{
  "meta": {
    "name": "Healthcare AI Radar",
    "version": "v1-draft",
    "as_of": "2026-08-27",
    "next_review": "2026-11",
    "method": "Seventeen categories of AI in healthcare, clinical and administrative, each scored 0-10 on three separate maturities: evidence (independent, prospective, ideally randomised, not vendor benchmarks), deployment (routine care at scale, not pilots) and economics (a repeatable way to get paid). Status is set by the weakest of the three scores, because one weak leg stops a category scaling: 7+ Landed, 5-6 Scaling, 3-4 Emerging, below 3 Watch. Example players are illustrative, not a ranking and not exhaustive. Every score note carries a dated source.",
    "status_bands": [
      { "min": 7, "label": "Landed" },
      { "min": 5, "label": "Scaling" },
      { "min": 3, "label": "Emerging" },
      { "min": 0, "label": "Watch" }
    ]
  },
  "categories": [
    {
      "id": "radiology",
      "name": "Radiology imaging",
      "side": "clinical",
      "workflow": "Diagnosis",
      "autonomy": "Flag / triage",
      "scores": { "evidence": 9, "deployment": 9, "economics": 8 },
      "read": "The proven beachhead. Detection, triage and measurement across CT, MRI and X-ray, with the radiologist signing every read. The one clinical category where evidence, deployment and payment have all converged; the open question is consolidation, as foundation models absorb single-finding algorithms into platforms.",
      "evidence_notes": [
        { "point": "1,163 FDA-cleared AI algorithms in radiology, 76% of all cleared AI devices; the FDA is clearing roughly 30 AI devices a month.", "source": "IntuitionLabs, FDA-approved AI medical devices guide", "url": "https://intuitionlabs.ai/articles/fda-approved-ai-medical-devices-list", "date": "2026-03" },
        { "point": "CMS approved a New Technology Add-on Payment for Aidoc's CARE Multi-Triage CT Body device, the first reimbursement of an AI triage suite built on a foundation model.", "source": "Imaging Technology News", "url": "https://www.itnonline.com/content/cms-reimburse-ai-triage-solution-built-ct-diagnostic-foundation-model", "date": "2026" }
      ],
      "players": ["Aidoc", "Annalise.ai", "Lunit", "Qure.ai"],
      "watch_for": "Platform consolidation; NTAP billing from October 2026; generalist reporting models moving from flag to draft."
    },
    {
      "id": "breast-screening",
      "name": "Breast screening",
      "side": "clinical",
      "workflow": "Screening",
      "autonomy": "Triage / second reader",
      "scores": { "evidence": 10, "deployment": 8, "economics": 7 },
      "read": "The flagship evidence story in all of clinical AI. A randomised controlled trial inside a national screening programme is the bar every other category now gets judged against. Economics run through programme-level procurement rather than per-case codes, which caps the score until more national programmes commit.",
      "evidence_notes": [
        { "point": "MASAI randomised controlled trial, 105,000+ women in the Swedish national programme using ScreenPoint's Transpara: 29% more cancers detected with no increase in false positives and a 44% reduction in radiologist screen-reading workload.", "source": "The Lancet Digital Health", "url": "https://www.thelancet.com/journals/landig/article/PIIS2589-7500(24)00267-X/fulltext", "date": "2024-2025" },
        { "point": "Final MASAI results: 12% reduction in interval cancers, 27% fewer aggressive interval cancers, with preferential detection of small node-negative invasive cancers.", "source": "ScreenPoint Medical / The Lancet", "url": "https://screenpoint-medical.com/insights/final-results-masai-trial", "date": "2026" }
      ],
      "players": ["ScreenPoint Medical (Transpara)", "Lunit", "iCAD"],
      "watch_for": "National screening programmes formally adopting AI reading; UK National Screening Committee decisions."
