Nature, Published online: 23 June 2025; doi:10.1038/d41586-025-01946-8
The uncontrolled deployment of machine learning in medicine can distort patient information and sacrifice long-term data reliability for short-term benefits.
Around the world, countries are facing the challenges of aging populations, growing rates of chronic disease, and workforce shortages, leading to a growing burden on health care systems. From diagnosis to treatment, AI and other digital solutions can enhance the efficiency and effectiveness of health care, easing the burden on straining systems. According to the World Health Organization (WHO), spending an additional $0.24 per patient per year on digital health interventions could save more than two million lives from non-communicable diseases over the next decade.
To work most effectively, digital solutions need to be scaled and embedded in an ecosystem that ensures a high degree of interoperability, data security, and governance. If not, the proliferation of point solutions— where specialized software or tools focus on just one specific area or function—could lead to silos and digital canyons, complicating rather than easing the workloads of health care professionals, and potentially impacting patient treatment. Importantly, technologies that enhance workforce productivity should keep humans in the loop, aiming to augment their capabilities, rather than replace them.
Through a survey of 300 health care executives and a program of interviews with industry experts, startup leaders, and academic researchers, this report explores the best practices for success when implementing integrated digital solutions into health care, and how these can support decision-makers in a range of settings, including laboratories and hospitals.
Key findings include:
Health care is primed for digital adoption. The global pandemic underscored the benefits of value-based care and accelerated the adoption of digital and AI-powered technologies in health care. Overwhelmingly, 96% of the survey respondents say they are “ready and resourced” to use digital health, while one in four say they are “very ready.” However, 91% of executives agree interoperability is a challenge, with a majority (59%) saying it will be “tough” to solve. Two in five leaders say balancing security with usability is the biggest challenge for digital health. With the adoption of cloud solutions, organizations can enjoy the benefits of modernized IT infrastructure: 36% of the survey respondents believe scalability is the main benefit, followed by improved security (28%).
Digital health care can help health care institutions transform patient outcomes—if built on the right foundations. Solutions like AI-powered diagnostics, telemedicine, and remote monitoring can offer measurable impact across the patient journey, from improving early disease detection to reducing hospital readmission rates. However, these technologies can only support fully connected health care when scaled up and embedded in ecosystems with robust data governance, interoperability, and security.
Health care data has immense potential—but fragmentation and poor interoperability hinder impact. Health care systems generate vast quantities of data, yet much of it remains siloed or unusable due to inconsistent formats and incompatible IT systems, limiting scalability.
Digital tools must augment, not overload, the workforce. With global health care workforce shortages worsening, digital solutions like clinical decision support tools, patient prediction, and remote monitoring can be seen as essential aids rather than threats to the workforce. Successful deployment depends on usability, clinician engagement, and training.
Regulatory evolution, open data policies, and economic sustainability are key to scaling digital health. Even the best digital tools struggle to scale without reimbursement frameworks, regulatory support, and viable business models. Open data ecosystems are needed to unleash the clinical and economic value of innovation. Regulatory and reimbursement innovation is also critical to transitioning from pilot projects to high-impact, system-wide adoption.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
This content was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
iScience. 2025 May 15;28(6):112681. doi: 10.1016/j.isci.2025.112681. eCollection 2025 Jun 20.
ABSTRACT
Metabolic reprogramming is a defining feature of pancreatic cancer, influencing tumor progression and the tumor microenvironment. By integrating single-cell transcriptomics, spatial transcriptomics, and spatial metabolomics, this study visualized the spatial co-localization of metabolites and gene expression within tumor samples, uncovering metabolic heterogeneity and intercellular interactions. Spatial transcriptomics identified distinct pathological regions, which were further characterized using single-cell transcriptomic data and pathologist annotations. Pseudotime trajectory analysis revealed metabolic shifts along the malignant progression, while single-cell Metabolism (scMetabolism) delineated metabolic differences between pathological regions, classifying them as hypermetabolic or hypometabolic. Notably, aberrant cell communication between cancer cells, macrophages, and fibroblasts was observed, with key receptor-ligand pairs significantly co-expressed in malignant regions and correlated with poor prognosis. Spatial metabolomics imaging identified signature metabolites, highlighting metabolic alterations in amino acid metabolism, polyamine metabolism, fatty acid synthesis, and phospholipid metabolism. This integrated analysis provides critical insights into pancreatic cancer metabolism, offering potential avenues for targeted therapeutic interventions.
