Author name: Dr. Rania Kassir

Google’s late-February 2026 launch of Nano Banana 2, also described by Google and some coverage as Gemini 3.1 Flash Image, can look like a routine model update, but in clinics and research groups it behaves more like a workflow shift. It lands amid familiar production pressures: recruitment materials constrained by IRB timelines, conference figures that must stay legible at a distance, patient infographics that simplify without distorting, and teaching slides that communicate risk responsibly. Google’s core claim is that it pairs high image quality with Flash-level speed, now accessible via the Gemini app and Google Search experiences such as AI Mode and Lens, with explicit rollout messaging for MENA. When image generation becomes both fast and visually persuasive, the change is not only higher output, it is faster decision cycles. Google highlights features that map directly onto scientific and clinical communication: improved text rendering inside images, stronger subject consistency across elements, aspect-ratio control, and outputs up to 4K. In practice, that can mean fewer “manual design” iterations for medication titration visuals, consent-process diagrams, visual abstracts, and explanatory schematics, especially in teams without dedicated design support. One especially consequential claim is that Nano Banana 2 can draw on real-time information and images from web search to improve accuracy and support diagrams and infographics. The upside is immediate: clearer visuals produced quickly, potentially improving patient comprehension and reducing cognitive load for trainees. The methodological tension is equally real: if “grounding” relies on sources that are not clearly surfaced, archived, and citable, we gain speed while losing traceability. The risk is not only that an image could be wrong, but that we may not be able to reconstruct why it looks convincingly right. That tension sharpens with the “across Gemini, Search, and Ads” framing. The most clearly documented integrations at the moment are Gemini and Search. The Ads direction is plausible in context, Google Ads already supports generative image workflows with explicit guidance that advertisers must review AI-generated assets before publishing, and Google has previously described bringing Gemini models into Performance Max, but reports that Nano Banana 2 will directly power creative suggestions in Ads should be treated as a reported trajectory until Google’s Ads documentation names the model and scope explicitly. For health researchers and clinicians, this matters because the same leap that improves patient education materials can also accelerate persuasive health content optimized for clicks and conversion. As visual polish becomes cheaper, it becomes even less correlated with truth, shifting the burden of appraisal onto audiences who often have the least time and the most at stake. The most practical summary is simple: Nano Banana 2 compresses creation time, but it does not remove responsibility, it relocates it. Responsible teams will spend less time drafting and more time verifying: ensuring diagrams encode the correct causal claim, risk visuals match guideline thresholds, translations preserve meaning, and public-facing graphics do not outrun the evidence. The weak point is institutional: many organizations still lack lightweight governance for “AI-assisted visuals,” even when they have mature controls for medication orders, patient instructions, and research data outputs. Ethically, better tools raise, not lower, the bar on three duties. Transparency matters when provenance affects trust (patient materials, public education, high-impact research communication). Data integrity and auditability matter when web-grounded generation influences content and sources are not recoverable. Privacy discipline remains non-negotiable: identifiable patient details should not become prompt ingredients to produce “better” visuals. Watermarking and content credentials can help, but they are not substitutes for domain review when the content is medical.

