Research Implementation Challenges

Microsoft just released a 35-page report on medical AI - and it’s a reality check for healthcare. The paper, “The Illusion of Readiness”, tested six of the most popular models (OpenAI, Gemini, etc)… across six multimodal medical benchmarks. And the verdict? The models scored high on medical exams. But they’re not even close to being real-world ready. Here’s what the stress tests revealed: ▶ 1. Shortcut learning Models often answered correctly even when key information, like medical images, was removed. They weren’t reasoning - they were exploiting statistical shortcuts. That means benchmark wins may hide shallow understanding. ▶ 2. Fragile under small changes Making small tweaks caused big swings in predictions. This fragility shows how unreliable model reasoning becomes under stress. In visual substitution tests, accuracy dropped from 83% to 52% when images were swapped - exposing shallow visual–answer pairings. ▶ 3. Fabricated reasoning Models produced confident, step-by-step medical explanations - but many were medically unsound… or entirely fabricated. Convincing to the eye, dangerous in practice. And more importantly, healthcare isn’t a multiple-choice exam. It’s uncertainty, incomplete data, and high stakes. So Microsoft’s team calls for new standards: - Stress tests that expose fragility - Clinician-guided guidelines that profile benchmarks - Evaluation of robustness and trustworthiness - not just leaderboard scores The takeaway is simple: Medical AI may ace tests today. But until it proves reliable under stress, it’s not ready for the clinic. When do you think popular LLMs will be clinic-ready? #entrepreneurship #healthtech #AI

𝗪𝗵𝘆 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝟵𝟬% 𝗼𝗳 𝗔𝗜 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗳𝗮𝗶𝗹 𝗯𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲𝘆 𝗿𝗲𝗮𝗰𝗵 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 It's not the models. It's not the data. It's the architecture. Across the industry, brilliant engineers build AI prototypes that work perfectly in Jupyter notebooks... then spend 6 months trying to productionize them. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? Most AI projects start as experiments and never graduate to engineered systems. Here's what separates successful AI implementations from failures: 𝟭. 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻 𝗛𝗲𝗹𝗹 When API keys, model parameters, and prompt templates are scattered across 12 different files, deployment becomes a nightmare. Successful teams separate their config completely from day one. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗧𝗿𝗮𝗽 Teams treat prompts like throwaway code. Wrong. Your prompts ARE your product logic. Version them, test them, and organize them like the critical business logic they are. 𝟯. 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 That beautiful demo hitting OpenAI 100 times per second? It'll cost $500/day in production. Smart teams build rate limiting from day one, not as an afterthought. 𝟰. 𝗧𝗵𝗲 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗕𝗹𝗶𝗻𝗱𝘀𝗽𝗼𝘁 Companies regularly spend $10K/month on API calls for repetitive queries. Intelligent caching can cut AI costs by 70%. 𝗧𝗵𝗲 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻? Start with production architecture, not prototype architecture.

Building a business in preventive health is hard. No one tells you this upfront. I learned it the hard way, first as a founder, now as an investor. You’ve chosen the harder game. Not because prevention doesn’t work, but because people don’t pay for it. Most wake up thinking about chai, parathas , deadlines, and school fees. Not long-term health. People today want instant gratification—sugar today, gym tomorrow. Curative health makes money because illness is urgent. No one postpones a bypass surgery or negotiates an ICU bill. But prevention? Everyone hunts for gym discounts, coupon codes for healthy food and thinks twice before paying for a fitness program. Hospitals & pharma companies thrive because they sell relief from suffering. And suffering is a guaranteed market—prevention is not. That’s why venture money chases curative health—it scales fast with clear unit economics, solving problems people can’t ignore. Asking someone to eat better, sleep more, and exercise for a payoff 5–10 years later is like selling an FD over a lottery ticket. So accept this reality and build accordingly. Yet, prevention is the hardest problem and the biggest untaped opportunity. #1 Healthcare in India will shift to prevention-not because people suddenly care, but because not caring is becoming too expensive. #2 Your market isn’t TAM, it’s WTM (willing-to-pay market). Everyone should care about health, but few will pay for it.To go mass-market, make it cheaper than chai and as seamless as WhatsApp. #3 If you’re selling prevention, reposition it. People don’t buy prevention. They buy status, performance, and convenience. Make it aspirational & competitive. #4 Trust in health isn’t built overnight. People won’t change their habits because you raised venture money. You’re in the business of delayed gratification. That means slow &sustainable growth. #5 If your product doesn’t make people healthier or keep them engaged long-term, the number of app downloads doesn't matter. Focus on retention, real outcomes and revenue—not GMV, DAUs or metrics to make a pitch deck look good. If you can do this, you won’t just build a business—you’ll change how India thinks about health. And that’s worth building for.

