A few years ago, the business technology world’s favourite buzzword was ‘Big Data’ – a reference to organisations’ mass collection of information that could be used to suggest previously unexplored ways of operating, and float ideas about what strategies they may best pursue.

What’s becoming increasingly apparent is that the problems companies faced in using Big Data to their advantage still remain, and it’s a new technology – AI – that’s making those problems rise once again to the surface. Without tackling the problems that beset Big Data, AI implementations will continue to fail.

So what are the issues stopping AI deliver on its promises?

The vast majority of problems stem from the data resources themselves. To understand the issue, consider the following sources of information used in a very average working day.

In a small-to-medium sized business:

• Spreadsheets, stored on users’ laptops, in Google Sheets, Office 365 cloud.
• The customer relationship manager (CRM) platform.
• Email exchanges between colleagues, customers, suppliers.
• Word documents, PDFs, web forms.
• Messaging apps.

In an enterprise business:

• All of the above, plus,
• Enterprise resource planning (ERP) systems.
• Real-time data feeds.
• Data lakes.
• Disparate databases behind multiple point-products.

It’s worth noting that the simple list above isn’t comprehensive, and nor is it intended to be. What it demonstrates is that in just five lines, there are around a dozen places where information can be found. What Big Data needed (perhaps still needs) and what AI projects also rest on, is somehow bringing all those elements together in such a way that a computer algorithm can make sense of it.

Marketing behemoth Gartner’s hype cycle for artificial intelligence, 2024, placed AI-Ready Data on the upward curve of the hype cycle, estimating it would be 2-5 years before it reached the ‘plateau of productivity’. Given that AI systems mine and extract data, most organisations – save those of the very largest size – don’t have the foundations on which to build, and may not have AI assistance in the endeavour for another 1-4 years.

The underlying problem for AI implementation is the same as dogged Big Data innovations as they, in the past, made their way through the hype cycle – from innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, to plateau of productivity – data comes in many forms; it can be inconsistent; perhaps it adheres to different standards; it may be inaccurate or biased; it could be highly sensitive information, or old and therefore irrelevant.

Transforming data so it’s AI-ready remains a process that’s as relevant today (perhaps more so) than it’s ever been. Those companies wanting to get a jump start could experiment with the many data treatment platforms currently available, and as is becoming the common advice, might begin with discrete projects as test-beds to assess the effectiveness of emerging technologies.

The advantage of the latest data preparation and assembly systems is that they are designed to prepare an organisation’s information resources in ways that are designed for the data to be used by AI value-creation platforms. They can offer, for example, carefully-coded guardrails that will help ensure data compliance, and protect users from accessing biased or commercially-sensitive information.

But the challenge of producing coherent, safe, and well-formulated data resources remains an ongoing issue. As organisations gain more data in their everyday operations, compiling up-to-date data resources on which to draw is a constant process. Where big data could be considered a static asset, data for AI ingestion has to be prepared and treated in as close to real-time as possible.

The situation therefore remains a three-way balance between opportunity, risk, and cost. Never before has the choice of vendor or platform been so crucial to the modern business.

(Source: “Inside the business school” by Darien and Neil is licensed under CC BY-NC 2.0.)

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Oncogene, Published online: 21 October 2025; doi:10.1038/s41388-025-03605-8

Meflin is a druggable target using antibody-drug conjugates in progressive osteosarcoma

npj Digital Medicine, Published online: 21 October 2025; doi:10.1038/s41746-025-02082-3

Although still at a nascent state, quantum computing promises advances in healthcare, from drug discovery to personalised treatments. But it also threatens current cryptographic systems that protect medical data and infrastructure. The concept of “Q-Day” highlights risks such as “harvest now, decrypt later” attacks, with particular concerns for medical devices and sensitive applications in fields like femtech. Preparing for this future requires the rapid adoption of post-quantum cryptography, the coordination of time-phased and scalable “technology rollout” strategies, and revised regulatory frameworks to safeguard patient safety, privacy, and trust.

