not, while drawing a firm line at delegating intraoperative judgment. When it came to patient outcomes, opinions split further: fifty percent believed AI would lead to improvement, nineteen percent expected a neutral effect, twenty-five percent were unsure, and six percent anticipated harm. That distribution captures a specialty that is cautiously hopeful but far from convinced.

Experience appears to shape attitudes, though the survey could not prove it statistically. Surgeons with fewer than five years in practice were 2.83 times more likely to report having used AI tools compared with those in practice longer, a substantial numerical difference that fell short of statistical significance with a p value of 0.21, likely a casualty of the small sample size. Still, the trend fits a familiar pattern across medicine: younger clinicians, trained alongside consumer AI tools, arrive in practice already fluent in them, while more senior surgeons encounter the technology later and often more skeptically. If the trend holds in larger samples, the demographic gradient of AI adoption could become a defining feature of how surgical specialties modernize.

The study’s technical limitations deserve honest weight. Thirty-six respondents represent a small fraction of the ACMS membership, and those motivated to answer a survey about AI are plausibly more interested in the topic than their peers, introducing sampling bias. Recall bias may also color self-reported usage patterns. The authors are transparent about these constraints and call for larger studies to validate the findings, as well as for standardized guidelines evaluating responsible AI use in healthcare. Their caution echoes wider calls in the literature, including proposals for formal requirements to mitigate patient harm risks from AI in medicine and frameworks for adopting large language models safely in clinical settings.

What emerges from this report is not a story of resistance or revolution but of calibrated pragmatism. Mohs surgeons, whose work demands microscopic precision and carries visible, personal consequences for patients, are welcoming AI into the margins of their practice, the scheduling, the documentation, the literature searches, while keeping it at arm’s length from the margin of excision. That distinction may prove to be the template for surgical AI adoption more broadly: efficiency gains first, decision support last, and rigorous validation, clear liability frameworks, and preserved human judgment as the non-negotiable conditions in between. As one of the first systematic looks at AI attitudes within this specialty, the survey suggests that the next five to ten years will test whether AI developers can earn the trust that these surgeons are not yet ready to give.

Subject of Research: A survey of American College of Mohs Surgery members on the current use, perceived benefits, and risks of artificial intelligence in dermatologic surgery.

Article Title: Current impact and future outlook of artificial intelligence among American College of Mohs Surgery members

Article References: Modiri, O., McGeough, O., Elder, A., Ensslin, C., & Lewin, J. M. (2026). Current impact and future outlook of artificial intelligence among American College of Mohs Surgery members. Archives of Dermatological Research, 318(1), Article 458. https://doi.org/10.1007/s00403-026-04791-5

Image Credits: AI Generated

DOI: 10.1007/s00403-026-04791-5

Keywords: artificial intelligence, Mohs surgery, dermatologic surgery, ChatGPT, large language models, survey, skin cancer, clinical practice, patient safety, surgical margins, healthcare workflow, American College of Mohs Surgery