Clinical and research work rarely fails because we cannot locate information; it fails because we cannot convert information into usable outputs quickly enough. The friction lives in the in‑between tasks: preparing a background brief for a case conference, turning scattered notes into a coherent protocol amendment, cleaning citations before a resubmission, or drafting a patient-facing handout that is both readable and accurate. What is newly compelling about the idea of an “AI computer” is that it aims at this connective tissue of work, not simply at conversation. Perplexity describes Computer as a unified, cloud-based system that can “research, design, code, deploy, and manage” projects end-to-end, breaking a goal into sub-tasks and routing them across specialized components. In the public description, it can orchestrate work across 19 models in parallel, “matching each task to the best model,” while also remembering prior context and connecting to external services. The ambition is not subtle: it is a bid to turn the browser into an operational workspace where the user specifies intent and the system performs the multi-step work. The attraction is easy to understand in real workflows. We already practice constrained delegation every day: we assign parts of a project to trainees, research assistants, administrators, or colleagues, and we integrate, verify, and sign off. An agentic system promises a similar pattern, but at a different speed and scale. If it performs reliably, it may create time for what remains stubbornly human: therapeutic presence, clinical judgment under uncertainty, nuanced supervision, and careful interpretation of evidence. That tension becomes sharper when orchestration is invisible. If Computer decomposes a task into steps, chooses tools, and merges results, then provenance matters: which sources were used, which model generated which claim, and what was the system’s uncertainty at each step? In research, these details determine whether a literature synthesis is reproducible; in clinical settings, they determine whether a handout, policy memo, or documentation aid stays within the boundaries of evidence-based practice. The more autonomous the workflow, the more we need systems that make their reasoning legible rather than merely impressive. A second tension is provenance. In research, we need to know what sources were used, how claims were derived, and what uncertainty remains, because the credibility of a synthesis depends on traceable reasoning. In clinical environments, provenance is equally important, though less often formalized: we need to know whether an output is grounded in guidelines, high-quality trials, local policy, or merely plausible generalizations. Agentic tools can compress steps so efficiently that they also compress our visibility into where a conclusion came from. Cost pulls these questions from theory into daily decision-making. At roughly $240 per month, this is not an impulsive subscription for most clinicians; it is closer to a staffing tradeoff. Paying that amount implicitly assumes that time saved is both substantial and dependable, and that the time we spend verifying the output does not quietly re-inflate the workload. In clinical settings, the “true cost” includes not only money, but also the cognitive burden of oversight and the reputational risk of errors. From a practice perspective, the safest near-term uses are those that keep identifiable data out of the system and keep verification firmly human. Drafting non-identifying psychoeducation templates, creating training materials for interns, turning internal procedures into clearer language, or generating first-pass outlines for research documents can be sensible, provided we treat outputs as drafts and insist on source checking. The risk profile changes sharply when we move toward identifiable case details or highly specific clinical recommendations, especially in small communities or rare presentations where re-identification can be easier than we like to admit. We also need to acknowledge a quieter limitation that experienced researchers recognize: these tools can accelerate the appearance of scholarship. They can produce coherent framing, persuasive prose, and tidy synthesis even when the evidence base is mixed or contested. The danger, then, is not only “hallucination” in the headline sense; it is routine overconfidence, particularly under deadline pressure, fatigue, or institutional incentives that reward speed over carefulness. Ethically, we should treat agentic systems as a new layer of professional delegation that demands transparency and documentation habits. If AI materially shaped an output that informs care (a clinic policy, a patient handout, a decision support memo), the clinician’s responsibility is not reduced; it is reconfigured. We owe patients and colleagues a disciplined stance on what data entered the system, what sources were relied upon, and how claims were checked. This is consistent with broader AI-risk frameworks emphasizing lifecycle governance: mapping likely failures, setting boundaries, and building habits of verification rather than relying on good intentions. Looking ahead, the question is not whether “AI computers” will become more common; they likely will. The more important question is whether they become legible: systems that make their sources, assumptions, and limitations visible enough for clinical and research cultures that depend on auditability and trust. If we adopt them thoughtfully, starting with low-risk tasks, measuring time saved against time spent verifying, and maintaining strict boundaries around sensitive data, we can treat these tools as assistants rather than authorities, and preserve the integrity of our work while reducing avoidable friction.