    },
    {
      "id": "stroke",
      "name": "Stroke networks",
      "side": "clinical",
      "workflow": "Diagnosis / pathway coordination",
      "autonomy": "Triage / coordination",
      "scores": { "evidence": 9, "deployment": 8, "economics": 8 },
      "read": "The proof that the value is often in the pathway, not the pixel. The algorithms detect large vessel occlusions, but what they sell is minutes across a hub-and-spoke network. A template for other time-critical pathways, and one of the clearest NHS AI success stories, led by a British company.",
      "evidence_notes": [
        { "point": "Brainomix 360 deployed across 26 English hospitals in four thrombectomy networks; five-year prospective study associates it with a doubling of endovascular thrombectomy rates and reduced transfer delays.", "source": "The Lancet Digital Health", "url": "https://www.thelancet.com/journals/landig/article/PIIS2589-7500(25)00109-8/fulltext", "date": "2025" },
        { "point": "Viz.ai associated with door-to-puncture time reductions of 11-25 minutes in US networks.", "source": "VALIDATE study and stroke network experience, PMC", "url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12802620/", "date": "2025-2026" }
      ],
      "players": ["Brainomix (UK)", "Viz.ai", "RapidAI"],
      "watch_for": "The same network-coordination pattern applied to STEMI, sepsis transfer and major trauma."
    },
    {
      "id": "cardiology",
      "name": "Cardiology",
      "side": "clinical",
      "workflow": "Screening / diagnosis",
      "autonomy": "Flag / measure",
      "scores": { "evidence": 7, "deployment": 6, "economics": 7 },
      "read": "The second wave behind radiology, and the category whose economics turned in 2026. Three businesses inside it: AI-ECG reading cheap ubiquitous signals for expensive structural disease, automated echocardiography, and coronary CT analysis moving into procedure planning. Watch the cheap-signal screening pattern: wearable ECGs as population-scale screens.",
      "evidence_notes": [
        { "point": "More than 200 FDA-cleared AI algorithms touch cardiology (140 cardiology-listed, 203 including cardiac imaging listed under radiology).", "source": "Cardiovascular Business", "url": "https://cardiovascularbusiness.com/topics/artificial-intelligence/cardiology-now-has-more-200-fda-cleared-ai-algorithms", "date": "2026-03" },
        { "point": "The 2026 Hospital OPPS Final Rule establishes national outpatient reimbursement for AI-assisted cardiac analysis; Us2.ai echo AI deployed in NHS community diagnostic settings including the Liverpool BEAT breathlessness project.", "source": "Us2.ai clinical evidence and OPPS Final Rule coverage", "url": "https://us2.ai/clinical-evidence/", "date": "2026" }
      ],
      "players": ["HeartFlow", "Cleerly", "Us2.ai", "Ultromics (UK)", "Eko Health"],
      "watch_for": "Wearable single-lead ECG screening for structural disease; InVision cardiac amyloid NTAP (up to $2,275 per stay) from October 2026."
    },
    {
      "id": "pathology",
      "name": "Digital pathology",
      "side": "clinical",
      "workflow": "Diagnosis",
      "autonomy": "Flag / draft",
      "scores": { "evidence": 7, "deployment": 4, "economics": 3 },
      "read": "Where radiology was around 2016. The algorithms are cleared and clinically evaluated, but you cannot run them on glass: scanner adoption is the bottleneck and the leading indicator. A durable structural driver sits underneath, pathologist shortage plus precision oncology demanding more from every slide.",
      "evidence_notes": [
        { "point": "Paige Prostate prospectively evaluated inside routine NHS diagnostic workflows across multiple trusts on more than 1,000 prostate biopsy cases; Paige and Ibex hold FDA clearances for prostate diagnosis.", "source": "NCBI Bookshelf, Paige Prostate Suite evaluation", "url": "https://www.ncbi.nlm.nih.gov/books/NBK608438/", "date": "2025-2026" },
        { "point": "Ibex Prostate Detect in routine use at an academic medical centre cut immunohistochemistry stain utilisation by 38%; KLAS reports US digital pathology purchasing accelerating from a low base.", "source": "ScienceDirect implementation study; KLAS via HIT Consultant", "url": "https://www.sciencedirect.com/science/article/pii/S2153353926001343", "date": "2026" }
      ],
      "players": ["Paige", "Ibex", "PathAI", "Proscia (platform)", "Visiopharm"],
      "watch_for": "Scanner installed base growth; whole-slide foundation models; first reimbursement codes for AI-assisted pathology reads."