PMID:40538442 | PMC:PMC12177182 | DOI:10.1016/j.isci.2025.112681
Cancer Treat Rev. 2025 Jun 14;139:102979. doi: 10.1016/j.ctrv.2025.102979. Online ahead of print.
ABSTRACT
Breast cancer is characterized by significant molecular heterogeneity; therefore, there are distinct clinical features, treatment modalities, and prognostic outcomes across its various molecular subtypes. In the era of precision medicine, liquid biopsy has emerged as a convenient and minimally invasive technique capable of dynamically representing the comprehensive tumor gene spectrum. This review systematically elaborates the clinical value of liquid biopsy as a breakthrough tool for precision diagnosis and treatment in breast cancer through dynamic detection of key biomarkers, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and non-coding RNA (ncRNA). Specific genetic mutations and methylation signatures in ctDNA can be applied to early breast cancer screening, minimal residual disease monitoring, and tracking drug resistance mechanisms. CTCs enumeration (≥1/7.5 mL in early-stage cancer or ≥ 5/7.5 mL in metastatic cancer) and PD-L1 expression levels demonstrate direct correlations with prognostic stratification and the efficacy of immunotherapy. As the specificity and sensitivity of liquid biopsy continue to improve, personalized treatment strategies, informed by biomarker analysis and targeted precision therapies, have unveiled new avenues of hope for patients with breast cancer. However, several challenges persist in the practical application of liquid biopsy. Despite persistent challenges, such as insufficient standardization and difficulties in resolving low-abundance variants, future advancements should focus on multi-omics integration and AI-driven technological breakthroughs to overcome bottlenecks in clinical translation. This review summarizes cutting-edge liquid biopsy technologies for identifying clinically significant molecular biomarkers, focusing on discussing critical challenges in the strategies to advance precision oncology applications for optimized treatment guidance and disease surveillance in breast cancer.
PMID:40540857 | DOI:10.1016/j.ctrv.2025.102979
Cytokine Growth Factor Rev. 2025 Jun 6:S1359-6101(25)00071-1. doi: 10.1016/j.cytogfr.2025.06.001. Online ahead of print.
ABSTRACT
Lung cancer is a leading cause of cancer-related mortality worldwide, characterized by the uncontrolled growth and spread of abnormal cells. Despite constant progress in diagnosis and treatment of lung cancer remains highly challenging. Present review explores the molecular underpinnings of lung cancer, emphasizing genetic mutations, altered metabolism, and the role of biomarkers in diagnosis and treatment. Advances in diagnostic methods, particularly liquid biopsies, have enabled non-invasive detection and monitoring of lung cancer. Therapeutic approaches have evolved significantly, with molecular-targeted therapies focusing on pathways like EGFR, ALK, KRAS, and MET. Caner Immunotherapy, including immune checkpoint inhibitors, artificial intelligence, and multi-omics analysis, including genomics and image data to predict treatment response, has further enhanced precision medicine by enabling earlier detection, better prognostic models, and efficient drug discovery. AI has a significant impact on various aspects of oncology, including improving the earlier diagnosis of lung cancer. Despite these advancements, challenges such as drug resistance, tumor heterogeneity, and limited efficacy of certain therapies persist, necessitating continued research and innovation.
PMID:40544104 | DOI:10.1016/j.cytogfr.2025.06.001
Cell Signal. 2025 Jun 18;134:111950. doi: 10.1016/j.cellsig.2025.111950. Online ahead of print.