As AI systems enter clinical workflows, terminology needs to become operational rather than rhetorical. An AI agent refers to a model-based software system capable of planning and executing multi-step tasks toward a goal, including selecting actions, using external tools, and revising steps based on outcomes. In other words, it’s more than a chatbot reply: it’s a goal-directed workflow runner. This definition matters because a lot of confusion starts when we treat any fluent system as “agentic.” Google DeepMind just dropped a long, ~42-page warning about why most “AI agencies” will fail in the real world, and when we read Intelligent AI Delegation (published February 12, 2026), it quietly explains the same thing from first principles. The uncomfortable takeaway is simple: a lot of what’s marketed as “agents” today won’t survive contact with reality. Not because they can’t write or plan, but because they can’t delegate in the way real systems require. And therapy is exactly where that gap becomes visible early. Here’s the part that stings: most agents today aren’t really agents in the strong sense people imagine. They’re task runners with good branding, we give them a goal, they break it into steps, call tools, and return an output. That can be powerful automation, and it can absolutely create efficiency in low-stakes work. But it’s not delegation, it’s a prettier pipeline. And in healthcare, “pretty pipelines” are exactly how hidden risk slips into daily practice. So what do we mean when we say “delegation”? Delegation isn’t one thing. It can mean delegating execution (the system carries out steps you already chose), delegating workflow control (it sequences tools, retries, and manages handoffs), or delegating judgment (it decides what should be done and when, based on interpretation of context and risk). Most systems marketed as “agentic” are mostly capable of execution and some workflow control, not delegation where judgment is offloaded and accountability meaningfully shifts. In therapy, the danger is when we unintentionally delegate judgment while assuming we only delegated execution. DeepMind’s brutal point is that real delegation isn’t just splitting tasks. Delegation is transferring authority, responsibility, accountability, and trust, and doing it dynamically as the situation changes. That means we don’t only ask, “Who can do this fastest?” We ask, “Who should be trusted to do this, under these constraints, with these consequences?” Almost no current system behaves that way end-to-end once multiple tools, uncertain data, and real stakes are involved. Before delegation even happens, the framework implies we need to evaluate capability, risk, cost, verifiability, and reversibility. In other words, it’s not “does the agent have access to the calendar tool?” It’s “is this task safe to hand off, can we verify the result, and can we undo harm if it’s wrong?” That is a very clinical way of thinking, and it’s exactly why this paper hits therapists harder than it hits casual tech demos. Our work already assumes risk, not perfection. Therapy is not just information work; it’s relational work under ethics, confidentiality, and duty-of-care. Our clients don’t experience our work as “outputs”, they experience it as trust, safety, timing, attunement, and boundaries. So when an AI system is framed as an “agent,” the real question becomes: what are we delegating, and what are we accidentally outsourcing? If we let a system drift into clinical judgment, we may be delegating more than we think. When AI agency fails in our context, it often fails without obvious nonsense. It fails quietly: a note becomes subtly distorted, a risk signal gets downplayed, a client message becomes overly confident, or a safety-plan phrase becomes too generic to be safe. The output can look coherent and professional, sometimes more professional than we would write at 6 p.m. and that’s precisely what makes it dangerous. Fluency can create false reassurance, and false reassurance is a clinical risk. Current limitations of AI agents (especially in real workflows) Even when agents perform specific tasks well, they often struggle with coordination and interoperability, working reliably across tools, policies, datasets, and other agents. Multi-agent setups can become brittle: one agent’s assumption becomes another agent’s “fact,” tool errors propagate, and responsibility becomes hard to trace. This matters because real clinics aren’t single-tool sandboxes; they’re multi-system environments with partial data and shifting constraints. The limitation isn’t just intelligence, t’s dependable, governable collaboration. Because of limited interoperability, humans are still required to manage coordination between multiple agents. This is the governance problem: accountability diffusion. If one agent delegates to another, which calls a tool, which references incomplete data, who is actually accountable for the final wording sent to a client? We might sign it, the clinic might deploy it, the vendor might supply it, and the model might generate it, yet no one can point to a clear, auditable decision boundary. In therapy, that’s not abstract; it’s how ethical responsibility gets blurred. Still, we shouldn’t throw the tools away, and we should keep the narrative balanced. Agents promise real efficiency: they can reduce admin burden, draft psychoeducation we approve, structure worksheets, translate or simplify content to match literacy needs, and summarize our own prior notes with clear provenance. But practical constraints remain, so boundaries must stay explicit: delegate low-risk, reversible, verifiable tasks, and resist delegating anything that functions like diagnosis, risk assessment, crisis response, or treatment decisions. If we get that boundary right, we take the gains without surrendering what clients come for: trustworthy, human accountability.