    },
    {
      "id": "sepsis",
      "name": "Sepsis and deterioration",
      "side": "clinical",
      "workflow": "Monitoring / early warning",
      "autonomy": "Flag",
      "scores": { "evidence": 7, "deployment": 5, "economics": 3 },
      "read": "The category that teaches the field its most important lesson, in two acts. A proprietary black-box model deployed at enormous scale on internal benchmarks missed most of the sepsis it was meant to catch. The category recovered by doing the work: prospective multisite evidence, then FDA approval. Rebuilt on proper foundations, but payment remains unformed.",
      "evidence_notes": [
        { "point": "Epic Sepsis Model: claimed AUROC 0.76-0.83; independent external validation found 0.63 with 33% sensitivity at operational thresholds, and later ED validations found sensitivity as low as 14.7% with PPV of 7.6%.", "source": "JAMIA Open external validation", "url": "https://academic.oup.com/jamiaopen/article/7/4/ooae133/7900014", "date": "2024-2025" },
        { "point": "TREWS (Johns Hopkins, commercialised by Bayesian Health): prospective multisite evidence showing roughly 20% mortality reduction with earlier antibiotics; FDA approval granted May 2026 as one of the first cleared AI early-warning systems for sepsis.", "source": "Johns Hopkins Hub; NEJM AI", "url": "https://hub.jhu.edu/2026/05/12/fda-approves-early-warning-system-for-sepsis/", "date": "2026-05" }
      ],
      "players": ["Bayesian Health (TREWS)", "Dascena (InSight)"],
      "watch_for": "Whether FDA approval unlocks a payment route; EHR-embedded models being held to the same external-validation bar."
    },
    {
      "id": "intraoperative",
      "name": "Intraoperative AI",
      "side": "clinical",
      "workflow": "Intervention",
      "autonomy": "Flag / inform",
      "scores": { "evidence": 3, "deployment": 2, "economics": 1 },
      "read": "The newest clinical frontier and the highest-stakes white space on the map. Real-time tissue intelligence gives surgeons molecular-level tumour classification in minutes during the operation instead of days from the lab, directly informing how much tissue to take. Evidence emerging, deployment early, economics unformed. Highest ceiling of any clinical category.",
      "evidence_notes": [
        { "point": "Stimulated Raman histology plus models such as FastGlioma and DeepGlioma enable rapid intraoperative molecular diagnosis of brain tumours without frozen-section delays; Harvard work decodes glioma genomics during surgery.", "source": "Harvard Medical School; Frontiers in Surgery review", "url": "https://hms.harvard.edu/news/ai-tool-decodes-brain-cancers-genome-during-surgery", "date": "2025-2026" },
        { "point": "AI-enhanced intraoperative ultrasound trial for brain tumour surgery (BrainUS-AI, NCT07376304) underway from 2026; broader surgical AI stack includes AR margin overlay, workflow-phase recognition and automated operative notes.", "source": "ClinicalTrials.gov", "url": "https://clinicaltrials.gov/study/NCT07376304", "date": "2026" }
      ],
      "players": ["Invenio / SRH ecosystem", "Proximie (UK)", "Theator", "Medtronic Touch Surgery"],
      "watch_for": "First randomised evidence that intraoperative AI changes resection completeness or survival; theatre-workflow products crossing the device line."
    },
    {
      "id": "genomics",
      "name": "Genomics and precision oncology",
      "side": "clinical",
      "workflow": "Treatment decision",
      "autonomy": "Draft / inform",
      "scores": { "evidence": 7, "deployment": 5, "economics": 6 },
      "read": "Shifted from variant pipelines to multimodal foundation models trained on proprietary clinical-plus-molecular data at a scale competitors cannot assemble. The moat is data exhaust from care delivery, not model architecture. Paid through established test reimbursement rather than AI-specific codes.",
      "evidence_notes": [
        { "point": "Tempus: 500+ petabytes, 45 million de-identified patient journeys, multimodal foundation-model results presented at ASCO 2026; pending Personalis acquisition adds molecular residual disease detection.", "source": "BioSpace / Tempus", "url": "https://www.biospace.com/press-releases/tempus-announces-initial-results-from-its-multimodal-foundation-model-efforts-for-novel-and-scalable-insight-generation-in-oncology", "date": "2026" },
        { "point": "JCO Precision Oncology study: tumour-normal matching, RNA-seq and liquid biopsy reflex identify actionable findings missed by standard testing in community oncology.", "source": "Tempus / JCO Precision Oncology", "url": "https://www.tempus.com/news/pr/tempus-announces-study-highlighting-the-role-of-advanced-genomic-profiling-features-in-identifying-clinically-actionable-findings/", "date": "2026-03" }
      ],
      "players": ["Tempus", "Foundation Medicine", "Personalis"],
      "watch_for": "Foundation-model-derived biomarkers entering guidelines; UK Genomic Medicine Service AI adoption."