🗃️ The Eight Pillars Of User Research (+ PDF) (https://lnkd.in/dxM2Tq7e), a comprehensive model with roles, tools and processes to deliver and scale UX research impact across the organization. By Emma Boulton. PDF: https://lnkd.in/eeg_uDfv Nothing is more misleading than poorly executed research that supports big claims or delivers big statements. I love how Emma’s model organizes research around critical dimensions — which all need to work together to make research more effective and more impactful. 🏭 1. Environment “Why research happens, who is engaged?” We start by understanding why research happens and what’s the level of maturity. We look where it happens, how to get a buy in, how to push back. We also track stakeholders and colleagues who will carry UX research. 🔬 2. Scope “How and when research happens?” Next, we define the bulk of work — the cadence and how insights are shared, how we prioritize UXR projects, integrate insights. We explore processes, methods, protocols — and establish guidelines and templates. 📅 3. Recruitment and admin “How do we manage projects and participants?” Good research rarely happens ad hoc. Ideally, a research recruitment desk would handle the full sweep from sampling and screening, to scheduling and paying incentives, to closing the loop with participants. 🗃️ 4. Data and knowledge management “What happens to the findings, data, insights?” Valuable insights get lost as teams grow and get restructured. We look into data gardening and knowledge management to make insights actionable. We also shape how we communicate research, so it can be impactful. 🤹 5. People “Who is carrying out research?” For research to happen, we need to build capability for it. That's where we explore the skills and roles needed that would have the highest effectiveness and impact — along with staffing, and establishing a community of practice. 🏢 6. Organisational context “What are internal and external constraints?” We track space, time, resources, budgets and spending in a systematic way. We explore how research activities fit into existing business constraints and market forces — defining the timeframe we have to operate within. 🏛️ 7. Governance “What are legal and ethical considerations?” Depending on legal requirements, some type of research is possible or not. We need to do due diligence as of risk assessments, GDPR, legal, data security and consent to enable research to safe, legal and ethical. 🛠️ 8. Tools and infrastructure “What systems and tools do we use?” Somebody has to deal with procurement, licensing, tooling and management of software and hardware. It can take an enormous amount of time, and often is a big blocker in organizations. Now that’s a useful model to take UX research initiatives off the ground, from scratch. Thanks for sharing, Emma and ReOps community! 👏🏼👏🏽👏🏾 ↓

Antibiotics have saved billions of lives. They are one of the most powerful medical discoveries in history. But their power is at risk. Antimicrobial resistance (#AMR) is rising fast. It could cause up to 10 million deaths annually by 2050. And yet, it has been over 50 years since a novel chemical class of antibiotic was approved for the life-threatening infections at the top of the World Health Organization's priority list. Why? Because developing new antibiotics is one of the toughest challenges in science. There are reasons: ➡️ Bacteria evolve rapidly, developing resistance through multiple mechanisms. ➡️ Bacteria and human cells share many biological processes, making it hard to target them without harming human cells. ➡️ Their outer membrane blocks many compounds, making drug design incredibly complex. It’s not just science though. It’s also economics. Developing antibiotics involves enormous investment and high risk. Without the right incentives, innovation stalls. At Roche, we are committed to addressing all these challenges. We continue to invest in antibiotics R&D. One of our candidates for a novel antibiotic is now entering phase III — the final stage of clinical development. And there is even more: We’re also advancing diagnostics in this area — to ensure we use the right antibiotic, for the right patient, at the right time. Fighting antimicrobial resistance is one of the greatest challenges of our time, and it is more important than ever. #AntimicrobialResistance #Innovation #AMR

