GENOMICS
NEURAL NETWORKS Feed forward, radial basis
multilayer perception, convolutional recurrent neural or modular neural network ENVIRONMENT GENOMICS C.V.D. w-weight PATHOLOGY ATRIAL FIB. SYMPTOMS IMAGERY Upstream feature inputs BLEED
OUTCOME MONITOR
Workspace algorithms
The time compexity of NAS is O(nt–), where n is number of neural architectures evaluated during the search, t– is the average time consumption for evaluating each of the n neural network. The Ontology Model is the complete phenomics of network eXaminations
Fig 2. Workflow structure.
authorisation and adoption program for a national proposition for whole genome sequencing of predictors for autonomous intercepts: n Step 1: Integrate leadership: Pathology leaders have a vital role in championing the adoption of AI, aligning it with national strategies and quality standards. Collaborations with technology partners and a dedication to research and development are vital for successful implementation.5
n Step 2: Policy for populace health: Pathology’s contribution to population health extends beyond individual diagnoses. AI-driven analysis of
pathology data can identify trends, predict disease, enable precision care, and inform public health policies. Ethical considerations on data privacy and usage are of prime importance.5
n Step 3: Secure interoperable flow: Modern pathology relies on digital systems. Ensuring secure and interoperable information systems that can seamlessly integrate with broader healthcare networks is crucial for utilising AI effectively. Robust cybersecurity and data protection measures are essential to maintain patient trust and data integrity.5
n Step 4: Safe personalised wellbeing:
AI in pathology can contribute to predictive health and enable precision care through tailored diagnostic and treatment strategies. The primary goal is to improve patient wellbeing through more accurate and personalised diagnoses, leading to better health management.5
n Step 5: Healthcare system model: Integrating AI into pathology requires a system-wide perspective. This includes establishing quality groups, developing federated models for data sharing and collaboration among institutions, and incorporating AI into specific clinical pathways to streamline
UPSTREAM CULTURE
CULTURE AND ASSURE END TO END WORKFLOW FOR EXAMINATIONS WORKPLACE ASSURANCES
Evaluations, philosophy, wisdom, knowledge analysis and explainability
PHILOSOPHY Logic Ethics
EVALUATIONS Descriptive Comparative Qualitative Economic
Epistemology
PATIENT BLOOD MANAGEMENT
KPI requirements, XIA comparatives, synthetic QA, and use-case evaluations
WISDOM Preception Intelligence Insight
PRECISE TARGET
POST EXAMINATION Workstreams eXamination
SEARCH DOMAINS
DECISION NODES
Workflow Node Analytics: Weight an activation Predictive health and precision care statistics
STROKE
PREDICTIVE VALUE
DIAGNOSTIC SIGNIFICANCE
NAS CATEGORIES search space
search strategy performance estimation strategy PRE EXAMINATION
EXAMINATION INTERCEPT
WORKSTREAM EXAMINATIONS Significance, predictive, precision, prescriptive
Audiences Governance
Health provider Citizen
Digital faciliator Scientist/academic Commissioner
KNOWLEDGE ANALYSIS Scepticism Sources Nature
POPULATION HEALTH
MANAGEMENT
EXPLAINABILITY Trustworthiness Counter-factual Causal reasoning Scientific statistics Certain qualification
Fig 3. Culture work, assure flow.
WWW.PATHOLOGYINPRACTICE.COM JUNE 2025 27
THROMBUS
© copyright James Henry
© copyright James Henry
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