    },
    {
      "id": "drug-discovery",
      "name": "AI drug discovery",
      "side": "clinical",
      "workflow": "Research and development",
      "autonomy": "Draft (molecules)",
      "scores": { "evidence": 2, "deployment": 1, "economics": 1 },
      "read": "Heavily funded, still unproven end-to-end. The technology demonstrably compresses discovery, but discovery was never the binding constraint; trials are. Zero FDA approvals of an AI-designed drug so far. The next 24 months of Phase II/III readouts decide the category's claim, with Insilico's rentosertib as the bellwether.",
      "evidence_notes": [
        { "point": "117 AI-enabled therapeutic assets across 63 companies have entered human trials; 51% completed Phase 1, only 8 completed Phase 2, zero FDA approvals (ASCO 2026 analysis).", "source": "IntuitionLabs pipeline review", "url": "https://intuitionlabs.ai/articles/ai-discovered-drugs-clinical-trials-2026", "date": "2026-07" },
        { "point": "Insilico's rentosertib entered Phase III for idiopathic pulmonary fibrosis in July 2026 off a Nature Medicine Phase IIa; Isomorphic released IsoDDE (February 2026) but has not yet dosed a patient, first-in-human slipping to late 2026.", "source": "PDP Spectra / AIM Media House", "url": "https://aimmediahouse.com/ai-lifesciences/2026-is-the-year-ai-drug-discovery-meets-clinical-reality", "date": "2026" }
      ],
      "players": ["Insilico Medicine", "Recursion (incl. Exscientia)", "Isomorphic Labs", "Schrodinger"],
      "watch_for": "Rentosertib Phase III; any first FDA approval of an AI-designed asset; platform-economics proof beyond partnership fees."
    },
    {
      "id": "triage",
      "name": "Patient triage and virtual care",
      "side": "clinical",
      "workflow": "Front door / navigation",
      "autonomy": "Signpost",
      "scores": { "evidence": 4, "deployment": 6, "economics": 5 },
      "read": "The Babylon scar defines the category: peak-hype symptom checking, safety questions, collapse. What changed in 2026 is the buyer. When the system operator embeds triage as national infrastructure, the standalone consumer symptom checker dies; the opportunity moves to powering the infrastructure and the specialty layers beneath it. Conversational diagnostic AI is the next wave.",
      "evidence_notes": [
        { "point": "NHS App AI triage tool rolling out to 200,000+ patients within 12 months and all App users by April 2028, part of a £10bn three-year technology programme.", "source": "NHS England", "url": "https://www.england.nhs.uk/2026/07/nhs-accelerates-artificial-intelligence-rollout-to-cut-waiting-times-and-improve-care-for-millions/", "date": "2026-07" },
        { "point": "Babylon collapsed August 2023 with triage-accuracy safety concerns; Ada Health persists at signpost autonomy with 25m+ assessments; conversational diagnostic AI showing strong feasibility-study results in primary care.", "source": "IntuitionLabs chatbot review; arXiv feasibility study", "url": "https://intuitionlabs.ai/articles/ai-chatbots-healthcare", "date": "2026" }
      ],
      "players": ["Ada Health", "eMed (ex-Babylon UK)", "Buoy"],
      "watch_for": "Who supplies the NHS App triage engine; regulated conversational diagnostics reaching device clearance."
    },
    {
      "id": "remote-monitoring",
      "name": "Remote monitoring and virtual wards",
      "side": "clinical",
      "workflow": "Monitoring",
      "autonomy": "Flag / triage",
      "scores": { "evidence": 5, "deployment": 6, "economics": 5 },
      "read": "The substrate of hospital-at-home. Converging with language models: monitoring streams plus conversational check-ins plus automated documentation. Deployment ran ahead of outcome evidence during the post-pandemic push; the categories now converge with scribes and agents into a coordination layer around the clinician.",
      "evidence_notes": [
        { "point": "Remote patient monitoring converging with LLMs for documentation, symptom-reporting triage and coaching; EHR-integrated RPM assistants expected 2026-2027.", "source": "IntuitionLabs, AI in remote patient monitoring", "url": "https://intuitionlabs.ai/articles/ai-remote-patient-monitoring", "date": "2026" }
      ],
      "players": ["Doccla (UK)", "Current Health", "Huma (UK)"],
      "watch_for": "Virtual ward outcome evidence at scale; NHS neighbourhood health build-out under the 10 Year Health Plan."