Scientists can’t agree on where the world’s forests are A deceptively simple question underlies many global environmental policies: where, exactly, are the world’s forests? A new study suggests the answer depends heavily on which map one consults—and that the differences are large enough to reshape climate targets, conservation priorities, and development spending. Researchers Sarah Castle, Peter Newton, Johan Oldekop, Kathy Baylis, and Daniel Miller compared ten widely used global forest datasets derived from satellite imagery. These products underpin everything from carbon accounting to biodiversity assessments. Yet they rarely agree. Across areas identified as forest by at least one dataset, only about 26% was classified as forest by all of them. Even after adjusting maps to a common spatial scale, agreement improved only modestly. The divergence stems partly from definitions. Some datasets treat sparsely wooded landscapes as forest, while others require dense canopy. A 10% canopy threshold, for instance, includes savannas and open woodlands; a 70% threshold captures only closed forest. Resolution also matters: high-resolution imagery can detect narrow riparian strips or small fragments that coarser data overlook. Differences in sensors, algorithms, and training data introduce further variation. Patterns of disagreement are uneven. Moist tropical forests, where tree cover is continuous, show relatively high consistency. Dry forests and fragmented landscapes show far less, with some biomes reaching consensus on as little as 12% of forested area—often where conservation decisions are most contested. Case studies illustrate the practical consequences. In Kenya, estimates of forest carbon ranged from roughly 2% to 37% of national biomass carbon depending on the dataset used. Maps that produced similar totals did not necessarily agree on where carbon was stored, complicating mitigation planning. In India, estimates of forest-proximate people living in poverty ranged from about 23 million to more than 250 million using identical socioeconomic data but different forest maps. In Brazil, even datasets tracking forest loss overlapped on less than half of mapped deforestation affecting habitat for the endangered white-cheeked spider monkey. Satellite-derived maps now form the empirical backbone of environmental governance. Governments use them to report climate progress, NGOs to target interventions, and investors to assess nature-related risk. The study does not identify a single “correct” dataset. Instead, it urges treating forest estimates as ranges, testing results across multiple products, and improving standardization. Before deciding how to manage forests, policymakers may first need to agree on where they are. 🌳 Full piece: https://mongabay.cc/uf6jMn 🔬 Paper: https://lnkd.in/g_UbxRjk

Last week Anthropic released its latest Economic Index. A key takeaway is that frontier models performance is heavily dependent on the context they can access. For example, the report notes that for Claude to develop a sales strategy for a key account, it needs more than CRM data. It also needs the tacit knowledge held by account executives, marketers, and external partners—knowledge that rarely lives in structured databases. In Anthropic’s words, “All else equal, lacking access to such contextual information will make Claude less capable.” Anthropic also found that when API customers rely on Claude for complex tasks, they tend to provide it with lengthy inputs. Relying on employees to paste sprawling context into prompts slows adoption, degrades results, and frustrates teams. The report warns, “This could represent a barrier to broader enterprise deployment for some important tasks that rely on dispersed context.” For years, we’ve heard the mantra: AI is only as strong as its data. But today, the bigger limiter is context. AI is only as strong as the organizational context it can securely access: across your data, processes, tools, and the tacit knowledge of your people. Without that complete map of your organization’s collective intelligence, even the most powerful models hit a wall.