ABSTRACT
FAAP100 plays an essential role in DNA damage repair, with dysregulation associated with elevated cancer susceptibility. Nevertheless, comprehensive pan-cancer analyses examining FAAP100 prognostic significance, immune correlations, and epigenetic regulation remains unexplored. This study systematically characterized FAAP100 across 33 cancer types utilizing multi-omics data from TCGA, UALCAN, cBioPortal, TIMER2.0, and CPTAC. Analytical assessments included expression profiles, prognostic significance, and diagnostic utility, alongside associations with DNA methylation, immune cell infiltration, immune checkpoint gene expression, tumor mutational load (TMB), microsatellite instability (MSI), and drug resistance. Findings revealed significant FAAP100 upregulation across multiple cancer types, exhibiting inverse correlations to patient survival. Genomic characterization identified associations between FAAP100 overexpression and both copy number amplification and promoter hypomethylation. Immune profiling demonstrated robust correlations with immune cell infiltration levels and checkpoint molecule activity. Functional assays utilizing PC9 and H1299 cells indicated that FAAP100 enhances cellular proliferation and migration while inhibiting apoptosis processes. In vivo studies confirmed tumor growth suppression upon FAAP100 knockdown. Collectively, this multi-omics investigation identifies FAAP100 as a pan-cancer oncogene driver, highlighting its potential as both a prognostic biomarker and therapeutic target. The integrated analysis of expression patterns, epigenetic modifications, immune characteristics, and genomic alterations elucidates the mechanistic involvement of FAAP100 in tumor progression, providing a foundation for clinical application in precision oncology approaches..
PMID:40541815 | DOI:10.1016/j.cellsig.2025.111950
J Gastrointest Cancer. 2025 Jun 19;56(1):139. doi: 10.1007/s12029-025-01249-1.
ABSTRACT
BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor, with rising incidence and mortality rates posing a significant threat to global public health. Accurate prediction of liver cancer occurrence and progression is essential for improving patient prognosis. This study uses bibliometric methods to analyze the current state and future trends in liver cancer prediction research.
METHODS: A search was conducted in the Web of Science (WOS) database on October 22, 2023, identifying 1092 articles on liver cancer prediction. These articles were quantitatively analyzed using CiteSpace 6.2 software, with a focus on research hotspots, authors, countries, and keywords.
RESULTS: The study involved 114 countries, 4254 institutions, and 280 journals, with 48,788 citations. China (826 papers) and the USA (96 papers) dominate the field. Leading institutions include Sun Yat-sen University, Fudan University, Zhejiang University, and Yonsei University. The most cited journals were Hepatology (2209 citations) and Journal of Hepatology (946 citations). Frontiers in Oncology had the highest H-index (14). Key authors include Kim Seung Up (23 papers) and Ahn Sang Hoon (H-index = 14). Early research focused on risk factors and staging, while recent studies emphasize DNA methylation, immune microenvironments, and tumor metastasis. Future research will focus on multi-omics data integration and AI-driven predictive model optimization.
CONCLUSION: This study provides a comprehensive overview of liver cancer prediction research, highlighting key trends and the potential of multi-omics data and machine learning to enhance predictive models and clinical outcomes.
PMID:40537718 | DOI:10.1007/s12029-025-01249-1
The Void IDE was recently released in beta, positioning itself as a privacy-focused and free alternative to popular closed-source AI editors like Cursor and GitHub Copilot. Void IDE is a fork of Visual Studio Code. While Microsoft recently announced plans to open Source its GitHub Copilot Chat Extension possibly in a few months, the beta release is available now for the community to fiddle with.
By Bruno Couriol
Cell Death Discovery, Published online: 20 June 2025; doi:10.1038/s41420-025-02550-5
Ferroptosis in periodontitis: mechanisms, impacts, and systemic connections
Support Care Cancer. 2025 Jun 19;33(7):596. doi: 10.1007/s00520-025-09670-9.
ABSTRACT
PURPOSE: Anorexia is a frequent and serious symptom in patients with lung cancer, often leading to malnutrition and cachexia, and negatively affecting quality of life and survival. This scoping review systematically synthesizes current evidence on biomarkers associated with cancer-related anorexia (CRA) in lung cancer, aiming to clarify biological mechanisms and inform targeted interventions.
METHODS: We performed a comprehensive literature search of studies evaluating the associations between CRA and various biomarkers in patients with lung cancer. Data were extracted and analyzed for pathway, genomic, transcriptomic, epigenetic, proteomic, metabolic, and composite biomarkers.
RESULTS: A total of 33 studies were included, identifying more than 100 biomarkers closely associated with CRA in lung cancer. These include inflammatory cytokines, energy metabolism markers, epigenetic and transcriptomic alterations, and disruptions in multiple cellular signaling pathways. Our analysis demonstrates that CRA is not the result of a single factor but reflects widespread dysregulation across metabolic, immune, and signaling networks. Some studies suggest that nutritional and anti-inflammatory interventions, such as n-3 fatty acid and antioxidant supplementation, can modulate biomarker profiles and potentially improve clinical outcomes.
