Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

Artificial Intelligence in Healthcare

Sweat potentially contains a wealth of physiologically relevant information, but has traditionally been an underutilized resource for non-invasive health monitoring. Recent advances in wearable sweat sensors have overcome many of the historic drawbacks of sweat sensing and such sensors now offer methods of gleaning molecular-level insight into the dynamics of our bodies. Here we review key developments in sweat sensing technology. We highlight the potential value of sweat-based wearable sensors, examine state-of-the-art devices and the requirements of the underlying components, and consider ways to tackle data integrity issues within these systems. We also discuss challenges and opportunities for wearable sweat sensors in the development of personalized healthcare.

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Wearable sweat sensors

Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.

Integrative omics for health and disease.

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

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Wearables and the medical revolution.

Precision health relies on the ability to assess disease risk at an individual level, detect early preclinical conditions and initiate preventive strategies. Recent technological advances in omics and wearable monitoring enable deep molecular and physiological profiling and may provide important tools for precision health. We explored the ability of deep longitudinal profiling to make health-related discoveries, identify clinically relevant molecular pathways and affect behavior in a prospective longitudinal cohort (n = 109) enriched for risk of type 2 diabetes mellitus. The cohort underwent integrative personalized omics profiling from samples collected quarterly for up to 8 years (median, 2.8 years) using clinical measures and emerging technologies including genome, immunome, transcriptome, proteome, metabolome, microbiome and wearable monitoring. We discovered more than 67 clinically actionable health discoveries and identified multiple molecular pathways associated with metabolic, cardiovascular and oncologic pathophysiology. We developed prediction models for insulin resistance by using omics measurements, illustrating their potential to replace burdensome tests. Finally, study participation led the majority of participants to implement diet and exercise changes. Altogether, we conclude that deep longitudinal profiling can lead to actionable health discoveries and provide relevant information for precision health. Personalized omics profiling can lead to actionable health discoveries and stimulate lifestyle changes.

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A longitudinal big data approach for precision health

A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO2] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.

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Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information.

CTLA-4 is an important regulator of T-cell function. Here, we report that expression of this immune-regulator in mouse B-1a cells has a critical function in maintaining self-tolerance by regulating these early-developing B cells that express a repertoire enriched for auto-reactivity. Selective deletion of CTLA-4 from B cells results in mice that spontaneously develop autoantibodies, T follicular helper (Tfh) cells and germinal centers (GCs) in the spleen, and autoimmune pathology later in life. This impaired immune homeostasis results from B-1a cell dysfunction upon loss of CTLA-4. Therefore, CTLA-4-deficient B-1a cells up-regulate epigenetic and transcriptional activation programs and show increased self-replenishment. These activated cells further internalize surface IgM, differentiate into antigen-presenting cells and, when reconstituted in normal IgH-allotype congenic recipient mice, induce GCs and Tfh cells expressing a highly selected repertoire. These findings show that CTLA-4 regulation of B-1a cells is a crucial immune-regulatory mechanism.

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CTLA-4 expression by B-1a B cells is essential for immune tolerance

e16046 Background: Esophageal cancer is the sixth most common cause of cancer-related mortality. There are two distinct histological types of esophageal cancer: esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC). In China, ESCC is the major subtype. Higher incidence of EAC is observed in western countries. Liquid biopsy detecting circulating tumor DNA is a non-invasive, real-time and highly sensitive approach that is able to identify and monitor the genetic mutations in tumor. Methods: In this study, we collected 490 Chinese esophageal cancer patients from 2018-2022 and characterized the mutational landscape of esophageal tumor tissue and circulating tumor DNA by whole exome sequencing or deep sequencing of targeted gene panel. The consistency of genetic alteration between tumor and ctDNA was also explored. Results: A total of 176 patients with plasma and 345 patients with cancer tissue were analyzed, among them, 31 patients had both ctDNA and tissue biopsy data. 277 genes with 1257 mutations were identified from plasma samples of 176 patients. The top frequently mutated genes were TP53 (89.1%), KMT2D (24.0%), SYNE1 (14.9%), NOTCH1 (14.3%), EP300 (13.7%), LRP1B (13.7%) and ZNF750 (10.3%). The mutation rates of these genes between ctDNA and tissue biopsy (N = 31 patients) were in concordance by Fisher's exact test (P > 0.05). Most frequent mutations were found in TP53 gene, which account for 89.1% of total plasma samples and 90.4% of tumor tissues. KMT2D gene mutations were detected in 24% of patients which encode histone methyltransferase that methylates the Lys-4 position of histone H3 and regulates transcriptional activation, indicating epigenetic regulation might be involved in the pathogenesis of esophageal cancer. Functional enrichment analysis was performed including Gene Ontology (GO) and KEGG analysis across all the three domains, Biological process, Molecular Function, and Cellular component. The KEGG terms with the highest enrichment score were PI3K−Akt signaling pathway, which is a key driver in carcinogenesis. In addition, we didn’t observe the mutually exclusive mutation or co-occurrence pattern of any gene pairs in our cohort. Conclusions: We demonstrated the utility of liquid biopsy to draw a ctDNA mutation profiling of esophageal cancer in Chinese patients. Genetic mutations identified from ctDNA showed consistency with mutations from tumor tissue.

Gao Zhou

Papers

Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information.

CTLA-4 expression by B-1a B cells is essential for immune tolerance

Genetic alteration and ctDNA mutation profiling of esophageal cancer in Chinese patients.