To architect effectively for an MVP, teams must answer three questions in order: Is the business idea worth pursuing? What performance and scalability are needed? How much maintainability and supportability are required? These guide Minimum Viable Architecture decisions. Empirical testing helps reject costly assumptions early and adapt architecture as the MVP evolves.

By Pierre Pureur, Kurt Bittner

Nature Biotechnology, Published online: 21 October 2025; doi:10.1038/s41587-025-02856-w

Multiplexed editing in primary human T cells generates enhanced immune cell therapies.

Nature, Published online: 21 October 2025; doi:10.1038/d41586-025-03430-9

Take the time to ensure that AI is safe

BERLIN — For years, scientists have held out hope that tests that look for the molecular fingerprints of a cancer’s presence could help clinicians determine which patients need further treatment after surgery, and which can be considered truly cured after an operation. Perhaps, the thinking goes, those in the latter category could be spared from unnecessary, expensive therapies that can carry serious side effects.

A study presented here Monday bolstered the case that such tests, which detect circulating tumor DNA, or ctDNA, that cancer cells slough off into the blood, could be effective tools. 

Researchers used the diagnostics to identify patients who appeared to have remnants of muscle-invasive bladder cancer after surgery, and directed them to treatment. Those who tested negative were allowed to live treatment-free, and were ultimately found to be at low risk of recurrence. 

Continue to STAT+ to read the full story…

© STEVE GSCHMEISSNER / SCIENCE PHOTO LIBRARY

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Abridge vs. OpenEvidence, round one

New announcements from Abridge and OpenEvidence,  two of the most prominent health tech startups to emerge in recent years, highlight how despite different initial offerings, many artificial intelligence companies in health care will just end up competing with each other for physician eyeballs.

On Monday morning, Abridge, best known for its AI scribe that helps doctors automate the writing of clinical notes, announced a new product that will surface “real-time insights, prompts, and pathways” from the widely-used medical resource UpToDate based on things said in a clinical conversation and that are written in the patient’s record. 

Continue to STAT+ to read the full story…

© ADOBE, Alex Hogan/STAT

Background: Obesity is a chronic, relapsing disease influenced by environmental, lifestyle, biological, and genetic factors, affecting over 1 billion people globally. Treatment for adults typically involves multicomponent lifestyle interventions—diet, physical activity, and behavior change—for at least 6-12 months. However, adherence is often low, and in-person sessions can be time-consuming and costly. Digital therapeutics (DTx), which enhance patient engagement and support long-term outcomes, have proven effective in managing chronic and mental health conditions. DTx offer scalable, evidence-based solutions with the potential to improve obesity management. Objective: The Digital Therapy to Promote Weight Loss in Patients With Obesity by Increasing Their Adherence to Treatment (DEMETRA) study is a prospective, multicenter, pragmatic, randomized, double-arm, single-blind, placebo-controlled trial evaluating the 6-month efficacy of an innovative, multicomponent digital intervention for obesity, which combines dietary, physical activity, and behavioral strategies in people with obesity (primary objective). Secondary objectives were assessing changes in BMI, waist circumference, blood pressure, glucose metabolism, lipid profile, adherence, and factors associated with absolute 6-month weight loss. Methods: The trial was conducted at 2 obesity centers in Italy with 246 participants aged 18-65 years (BMI 30-45 kg/m2), randomly assigned to either the Digital Therapeutics for Obesity (DTxO) app or a placebo app. DTxO offered personalized diet plans, exercise routines, and psycho-behavioral support, while the placebo app only allowed users to log data without feedback. Both groups followed a Mediterranean-style low-calorie diet with an 800 kcal/day deficit. On average, participants used the DTxO app for 42 minutes/day and the placebo app for 35 minutes, primarily for physical activity tracking. Univariable and multivariable generalized linear models were used to assess associations with 6-month absolute weight change (primary end point) and percent weight change (secondary end point). Results: Overall, 207 participants (84.1%) completed the 6-month visit. Both arms achieved a statistically significant absolute (DtxO: –3.2 kg, IQR –6.0 kg to –0.9 kg; placebo: –4.0 kg, IQR –6.9 kg to –0.5 kg; P<.001) and percent loss in body weight (DtxO: –3.0%, IQR –5.7% to –0.8%; placebo: –4.0%, IQR –8.5% to –0.5%; P<.001) after 6 months, without significant between-group differences (univariable generalized linear models: P=.34 and P=.17, respectively). Univariable regression analyses showed a significant association between adherence to app use and 6-month absolute weight loss (β=–.06, SE 0.02, P=.01) as well as percent weight loss (β=–.05, SE 0.01, P=.01). Adherent participants, defined as those with overall adherence at or above the 75th percentile of daily usage, included 35 individuals in the intervention group and 10 in the placebo group. In this subgroup, the estimated 6-month mean absolute weight change was –7.02 kg (95% CI –9.45 to –4.59) in the DTxO-adherent group and –3.50 kg (95% CI –7.01 to 0.01) in the placebo-adherent group (P=.02). The estimated 6-month mean percent change in weight was –6.31% (95% CI –8.86 to –3.76) in the DTxO-adherent group and –2.78% (95% CI –6.48 to 0.92) in the placebo-adherent group (P=.03). A significantly greater weight loss (P=.01 for study arm, either on absolute or percent change in weight from baseline) among adherent participants randomized to the DTxO app was also confirmed by analyses using mixed linear models for repeated measures. Conclusions: Although overall weight loss did not differ significantly between the DTxO and placebo groups, participants who used the DTxO app for at least 40% of the expected time achieved significantly greater weight loss. These results suggest that higher engagement with DTx can improve obesity outcomes. Further research should explore combining DTxO with pharmacological treatments or bariatric surgery. Trial Registration: ClinicalTrials.gov NCT05394779; https://clinicaltrials.gov/ct2/show/NCT05394779