CONCLUSION: CRA in lung cancer is a multifactorial syndrome involving complex interactions among inflammatory, metabolic, and signaling pathways. Multi-omics biomarker integration holds promise for early detection and individualized treatment, but larger, multi-center studies are needed to confirm clinical utility and optimize management strategies. Precision interventions based on biomarker profiles should be further explored in future research and practice.
PMID:40536584 | DOI:10.1007/s00520-025-09670-9
Architectural success in decentralized systems depends more on how decisions are made than on system design alone. Replacing control with trust requires visible, structured practices—such as ADRs and advice forums—to build confidence and clarity.
By Lindsey Tibbitts
Nature, Published online: 19 June 2025; doi:10.1038/d41586-025-01942-y
Although language models can accelerate systematic reviews, a fully automated system is still some way off.
Nature, Published online: 18 June 2025; doi:10.1038/s41586-025-09180-y
During visual learning, neural plasticity is driven by unsupervised learning in mice.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Why it’s so hard to stop tech-facilitated abuse
After Gioia had her first child with her then husband, he installed baby monitors throughout their home—to “watch what we were doing,” she says, while he went to work. She’d turn them off; he’d get angry. By the time their third child turned seven, Gioia and her husband had divorced, but he still found ways to monitor her behavior. One Christmas, he gave their youngest a smartwatch. Gioia showed it to a tech-savvy friend, who found that the watch had a tracking feature turned on. It could be turned off only by the watch’s owner—her ex.
And Gioia is far from alone. In fact, tech-facilitated abuse now occurs in most cases of intimate partner violence—and we’re doing shockingly little to prevent it. Read the full story.
—Jessica Klein
This story is from the next print edition of MIT Technology Review, which explores power—who has it, and who wants it. It’s set to go live on Wednesday June 25, so subscribe & save 25% to read it and get a copy of the issue when it lands!
Why AI hardware needs to be open
—by Ayah Bdeir, a leader in the maker movement, champion of open source AI, and founder of littleBits, the hardware platform that teaches STEAM to kids through hands-on invention.
Once again, the future of technology is being engineered in secret by a handful of people and delivered to the rest of us as a sealed, seamless, perfect device. When technology is designed like this, we are reduced to consumers. We don’t shape the tools; they shape us.
However, this moment creates a chance to do things differently. Because away from the self-centeredness of Silicon Valley, a quiet, grounded sense of resistance is reactivating. Read the full story.
In China, people are seeking help from AI-generated avatars to process their grief after a family member passes away. Our story about this trend is the latest to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Iran is going offline to avoid Israeli cyberattacks
A government spokesperson said it plans to disconnect completely from the global internet this evening. (The Verge)
+ How attacks on Iran’s oil exports could hurt China. (WSJ $)
2 Trump is giving TikTok another reprieve from a US ban
It’s been a full five years since he signed the original executive order telling Bytedance to sell it. (CNN)
+ Why Chinese manufacturers are going viral on TikTok. (MIT Technology Review)
3 Conspiracy theories about the Minnesota shooting are all over social media
Whenever there’s an information vacuum, people are all too keen to fill it with noise and nonsense. (NBC)
+ The shooting suspect allegedly used data broker sites to find targets’ addresses. (Wired $)
4 Tensions between OpenAI and Microsoft are starting to boil over
OpenAI has even threatened to report its formerly close partner to antitrust regulators. (WSJ $)
+ Here are the concessions OpenAI is seeking. (The Information $)
+ Inside the story that enraged OpenAI. (MIT Technology Review)
5 California cops are using AI cameras to investigate ICE protests
And sharing license plate data with other agencies, a practice some experts say is illegal. (404 Media)
+ How a new type of AI is helping police skirt facial recognition bans. (MIT Technology Review)
6 Social media is now Americans’ primary news source
It’s overtaken TV for the first time. (Reuters)
+ They watched more TV via streaming than cable last month, too. (NYT $)
7 Weight loss drugs may not work quite as well as hoped
Researchers analysed data from 51,085 patients and found bariatric surgery delivered better, more sustainable results. (The Guardian)
8 What is AI doing to reading? 
Here’s what we stand to gain—and lose—when we outsource reading to machines. (New Yorker $)
9 India is relying on China to build up its EV market
It’s taking a drastically different course to the US. (Rest of World)
+ Why EVs are (mostly) set for solid growth in 2025. (MIT Technology Review)
10 People are building AI tools to decipher cats’ meows 
Bet at least half of them are “feed me.” (Scientific American $)
Quote of the day
“Have we fallen so low? Have we no shame?”