In the past two weeks, many clinicians have seen posts about Lotus Health AI, described as an “AI doctor powered by real doctors”, a physician-supervised service that can assess, diagnose, prescribe, and refer, positioned as free, always available, and multilingual. The company also announced a $35M Series A bringing total funding to $41M, co-led by Kleiner Perkins and CRV, and argues it can cut administrative waste by making physicians more productive without insurance billing. From a therapist’s perspective, the key question isn’t whether the marketing is compelling, it’s whether this becomes a reliable front door to primary care that reduces the friction therapy often absorbs: sleep problems mistaken for depression, thyroid/anemia issues that look like burnout, medication side effects that destabilize mood, and medical uncertainty that fuels panic and health anxiety. In day-to-day work, therapy can become the place where fragmented healthcare gets processed, emotionally, and sometimes administratively, because clients can’t access timely care elsewhere. One practical benefit, if Lotus functions as described, is speed as clinical timing, not just convenience.Earlier medical clarification can make therapy interventions more accurate and effective.When a client says, “Something feels off physically,” we validate and explore patterns, yet sometimes the responsible step is: get a medical assessment early.The differential matters: if symptoms are physiological or medication-related, the plan shifts; if they’re anxiety-driven, it shifts differently.In many systems, “soon” becomes weeks, and therapy becomes a holding space for unresolved uncertainty.A 24/7 primary-care-like channel that reviews consolidated history and routes to in-person care could reduce that gap.Lotus explicitly positions itself as going beyond generic chatbot advice by involving board-certified physicians who review guidance and prescribe when appropriate. A second benefit is indirect but meaningful: it may improve the quality and coherence of information clients bring back into therapy. Many clients struggle to describe symptoms clearly due to stress, dissociation, executive dysfunction, trauma, or exhaustion. If a platform helps organize meds, labs, and prior records into a clearer story, it can reduce shame (“I’m not making it up”), sharpen insight (“my panic clusters around sleep disruption”), and help therapists choose more precise interventions (e.g., exposure for health anxiety vs urgent referral for red flags). Third, there are coordination and access benefits even if therapists never touch the platform. When clients can obtain refills, medication reviews, and referrals with fewer obstacles, therapy is less likely to be derailed by preventable destabilizers. Practically, that can mean fewer sessions spent troubleshooting access and more time spent on the core work of therapy, skills, meaning-making, attachment repair, behavior change, and identity-level integration. That said, therapists are trained to notice how tools can become part of a symptom cycle. An always-available “doctor in your pocket” may stabilize some clients, but it can also feed reassurance-seeking for others, especially with health anxiety, OCD-spectrum checking, panic, somatic symptom patterns, or trauma-related body scanning. Even if guidance is solid, repeated checking can still function as avoidance or compulsion. The goal isn’t to demonize the tool, but to integrate it into a treatment plan with clear, time-limited, values-consistent use. There are also boundary questions that show up quickly. If clients rely on app-mediated care, therapists will be asked to interpret it: “Should I trust this?” “What does this diagnosis mean?” “Can you message them for me?” A helpful posture is to treat it like any outside provider: help clients clarify questions, process impact, and decide next steps, while avoiding medicine-by-proxy. It also matters that what any physician-led digital service can do may vary by jurisdiction, licensure, and telemedicine rules, so “available” may not always mean “authorized to treat” in the way clients assume. Ethically, the business model matters because trust is a clinical ingredient. If clients suspect recommendations are influenced by sponsorships or commercial incentives (whether or not that’s true), it can erode trust in healthcare and show up in therapy as cynicism, avoidance, or hopelessness. This makes transparency practical, not philosophical: what is automated vs clinician-reviewed, and how conflicts of interest are managed over time. Privacy and data integrity are equally central. Even with strong security claims, it helps to think concretely: what data is being linked, who can access it, what consent was given, what can be deleted, and what might be retained. Data-rich systems can fail in new ways, through breaches, misuse, or overconfidence in incomplete records, so the most ethical stance is careful integration, explicit consent, and humility. Overall, I’m cautiously interested in what this could become for therapy-adjacent care: faster evaluation, less avoidable suffering, and less informal care coordination falling onto therapists. But the promise depends on whether it earns trust in real-world use, clinically, ethically, and operationally, so clients aren’t left alone with medical uncertainty that insight alone can’t resolve.