    },
    {
      "id": "scribes",
      "name": "Ambient scribes",
      "side": "admin",
      "workflow": "Documentation",
      "autonomy": "Draft",
      "scores": { "evidence": 7, "deployment": 10, "economics": 9 },
      "read": "The fastest adoption curve healthcare IT has seen, and the template for how administrative AI enters healthcare: no device clearance at the basic tier, instant clinician-felt value, hard ROI against the workforce's loudest complaint. Strategically a wedge, not an endpoint: the ladder runs scribe, coding, order drafting, care navigation, and each rung crosses further into regulated territory.",
      "evidence_notes": [
        { "point": "More than 70% of large US integrated delivery networks have deployed or are piloting ambient scribes (KLAS); Abridge in 300+ health systems including Kaiser Permanente; DAX Copilot in 600+ organisations; randomised and controlled studies now validate documentation-time and burnout reductions.", "source": "KLAS via EHR Source", "url": "https://www.ehrsource.com/articles/ambient-ai-scribes-comparison/", "date": "2025-2026" },
        { "point": "NHS England expects ICB and provider adoption of ambient voice technology from April 2026; MHRA guidance of 29 July 2026 draws the line between transcribe/summarise/draft (not a device) and diagnosis/treatment/automated action (regulated device); national supplier registry requires Class 1 accreditation plus DTAC; Heidi holds the largest NHS AVT deal to date.", "source": "NHS England; MHRA", "url": "https://www.gov.uk/government/news/mhra-clarifies-regulatory-status-of-ambient-voice-technologies-used-in-the-nhs", "date": "2026-07" }
      ],
      "players": ["Abridge", "Microsoft / Nuance DAX", "Heidi", "Tortus (UK)", "Nabla", "Suki"],
      "watch_for": "The step from notes to orders (crosses the MHRA line); consolidation as EHR vendors bundle; NHS supplier registry entries."
    },
    {
      "id": "rcm",
      "name": "Revenue cycle and prior authorisation",
      "side": "admin",
      "workflow": "Back office",
      "autonomy": "Draft / act",
      "scores": { "evidence": 6, "deployment": 6, "economics": 8 },
      "read": "Direct, CFO-signed ROI in a US market where denial pain compounds yearly. The caveat is structural: payers automate denials as fast as providers automate appeals, and an AI-versus-AI arms race compresses margin on both sides. UK translation runs through clinical coding and the productivity agenda rather than prior auth.",
      "evidence_notes": [
        { "point": "AI-in-RCM market $20.6B (2024) projected to $70B by 2030; denial rates around 12% with leakage growing 25% year on year; mature AI deployments report 30-40% denial-rate reductions and 22% fewer prior-auth denials.", "source": "Healthcare Finance News; industry statistics roundup", "url": "https://www.healthcarefinancenews.com/news/payer-denials-and-prior-authorization-delays-are-top-rcm-concerns", "date": "2026" },
        { "point": "Bain hospital executive survey: 82% expanding AI investment, with prior auth, clinical documentation and predictive analytics the top three areas; only about a quarter of organisations run revenue-cycle AI at scale.", "source": "Bain survey via RCM statistics roundup", "url": "https://stealthagents.com/research/ai-revenue-cycle-management-automation-statistics-2026", "date": "2025-2026" }
      ],
      "players": ["Waystar", "AKASA", "Cohere Health", "Charta Health"],
      "watch_for": "US prior-auth reform; the payer-side automation counter-wave; agentic end-to-end claims workflows."