—Remarks made by federal judge Williams G. Young this week as he voided some of the Trump administration’s cuts to National Institutes of Health grants, saying they were discriminatory, the New York Times reports.
One more thing
Why AI could eat quantum computing’s lunch
Tech companies have been funneling billions of dollars into quantum computers for years. The hope is that they’ll be a game changer for fields as diverse as finance, drug discovery, and logistics.
But while the field struggles with the realities of tricky quantum hardware, another challenger is making headway in some of these most promising use cases. AI is now being applied to fundamental physics, chemistry, and materials science in a way that suggests quantum computing’s purported home turf might not be so safe after all. Read the full story.
—Edd Gent
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ Wait a minute, Will Smith was offered a role in Inception? Much to think about.
+ No pain, no gain? Not necessarily.
+ John Waters, you really are one of a kind.
+ Say it ain’t so—I refuse to believe that young love is dead!
Biomol Biomed. 2025 Jun 10. doi: 10.17305/bb.2025.12324. Online ahead of print.
ABSTRACT
Immune checkpoint inhibitors (ICIs) demonstrate substantial interpatient variability in clinical efficacy for unresectable non-small cell lung cancer (NSCLC), underscoring the unmet need for noninvasive biomarkers to predict early therapeutic responses and improve survival outcomes. To address this, we developed a CT-based deep learning model integrated with the systemic immune-inflammatory-nutritional index (SIINI) for early prediction of ICI response. In a retrospective multicenter study of 265 patients treated with ICIs (incorporating chest CT and laboratory data), the cohort was divided into training (70%), internal validation (30%), and external validation sets. The combined model-leveraging DenseNet121-derived deep radiomic features alongside SIINI-achieved strong predictive performance, with AUCs of 0.865 (95% CI: 0.7709-0.9595) in the internal validation cohort and 0.823 (95% CI: 0.6627-0.9827) in the external validation cohort. Gradient-weighted class activation mapping (Grad-CAM) highlighted key CT regions contributing to model predictions, enhancing interpretability for clinical application. These findings highlight the potential of integrating deep learning with inflammatory biomarkers to support personalized ICI therapy in unresectable NSCLC. Future directions include incorporating multi-omics biomarkers, expanding multicenter validation, and increasing sample sizes to further improve predictive accuracy and facilitate clinical translation.
PMID:40525631 | DOI:10.17305/bb.2025.12324
Infect Agent Cancer. 2025 Jun 15;20(1):37. doi: 10.1186/s13027-025-00672-0.
ABSTRACT
Mendelian Randomization (MR) is increasingly used in cancer research to infer causal relationships by leveraging genetic variants as instrumental variables. While the growth of genome-wide association studies and biobank data has expanded the utility of MR, this surge-particularly pronounced in China-raises concerns about methodological rigor. The widespread adoption may be partly driven by the Chinese translation of key MR literature. Recent advances such as multivariable MR, mediation analysis, and integration with AI and omics data have enhanced the robustness and biological interpretability of MR studies. However, challenges persist, including horizontal pleiotropy, weak instrument bias, and misinterpretation of biomarkers as causal exposures. To improve MR study credibility, frameworks like STROBE-MR and MR-GRADE are being adopted. This article reviews methodological improvements and persistent pitfalls in MR, especially within cancer epidemiology, and highlights strategies for ensuring validity in this rapidly evolving field.
PMID:40518507 | PMC:PMC12168377 | DOI:10.1186/s13027-025-00672-0
Comput Biol Med. 2025 Aug;194:110559. doi: 10.1016/j.compbiomed.2025.110559. Epub 2025 Jun 14.
ABSTRACT
BACKGROUND: The in silico analyses provide evidence supporting the potential of methylation-driven differentially expressed genes as therapeutic targets across cancer types. This leads us to identify novel targets and their associated drug compounds for further progress towards pancreatic cancer treatment.