AI in healthcare is no longer a concept. It’s a reality in motion and we’re now watching it mature in a more serious, more structured way. For a long time, healthcare was treated like a “possible use case” for general AI. But in practice, it has already been one of the most common real-world arenas where people test what these systems can do. Patients ask questions after clinic hours because anxiety doesn’t follow office schedules. Caregivers try to interpret lab results while waiting for a follow-up appointment. People navigating chronic conditions search for plain-language explanations of complex treatment plans. Clinicians, meanwhile, are under relentless documentation pressure and are constantly looking for tools that reduce cognitive load without sacrificing safety. The demand wasn’t hypothetical, it was already here. What’s changing now is that major AI companies are building healthcare-specific products that admit, plainly, that medicine is not “one size fits all.” Two launches make the shift easy to see: Claude for Healthcare and ChatGPT Health. They’re often discussed in the same breath, but clinically and ethically, we shouldn’t treat them as interchangeable. They point to two different problems and two different audiences, and that distinction matters because it shapes how risk shows up. ChatGPT Health is best understood as a patient-oriented space: a place where individuals can connect personal health or wellness information and receive explanations, summaries, and context in language that feels human. The promise is clarity. Healthcare is full of jargon, fragmented portals, and rushed appointments; a tool that helps someone understand their own information could reduce confusion and improve follow-through. When used appropriately, it can support better conversations with clinicians because patients arrive with sharper questions and less overwhelm. But that same strength is also its most predictable risk. When a system explains something smoothly, people can mistake fluency for clinical authority. We’ve all seen it: a confident tone can feel like certainty, even when the underlying situation is ambiguous. In healthcare, that gap is not academic, it can shape real decisions. So the safety challenge for a patient-facing tool isn’t just accuracy in a narrow sense. It’s expectation-setting, clear boundaries, and guardrails that prevent “informational support” from being interpreted as diagnosis or medical instruction. Claude for Healthcare, on the other hand, is more naturally framed as an enterprise and workflow tool. The emphasis is less “ask me anything” and more “connect me to the work.” Healthcare organizations don’t just need answers; they need operational support: interpreting and summarizing complex information at scale, reducing administrative friction, supporting research and internal processes, and fitting into existing systems without turning every task into another tab and another login. If we’re honest about what burns clinicians out, a large portion of it lives here, in documentation, administrative tasks, and the endless effort of finding and re-finding information across messy workflows. That’s why workflow-oriented tools feel so attractive: they target the pressure points that are actually breaking the system. But again, the risk is different. When AI becomes part of a workflow, it can scale both efficiency and error. If an output is wrong and nobody catches it, the mistake doesn’t just affect one conversation it can become embedded in documentation, passed forward, copied, and normalized. The more “plugged in” the tool is, the more essential it becomes to design for oversight rather than speed alone. This is where the compliance conversation becomes more than a checkbox. Once these systems touch sensitive health information, the question isn’t simply “is it HIPAA -compliant?” or “is it GDPR-compliant?” The deeper question is: how will data governance work in real life, with real people, under real time pressure? HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation) are different frameworks, but both push toward the same discipline: clear rules about what data is collected, why it’s collected, who can access it, how long it’s kept, and what happens when something goes wrong. And here’s the point we can’t afford to gloss over: compliance is not something a company can fully “grant” to a user through a product announcement. Even if a system is designed to be HIPAA-ready or offers strong security features, organizations still need to deploy it responsibly. That means access controls, role-based permissions, audit trails, staff training, retention policies, incident response planning, and crystal-clear boundaries around what data should and should not be entered into the system. For patient-facing AI like ChatGPT Health, privacy and consent have to be especially legible, because patients don’t always realize what they’re sharing when they upload documents, connect accounts, or paste text from portals. The tool needs to prevent accidental oversharing and make it obvious when a question crosses into “you need a clinician” territory. For workflow-oriented AI like Claude for Healthcare, the burden shifts toward institutional controls: connector permissions, least-privilege access, monitoring, and accountability structures that keep “helpful automation” from becoming invisible decision-making. What’s genuinely promising in all of this is the direction of travel. We’re moving away from the idea that one generic assistant can safely serve every healthcare scenario. We’re seeing specialization: tools designed for patient understanding, and tools designed for clinical and organizational workflows. That specialization makes it easier to define what the system is for, how it should be evaluated, and where the boundaries must be enforced. Our take is simple: we’re watching healthcare AI diversify, and that’s a sign the sector is being taken seriously. But seriousness comes with obligations. These tools should assist, not diagnose. They should reduce burden, not quietly introduce new error channels. And they should handle sensitive data with governance that is operational, not rhetorical. If we build with those principles, AI can improve clarity and relieve real pressure in healthcare. If we don’t, it will scale confident-sounding uncertainty into the one domain where people can least afford it.