I'm convinced the Forward Deployed Engineer role will continue to grow. The hardest part of Enterprise AI isn't the model. It’s the "Head Knowledge" transfer. As a Forward Deployed Engineer, my job isn't just to write code; it’s to extract the implicit rules living in the heads of Subject Matter Experts (SMEs) and turn them into logic an agent can actually act upon. Here’s the reality: You can’t just quote "LLM benchmarks" to a guy running a 1000° aluminium smelter. Why? Because they know the stakes. You can't trust an LLM to decide the quality of an anode when a mistake means a catastrophic failure. They don't want to hear your "bullish" predictions; they want to see your proof. SMEs will call out your bullshit in seconds. The only way to win in this role is to build trust through consistency and hard data. You prove the work, you show the edge cases, and you respect the "head knowledge" that’s kept that factory running for 30 years. I’m bullish on the FDE role because we are the bridge. We don't just talk about AI but we make sure it survives in real factories. PS: If you're interested about this role and what tradecrafts make a good FDE, I'm thinking about collecting all this knowledge together. Of course, I'm still learning but this will come out soon.

I've spent 10 years helping scientists communicate their research. I've never once thought that doing so could put their funding at risk. Until now. The US Office of Management and Budget has proposed a rule (Docket OMB-2026-0034) that, if it passes in October 2026, would fundamentally change how science gets funded in America. And I mean fundamentally. Here are the key points: 🔴 Political appointees, not scientists, decide which grants get funded. Peer review becomes advisory only. A political appointee can override the scientific community's judgment with zero explanation. 🔴 Active grants can be cancelled at any time, for any reason. Mid-project. No misconduct required. Just "doesn't align with our priorities anymore." 🔴 International collaboration is severely restricted. Travel, partnerships, joint research with scientists in dozens of countries: presumptively banned or requiring political sign-off. 🔴 Publication fees are unallowable. That means federally funded scientists may not be able to publish their findings Open Access. And then there's the one that hit me hardest as a science communicator: 🔴 Science communication could effectively be banned for federally funded researchers. The proposed rule prohibits using federal grant funds for any public communications that could be labelled "issue advocacy." Given that the rule's own preamble classifies climate science, public health research, and DEI research as "divisive ideology," a researcher speaking publicly about their own federally funded findings on any sensitive topic puts their entire grant at risk. NOTE: this is not yet law. The public comment period is open until approximately 13 July 2026. But this is what's being proposed, right now, for one of the most influential research ecosystems on the planet. At Animate Your Science, our mission is to help researchers communicate their work in ways that reach beyond journal paywalls and conference rooms. That mission only matters if scientists are allowed to communicate. What's being proposed here is a direct threat to that. I'll keep making noise about it. Please read and share the original breakdown by Elizabeth Ginexi. It is detailed and calm. And sadly it is worse than this post makes it look. 👉 https://lnkd.in/gNT2stY6

Alignment without context integrity is not safety. An AI agent can faithfully follow its instructions and still be dangerous if its understanding of reality is wrong. Anthropic recently disclosed that Claude models gained unauthorized access to the real systems of three organizations during cybersecurity evaluations. The agents had been told they were operating in a simulation with no internet access. But a configuration mistake gave them access to the live internet. They treated real production systems as part of the exercise and kept pursuing the goal they had been given. OpenAI separately disclosed that models found a previously unknown vulnerability, escaped an isolated evaluation environment and compromised Hugging Face. These were not simply failures of intelligence. The deeper problem was that the agents were acting inside a false understanding of reality. We have spent years asking whether an AI system will follow our instructions. We now also need to ask whether it correctly understands the environment in which those instructions are being executed. This creates a new security requirement. Context integrity. Before an agent acts, the system must continuously verify where it is, which resources are in scope, whose authority it carries, what it is allowed to do, and when that authority expires. Just in time permission for every action. At just the right time. For just enough time. Assessed in real time. Those facts cannot live only inside a prompt. They must be verified and enforced by the infrastructure around the model. A prompt is not a security boundary. Zero trust taught us to never trust identity and always verify access. And provide least privileged access. Agentic AI adds another dimension. Never blindly trust context. Continuously verify reality. The next security perimeter is not just the agent’s identity. It is the agent’s understanding of reality. The most dangerous agent may not be misaligned. It may simply be mistaken. And in an agentic world, a false belief can become a real breach.