OBJECTIVE: To identify targeted drugs based on methylation driven genes identified using bulk multi-omics data and single-cell level data to pinpoint important disease markers.
METHODS: The workflow involves screening using the TCGA and ICGC databases, followed by validation with GEO datasets. The study employs supervised learning algorithms like kNN and random forests, and constructs a prediction model using adaptive LASSO-Cox regression. The process also includes pathway analysis, evaluation of survival status, and immune profile deconvolution, as well as multistage evaluation of the methylation driven genes. We conducted drug targeting and molecular dynamic simulations, taking into account genes of interest.Lastly, molecular docking and dynamics simulations were used to find out if the key MEDEGs could be utilized as drug targets.
RESULTS: CD36, UGT1A1, TFF1, S100P, MUC13, CALHM3 and ANKRD44 were found to be top 7 methylation driven genes. The mutational profile was also documented along with pathway analysis, which showed concordance with our observation based on their significant enriched terms namely "Maintenance of Gastrointestinal Epithelium", and "Digestive System Homeostasis". CD36 had prognostic capabilities and was seen to significant in terms of survival and also showed significant immune dysregulation. Our novel findings suggest TFF1, S100P, and MUC13 were found to be associated with cell type specific expression as seen in single cell data and UGT1A1 was found to be suitable for probable drug targeting. CD36, UGT1A1, TFF1, S100P, and MUC13 showed concordance when observed at proteomics level and across other datasets. Apigenin-7-O-glucuronide emerged as the top binder for UDP-glucuronosyltransferase 1A1 (also known as UDP 1A1), forming stable complexes with favourable interactions. Catechin and epicatechin were identified as the best ligands for TFF1 and S100P, while rutin showed high-affinity binding to MUC13.
CONCLUSION: The study successfully identified and validated a panel of biomarkers specific to pancreatic cancer, with potential applications in early diagnosis and treatment. The findings highlight the importance of multi-omics data integration in cancer research and the potential of personalized medicine in improving patient outcomes. The in-silico drug targeting analysis provides a foundation for the development of novel drugs for PanCa treatment. Hence TFF1, S100P, MUC13, and UGT1A1 showcased themselves as most promising biomarkers and novel drug targets.
PMID:40517592 | DOI:10.1016/j.compbiomed.2025.110559
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
How AI can help make cities work better
In recent decades, cities have become increasingly adept at amassing all sorts of data. But that data can have limited impact when government officials are unable to communicate, let alone analyze or put to use, all the information they have access to.
This dynamic has always bothered Sarah Williams, a professor of urban planning and technology at MIT. Shortly after joining MIT in 2012, Williams created the Civic Data Design Lab to bridge that divide. Over the years, she and her colleagues have made urban planning data more vivid and accessible through human stories and striking graphics. Read the full story.
—Ben Schneider
This story is from the next print edition of MIT Technology Review, which explores power—who has it, and who wants it. It’s set to go live on Wednesday June 25, so subscribe & save 25% to read it and get a copy of the issue when it lands!
Inside OpenAI’s empire with Karen Hao
AI journalist Karen Hao’s newly released book, Empire of AI: Dreams and Nightmares in Sam Altman’s OpenAI, tells the story of OpenAI’s rise to power and its far-reaching impact all over the world.
Hao, a former MIT Technology Review senior editor, will join our executive editor Niall Firth in an intimate subscriber-exclusive Roundtable conversation exploring the AI arms race, what it means for all of us, and where it’s headed. Register here to join us at 9am ET on Monday June 30th June.