    },
    {
      "id": "coding",
      "name": "Clinical coding from notes",
      "side": "admin",
      "workflow": "Documentation / back office",
      "autonomy": "Draft",
      "scores": { "evidence": 4, "deployment": 4, "economics": 6 },
      "read": "The next rung on the scribe ladder: codes suggested from the ambient note for human confirmation. Economically attractive because coding backlogs are measurable money; regulatorily sensitive because code suggestion sits close to the MHRA line and payment integrity. Expect the scribe leaders to absorb this category rather than standalone winners.",
      "evidence_notes": [
        { "point": "MHRA guidance explicitly covers products that 'suggest clinical codes for review by a clinician' as outside device regulation when review is preserved, defining the safe operating envelope for the category.", "source": "MHRA / NHS England", "url": "https://www.england.nhs.uk/long-read/medical-device-regulation-for-ambient-voice-technology-products/", "date": "2026-07" }
      ],
      "players": ["Abridge", "Tortus (UK)", "Corti", "CodaMetrix"],
      "watch_for": "Autonomous coding claims; audit outcomes where AI-suggested codes met payment review."
    },
    {
      "id": "agentic-ops",
      "name": "Agentic operations",
      "side": "admin",
      "workflow": "Back office / coordination",
      "autonomy": "Act (bounded)",
      "scores": { "evidence": 3, "deployment": 3, "economics": 6 },
      "read": "The frontier of administrative AI: systems that execute multi-step workflows across EHR, billing, communication and compliance systems rather than producing one output. Landing first where work is repetitive and rules-heavy. The gate is governance: hospital security frameworks were not built for autonomous systems holding human-level privileges, which makes zero-trust agent architecture an unsolved and investable problem.",
      "evidence_notes": [
        { "point": "Agentic AI landing first in patient verification, scheduling, referral processing, documentation and coding; AWS launched a dedicated agentic health administration platform; over 80% of healthcare executives expect significant value across clinical and back-office operations.", "source": "iatroX; Kore.ai; HIT Consultant", "url": "https://www.iatrox.com/blog/agentic-ai-in-healthcare-where-it-is-actually-landing-first", "date": "2026" }
      ],
      "players": ["AWS health administration platform", "Kore.ai", "Hippocratic AI", "Notable"],
      "watch_for": "First public post-incident reviews of agent failures; zero-trust agent governance products; EHR-native agent frameworks."
    },
    {
      "id": "capacity",
      "name": "Capacity and patient flow",
      "side": "admin",
      "workflow": "Operations",
      "autonomy": "Flag / inform",
      "scores": { "evidence": 2, "deployment": 3, "economics": 3 },
      "read": "Perennially promised, stubbornly hard. Bed, theatre and discharge prediction demos well, but the binding constraints are physical and political rather than informational, so predictions that cannot move a constraint do not move a metric. Held at Watch until deployments show sustained flow outcomes rather than dashboard adoption.",
      "evidence_notes": [
        { "point": "Capacity and flow prediction remains inconsistently evidenced relative to vendor claims; the deployment gap between operational dashboards and demonstrated flow outcomes persists across systems.", "source": "Category read across 2026 operational AI coverage", "url": "https://www.kore.ai/blog/ai-agents-in-healthcare-12-real-world-use-cases-2026", "date": "2026" }
      ],
      "players": ["Palantir (FDP context, UK)", "Qventus", "LeanTaaS"],
      "watch_for": "Independent evaluations tying flow AI to length-of-stay or elective throughput; NHS Federated Data Platform use cases maturing."
    },
    {
      "id": "mental-health",
      "name": "Mental health AI",
      "side": "clinical",
      "workflow": "Treatment / support",
      "autonomy": "Signpost / draft",
      "scores": { "evidence": 3, "deployment": 5, "economics": 5 },
      "read": "The top-funded clinical indication seven years running, which is precisely why it needs the evidence test most. Access-gap economics are real and the funding is durable, but clinical-grade autonomous therapy remains evidence-thin relative to money raised, and that gap is itself the diligence signal.",
      "evidence_notes": [
        { "point": "Mental health is the top-funded clinical indication for the seventh consecutive year (Rock Health H1 2026), with digital health funding overall at $7.4B in H1 2026 and capital concentrating in megadeals.", "source": "Rock Health H1 2026", "url": "https://rockhealth.com/insights/h1-2026-funding-and-market-overview-durable-roots-shifting-routes/", "date": "2026-07" }
      ],
      "players": ["Woebot lineage", "Wysa (UK)", "Limbic (UK)", "Ieso (UK)"],
      "watch_for": "Regulated digital therapeutics with randomised evidence; NHS Talking Therapies AI triage adoption; safety incidents shaping policy."
    }
  ]
}