News & Comment

Gregoor et al. evaluated the healthcare implications and costs of an AI-enabled mobile health app for skin cancer detection, involving 18,960 beneficiaries of a Netherlands insurer. They report a 32% increase in claims for premalignant and malignant skin lesions among app users, largely attributed to benign skin lesions and leading to higher annual costs for app users (€64.97) compared to controls (€43.09). Cost-effectiveness analysis showed a comparable cost to dermatologist-based diagnosis alone. This editorial emphasizes the balance in AI-based dermatology between increased access and increased false positives resulting in overutilization. We suggest refining the diagnostic schemas with new referral pathways to capitalize on potential savings. We also discuss the importance of econometric analysis to evaluate the adoption of new technologies, as well as adapting payment models to mitigate the risk of overutilization inherent in AI-based diagnostics such as skin cancer detection.

Digital health technologies (DHTs) enable remote data collection, support a patient-centric approach to drug development, and provide real-time data in real-world settings. With increasing use of DHTs in clinical care and development, we expect a growing body of evidence supporting use of DHTs to capture endpoint data in clinical trials. As the body of evidence grows, it will be critical to ensure that available prior work can be leveraged. We propose a framework to reuse analytical and clinical validation, as well as verification data, generated for existing DHTs. We apply real life case studies to illustrate our proposal aimed at leveraging prior work, while applying the V3 framework (verification, analytical validation, clinical validation) and avoiding duplication. Utilizing our framework will enable stakeholders to share best practices and consistent approaches to employing these tools in clinical studies, build on each other’s work, and ultimately accelerate evidence generation demonstrating the reproducibility and value add of these new tools.

AI-based prediction models demonstrate equal or surpassing performance compared to experienced physicians in various research settings. However, only a few have made it into clinical practice. Further, there is no standardized protocol for integrating AI-based physician support systems into the daily clinical routine to improve healthcare delivery. Generally, AI/physician collaboration strategies have not been extensively investigated. A recent study compared four potential strategies for AI model deployment and physician collaboration to investigate the performance of an AI model trained to identify signs of acute respiratory distress syndrome (ARDS) on chest X-ray images. Here we discuss strategies and challenges with AI/physician collaboration when AI-based decision support systems are implemented in the clinical routine.

Artificial Intelligence-supported digital applications (AI applications) are expected to transform radiology. However, providers need the motivation and incentives to adopt these technologies. For some radiology AI applications, the benefits of the application itself may sufficiently serve as the incentive. For others, payers may have to consider reimbursing the AI application separate from the cost of the underlying imaging studies. In such circumstances, it is important for payers to develop a clear set of criteria to decide which AI applications should be paid for separately. In this article, we propose a framework to help serve as a guide for payers aiming to establish such criteria and for technology vendors developing radiology AI applications. As a rule of thumb, we propose that radiology AI applications with a clinical utility must be reimbursed separately provided they have supporting evidence that the improved diagnostic performance leads to improved outcomes from a societal standpoint, or if such improved outcomes can reasonably be anticipated based on the clinical utility offered.

Artificial intelligence systems are increasingly being applied to healthcare. In surgery, AI applications hold promise as tools to predict surgical outcomes, assess technical skills, or guide surgeons intraoperatively via computer vision. On the other hand, AI systems can also suffer from bias, compounding existing inequities in socioeconomic status, race, ethnicity, religion, gender, disability, or sexual orientation. Bias particularly impacts disadvantaged populations, which can be subject to algorithmic predictions that are less accurate or underestimate the need for care. Thus, strategies for detecting and mitigating bias are pivotal for creating AI technology that is generalizable and fair. Here, we discuss a recent study that developed a new strategy to mitigate bias in surgical AI systems.

Health and wellness/well-being are multifaceted topics further complicated when trying to understand the environmental impact. Typically, there has been a one size fits all approach when trying to understand the 3-way interaction, but that is a limited approach. Equally, measurement (of each) has often used a limited set of outcomes during short periods to provide insight. A more robust understanding of health and well-being within environments may require longitudinal/continuous assessment that holistically targets individuals. Therefore, there is a growing requirement for careful data management, individual-first methodologies, scalable research designs and new analytical approaches, e.g., artificial intelligence. That presents many challenges but interesting research opportunities for the field of digital medicine.

Digital health technologies (DHTs) have brought several significant improvements to clinical trials, enabling real-world data collection outside of the traditional clinical context and more patient-centered approaches. DHTs, such as wearables, allow the collection of unique personal data at home over a long period. But DHTs also bring challenges, such as digital endpoint harmonization and disadvantaging populations already experiencing the digital divide. A recent study explored the growth trends and implications of established and novel DHTs in neurology trials over the past decade. Here, we discuss the benefits and future challenges of DHT usage in clinical trials.

The generalizability of predictive algorithms is of key relevance to application in clinical practice. We provide an overview of three types of generalizability, based on existing literature: temporal, geographical, and domain generalizability. These generalizability types are linked to their associated goals, methodology, and stakeholders.