As researchers, we spend a surprising amount of time doing work that is not, strictly speaking, research. We design studies, refine hypotheses, collect and analyze data, and engage deeply with theory and then we open PowerPoint or Illustrator and begin the meticulous process of turning our methods and findings into figures. We adjust arrows, realign boxes, standardize fonts, correct axis labels, and export multiple versions to meet submission guidelines. The science may be rigorous, but the path from text to publication-ready visuals often feels inefficient and cognitively draining. Lately, we’ve been looking at PaperBanana, a text-to-figure AI tool that’s clearly targeting researchers who want to speed up figure creation without sacrificing academic clarity. The core promise is simple: turn method descriptions into structured methodology diagrams, and turn data into charts in a way that’s meant to stay faithful to what we actually did (not just “look plausible”). The detail that makes it feel more “research-first” than generic image generators is the idea of a closed-loop workflow: instead of one-shot output, the tool aims to draft, check, and refine—so the figure is more likely to match the logic of the method. The “plus” here isn’t that we get a pretty figure in seconds. The real added value is that we get cheap iteration. When iteration is cheap, we stop freezing figures too early. We can generate multiple drafts, compare them, and treat figure-building like writing: draft → critique → revise. That’s where quality usually improves, not because the AI is perfect, but because our feedback loop gets faster and less exhausting. At the same time, it’s worth being honest: Illustrae plays a different game. It’s typically the better fit when the job is not just “create one diagram,” but assemble a full visual story, multi-panel figures, posters, teaching material, and lots of manual layout decisions. Illustrae tends to offer more features and flexibility, with more ready-to-use options to manage variables, layouts, iterations, and visual adjustments. The tradeoff many people feel is cost: it’s often described as significantly more expensive (and/or less predictable) than a straightforward researcher subscription, which can be a barrier for individuals or small labs. PaperBanana, in contrast, feels more minimalist and research-focused. For method diagrams and conceptual figures, especially at the drafting stage, it can be comparable in performance, while being more affordable, which matters a lot for labs, PhD students, and early-career researchers. It’s also commonly framed as being built on NanoBanana Pro, positioned as stronger for structured scientific visuals than general image models like DALL·E 3 (at least for diagram-like outputs where structure and labels matter more than “art style”). Here’s the comparison in a clean table (pricing stars are about budget-friendliness + predictability, not “how expensive the company is in absolute terms”): Tool Best for Pricing style (practically) Public price (USD, as listed) Price predictability (1–5) Value for researchers (1–5) Flexibility / control (1–5) Strengths (advantages) Tradeoffs (be honest) Illustrae Posters, multi-panel figures, teaching material, complex layouts Subscription + credits paid pricing not publicly posted (custom quote) (illustrae.co) 2 4 5 More features; more layout control; strong for assembling and polishing big visuals Can be significantly more expensive / less predictable; cost can block individuals/small labs; still needs expert review to avoid conceptual drift PaperBanana Methods diagrams, conceptual figures, fast drafting of research visuals Subscription + credits From $4.90/mo (annual billing) for 100 credits; $6.90/mo for 400; $19.90/mo for 1,500 (paperbanana.studio) (Alt. “credit plans” page also lists $14.90/mo, $59.90/mo, $119.90/mo tiers.) (paperbanana.org) 4 5 3 Minimalist and research-focused; fast drafting; good for method diagrams; affordable enough to be realistic for students/labs No meaningful free tier for deep exploration (subscription needed); final figures still need human refinement; confidentiality policies may not fully guarantee exclusion of unpublished work from training Now the limits, because this is where “AI for academia” either becomes useful or becomes risky. First, human review remains essential. These tools accelerate drafting, but final figures still need expert refinement: labels, implied causality, statistical meaning, and whether the diagram accidentally over-claims what the method can do. Second, subscription friction is real. If there’s no meaningful free tier, adoption becomes a budgeting decision, not a quick experiment, especially for students. Third, confidentiality is still a question. Unless a tool makes an explicit, strong guarantee that unpublished papers/figures are excluded from model training (and clarifies retention), we should be cautious with sensitive or pre-publication material. And yes, we can always rely on classic AI tools like Notebook or NotebookLM for summarizing, outlining, or restructuring ideas. They’re great at text workflows, but they’re not built specifically for researchers’ visual needs, and they’re typically less precise for scientific diagram conventions, which increases the risk of subtle visual or conceptual inaccuracies compared with tools designed for academic figures. So when we ask “what’s the plus?”, it’s this: we’re buying back attention. Not just time, attention. If figure drafting becomes fast enough that we can iterate without dread, we can redirect our effort to what actually moves research forward: clearer hypotheses, sharper methods, better interpretation, and figures that communicate rather than decorate. Choosing the right tool isn’t about hype, it’s about fitness for scientific purpose.