Background: Retrieval-augmented generation (RAG) is increasingly used to improve large language models in the medical and nursing domains. However, a comprehensive understanding of its specific architecture and applications in medical and nursing reasoning remains limited. Objective: We aimed to summarize the current state, existing limitations, and future development directions of RAG in the medical and nursing domains. Methods: The PubMed, Web of Science, IEEE Xplore, and arXiv databases were searched for relevant articles using queries that combined terms related to RAG, medical, and nursing domains, covering the period from November 1, 2022, to May 31, 2025. This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Results: A total of 917 articles were retrieved, of which 67 met the inclusion criteria. Most studies focused on the medical domain (63/67, 94%), while only a few addressed nursing applications (4/67, 6%). The RAG frameworks included in this review were categorized into 5 functional types: text-based RAG (36/67, 54%), knowledge graph–enhanced RAG (17/67, 25%), agentic RAG (6/67, 9%), multimodal RAG (2/67, 3%), and plug-and-play RAG (6/67, 9%). On the basis of the Simon decision-making process theory, we divided the RAG workflow into 4 stages: intent recognition, knowledge retrieval, knowledge integration, and generation. Only 26 studies included explicit reasoning support, and few were aligned with real-world clinical workflows. Only 12 studies attempted to address ethical considerations related to RAG. Conclusions: We identified 4 key shifts in recent RAG development: shifting from surface-level matching toward contextualized intent recognition, from vague semantics toward logic-driven dynamic retrieval, from passive toward active knowledge retrieval, and from simple aggregation toward coherent context construction. However, most RAG systems in the medical and nursing domains have not yet introduced reasoning methods, and those that have are still predominantly reliant on data‑driven associations without causal modeling. This highlights the need to integrate causal mechanisms for more effective and domain-relevant reasoning in health care. Trial Registration: OSF Registries 10.17605/OSF.IO/WBSV5; https://osf.io/wbsv5

(MedPage Today) -- BERLIN -- Survival in high-risk bladder cancer improved significantly with use of circulating tumor (ct)DNA to guide adjuvant treatment in patients who tested positive after surgery, the randomized, placebo-controlled IMvigor011...