Special giveaway: Attendees will have the chance to receive a free copy of Hao’s book. See registration form for details.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The White House is sharing tasteless deportation memes
Its digital strategy revolves around boosting policies for cheap laughs. (WP $)
+ Trump’s immigration raids are a rapid escalation of his deportation tactics. (Vox)
+ The administration is revelling in the outraged reaction to its actions. (The Atlantic $)
+ But New Yorkers are fighting back. (New Yorker $)
2 New York is asking companies to disclose when AI contributes to layoffs
It’s the first official step towards measuring AI’s impact on the labor market. (Bloomberg $)
+ People are worried that AI will take everyone’s jobs. We’ve been here before. (MIT Technology Review)
3 Regeneron isn’t buying 23andMe after all
A non-profit controlled by its cofounder has made a higher bid. (WSJ $)
+ Anne Wojcicki says she has the backing of a Fortune 500 company. (FT $)
+ How to… delete your 23andMe data. (MIT Technology Review)
4 RFK Jr has filled the CDC’s vaccine committee with allies
Robert Malone, one of the appointees, has encouraged the public to embrace the term anti-vax. (The Atlantic $)
+ Here’s what food and drug regulation might look like under the Trump administration. (MIT Technology Review)
5 Americans are commissioning animal torture videos
The US government has revealed details of residents accused of paying people in Indonesia to abuse helpless monkeys. (Ars Technica)
6 China has conducted its first brain implant clinical trial
Making it only the second country to do so, after the US. (Bloomberg $)
+ Brain-computer interfaces face a critical test. (MIT Technology Review)
7 The US Navy wants your startup
It’s more open to partnerships than ever before, apparently. (TechCrunch)
+ China is stockpiling intercontinental ballistic missiles. (Insider $)
+ Generative AI is learning to spy for the US military. (MIT Technology Review)
8 The UK is working on a chemotherapy-free approach to treating leukaemia
Combining two targeted drugs appears to perform better. (The Guardian)
9 Brace yourself for AI sponcon
Just when you thought product placement couldn’t get any worse. (The Verge)
10 Zines are staging a comeback
Creatives are turning their backs on social media in favor of good old-fashioned booklets. (Wired $)
Quote of the day
“Being a highly “online” person is a very embarrassing thing and should be relegated to basement losers.”
—Derek Guy, aka The Menswear Guy on X, explains to Wired why he thinks a significant proportion of the Republican coalition need to step away from their keyboards.
One more thing
Bright LEDs could spell the end of dark skies
Scientists have known for years that light pollution is growing and can harm both humans and wildlife. In people, increased exposure to light at night disrupts sleep cycles and has been linked to cancer and cardiovascular disease, while wildlife suffers from interruption to their reproductive patterns, and increased danger.
Astronomers, policymakers, and lighting professionals are all working to find ways to reduce light pollution. Many of them advocate installing light-emitting diodes, or LEDs, in outdoor fixtures such as city streetlights, mainly for their ability to direct light to a targeted area.
But the high initial investment and durability of modern LEDs mean cities need to get the transition right the first time or potentially face decades of consequences. Read the full story.
—Shel Evergreen
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)
+ As commencement speeches go, Steve Jobs’ is definitely one of the best.
+ I love this iconic Homer moment recreated in Lego.
+ The remains of a beautiful Byzantine tomb complex has been uncovered between Aleppo and Damascus.
+ I want to believe: check out this short, bizarre history of alien abductions in America 
Signal Transduct Target Ther. 2025 Jun 15;10(1):186. doi: 10.1038/s41392-025-02243-6.
ABSTRACT
Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substantial unmet needs remain for patients worldwide, as drug resistance persists as an inevitable reality. Meanwhile, the journey towards amplifying the breadth and depth of the therapeutic effect requires comprehending and integrating diverse and profound progress. In this review, therefore, we aim to comprehensively present such progress that spans the various aspects of molecular pathology, encompassing elucidations of metastatic mechanisms, identification of therapeutic targets, and dissection of spatial omics. Additionally, we also highlight the numerous small molecule and antibody drugs, encompassing their application alone or in combination, across later-line, frontline, neoadjuvant or adjuvant settings. Then, we elaborate on drug resistance mechanisms, mainly involving targeted therapies and immunotherapies, revealed by our proposed theoretical models to clarify interactions between cancer cells and a variety of non-malignant cells, as well as almost all the biological regulatory pathways. Finally, we outline mechanistic perspectives to pursue innovative treatments of NSCLC, through leveraging artificial intelligence to incorporate the latest insights into the design of finely-tuned, biomarker-driven combination strategies. This review not only provides an overview of the various strategies of how to reshape available armamentarium, but also illustrates an example of clinical translation of how to develop novel targeted drugs, to revolutionize therapeutic landscape for NSCLC.
PMID:40517166 | PMC:PMC12167388 | DOI:10.1038/s41392-025-02243-6
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