The usage of digital devices in clinical and research settings has rapidly increased. Despite their promise, optimal use of these devices is often hampered by low adherence. The relevant factors predictive of long-term adherence have yet to be fully explored. A recent study investigated device usage over 12 months in a cohort of the electronic Framingham Heart Study. It identified sociodemographic and health-related factors associated with the long-term use of three digital health components: a smartphone app, a digital blood pressure cuff, and a smartwatch. The authors found that depressive symptoms and lower self-rated health were associated with lower smartwatch usage. Female sex and higher education levels were associated with higher app-based survey completion. Here, we discuss factors predictive for adherence and personalized strategies to promote adherence to digital tools.

Artificial intelligence (AI) and natural language processing (NLP) have found a highly promising application in automated clinical coding (ACC), an innovation that will have profound impacts on the clinical coding industry, billing and revenue management, and potentially clinical care itself. Dong et al. recently analyzed the technical challenges of ACC and proposed future directions. Primary challenges for ACC exist at the technological and implementation levels; clinical documents are redundant and complex, code sets like the ICD-10 are rapidly evolving, training sets are not comprehensive of codes, and ACC models have yet to fully capture the logic and rules of coding decisions. Next steps include interdisciplinary collaboration with clinical coders, accessibility and transparency of datasets, and tailoring models to specific use cases.

Digital tools are transforming mental health care. The promise of this transformation to improve outcomes has not yet been realized fully. While some have become skeptical, this article argues that we are just at the end of Act 1, with several opportunities and challenges ahead.

Postpartum mental health conditions are a public health concern, affecting a large number of reproductive-age women and their families. Postpartum depression alone affects at least 14% of new mothers and their families. However, very little has been written about how advances in digital mental health can benefit women in the postpartum period, or how those advances may poorly serve this vulnerable population. This manuscript takes a high-level view of the advances in different areas of digital mental health, including telehealth, apps, and digital phenotyping. In this comment, we explore ways in which digital interventions for postpartum mental health may help with connection to treatment, accessibility, agency, and ease of access. We also note particular concerns for how digital postpartum mental health may encounter issues of low-quality resources, ethical considerations, and equity considerations. We provide suggestions for how to leverage the promise and avoid the pitfalls of digital mental health for postpartum women.

Even as innovation occurs within digital medicine, challenges around equity and racial health disparities remain. Golden et al. evaluate structural racism in their recent paper focused on reproductive health. They recommend a framework to Remove, Repair, Restructure, and Remediate. We propose applying the framework to three areas within digital medicine: artificial intelligence (AI) applications, wearable devices, and telehealth. With this approach, we can continue to work towards an equitable future for digital medicine.

This paper reviews the current state of patient safety and the application of artificial intelligence (AI) techniques to patient safety. This paper defines patient safety broadly, not just inpatient care but across the continuum of care, including diagnostic errors, misdiagnosis, adverse events, injuries, and measurement issues. It outlines the major current uses of AI in patient safety and the relative adoption of these techniques in hospitals and health systems. It also outlines some of the limitations of these AI systems and the challenges with evaluation of these systems. Finally, it outlines the importance of developing a proactive agenda for AI in healthcare that includes marked increased funding of research and evaluation in this area.

Digital technology is increasingly important in people’s lives, particularly for new parents as it allows them to access information, stay connected to peers and offers them seductive solutions for improving infant sleep and parental well-being. Digital technology has been developed to support parents in the following four ways: (1) providing digital information on infant sleep, (2) offering targeted support for night-time care, (3) managing infant sleep and (4) monitoring infant sleep and safety. Evidence on the effectiveness of these strategies is varied and there are concerns regarding the reliability of information, use of personal data, commercial exploitation of parents, and the effects of replacing caregiver presence with digital technology.

Artificial Intelligence (AI) in medicine has grown rapidly, yet few algorithms have been deployed. It is not the problem with the AI itself but with the way functions and results are communicated. Regulatory science provides the appropriate language and solutions to this problem for three reasons: First, there is value in the intentionally interdisciplinary regulatory language. Second, regulatory concepts are important for AI researchers because these concepts enable tackling of risk and safety concerns as well as understanding of recently proposed regulations in the US and Europe. Third, regulatory science is a scientific discipline that evaluates and challenges current regulation—aiming for evidence-based improvements. Knowledge of the regulatory language, concepts, and science should be regarded a core competency for communicating medical innovation. Regulatory grade communication will be the key to bringing medical AI from hype to standard of care. Foregoing the possible benefits of regulatory science as a unifying force for the realization of medical AI is a missed opportunity.

This commentary examines the impact of multi-level racism on reproductive health disparities in the United States. Multi-level racism and its impact on reproductive health over the lifespan are described on a societal, community, and individual level. To advance, we recommend using the Remove, Repair, Restructure, Remediate (R4P) approach combined with the Retrofit, Reform, and Reimagine (3R) model to address multiple forms of racism. Emergent policies and actions are identified to proceed towards health equity.