Algorithms in Health Care and Public Health: Diagnosis, Triage, Surveillance, and Care Governance

Last Updated June 22, 2026

Algorithms in health care and public health examine how computational systems support diagnosis, triage, screening, care coordination, resource allocation, clinical decision support, disease surveillance, outbreak detection, population health, hospital operations, insurance administration, and health-system governance. These systems do not merely process medical data. They can influence who receives care, how quickly people are seen, which risks are flagged, which resources are allocated, and how institutions understand health, illness, vulnerability, and prevention.

Health algorithms operate inside clinics, hospitals, laboratories, insurers, public-health agencies, emergency systems, research institutions, pharmaceutical workflows, digital-health platforms, and community health infrastructures. Some systems are simple rules embedded in electronic health records. Others are machine-learning models, imaging classifiers, sepsis alerts, risk stratification systems, resource-allocation tools, epidemic models, claims algorithms, population-health dashboards, or public-health surveillance systems.

This article introduces algorithms in health care and public health, clinical decision support, diagnostic models, triage systems, risk prediction, screening, electronic health records, administrative data, public-health surveillance, outbreak detection, resource allocation, health equity, bias, privacy, safety, validation, human review, accountability, and responsible health algorithm governance. It shows why health algorithms must be judged not only by accuracy or efficiency, but by safety, equity, clinical usefulness, privacy, explainability, human judgment, accountability, and public trust.

This article explains how algorithms support health care and public health through clinical decision support, diagnostic modeling, risk stratification, screening, triage, patient monitoring, hospital operations, claims review, disease surveillance, outbreak detection, public-health modeling, resource allocation, health-equity review, privacy safeguards, audit trails, validation, human review, and governance. It emphasizes that health algorithms must be evaluated within care systems where error, delay, bias, opacity, privacy loss, and overreliance can affect human well-being.

Why Algorithms in Health Care and Public Health Matter

Algorithms in health care and public health matter because they can influence clinical attention, diagnostic reasoning, patient risk, care access, hospital capacity, outbreak response, resource allocation, insurance decisions, and public trust. A model that flags deterioration can change how quickly a patient is seen. A triage system can alter queue order. A claims algorithm can affect treatment approval. A public-health model can guide surveillance, communication, and intervention.

Health algorithms are often evaluated through accuracy, sensitivity, specificity, predictive value, calibration, or operational efficiency. These measures matter, but they are not enough. A model may be accurate in one hospital and unsafe in another, effective on average and inequitable across groups, useful in research and burdensome in practice, or technically impressive but clinically irrelevant.

Health algorithms are never only technical systems. They are care-system interventions.

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Health Algorithms Defined

A health algorithm is a computational procedure used to classify, predict, rank, diagnose, triage, allocate, monitor, explain, optimize, or govern health-related information and decisions. Health algorithms may include rule-based alerts, scoring systems, statistical models, machine-learning classifiers, image-analysis models, natural-language processing systems, simulation models, optimization tools, surveillance dashboards, and workflow automations.

The defining feature is not whether the system uses artificial intelligence. A simple rule embedded in an electronic health record can affect care. A complex model may only support background planning. The governance question depends on how the algorithm is used and what consequences follow from its outputs.

A health algorithm should be evaluated as part of a care pathway, not as an isolated prediction engine.

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Clinical Decision Support

Clinical decision support systems provide alerts, recommendations, reminders, risk scores, order suggestions, diagnostic prompts, guideline checks, medication warnings, care-gap notices, or workflow guidance. These systems can help clinicians manage complexity, reduce missed information, and standardize evidence-based care.

They can also create burden. Too many alerts produce fatigue. Poorly timed recommendations interrupt work. Weak explanations reduce trust. Overly rigid logic can ignore clinical context. Decision support should therefore be evaluated not only by correctness, but by usability, timing, workflow fit, clinical relevance, override design, and monitoring of downstream effects.

Clinical decision support should strengthen clinical reasoning, not replace it.

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Diagnostic Models and Medical Imaging

Diagnostic algorithms classify signs, symptoms, images, lab results, waveforms, genetic data, notes, or other health information. Imaging models may detect abnormalities in radiology, pathology, ophthalmology, dermatology, cardiology, or other specialties. Natural-language systems may extract findings from clinical notes or summarize relevant history.

Diagnostic models require careful validation because errors have direct clinical implications. A false negative may delay care. A false positive may lead to anxiety, additional testing, overtreatment, or cost. A model may perform differently across equipment, sites, populations, imaging protocols, disease prevalence, and clinical workflows.

A diagnostic model is safe only when its use context, limitations, and review responsibilities are clear.

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Triage, Risk Stratification, and Care Prioritization

Triage and risk stratification algorithms prioritize patients, calls, referrals, appointments, emergency cases, outreach lists, or care-management resources. They may estimate urgency, deterioration risk, readmission risk, length of stay, complication risk, or need for follow-up.

Prioritization is ethically sensitive because it determines attention under constraint. A triage algorithm can help identify urgent cases, but it can also delay care for people whose risks are undermeasured. It can reproduce unequal access if prior health care use is treated as need. It can make scarcity look technical when it is also institutional and ethical.

Triage algorithms should make care more responsive, not make rationing less visible.

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Electronic Health Records and Administrative Data

Health algorithms often depend on electronic health records, claims data, lab values, medication lists, diagnostic codes, clinical notes, imaging metadata, appointment history, hospital utilization, billing records, device data, and public-health reports. These data are not neutral mirrors of patient reality. They are records produced by institutions, documentation practices, billing systems, clinical workflows, and unequal access to care.

A patient with more encounters may appear sicker because more data exist. A patient with less access may appear lower risk because needs were never recorded. Administrative codes may reflect billing rules rather than clinical reality. Missing data may reflect barriers, not absence of illness.

Health data require interpretation before computation because the record is not the patient.

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Public Health Surveillance and Outbreak Detection

Public-health algorithms monitor populations, detect outbreaks, estimate disease spread, identify clusters, forecast demand, prioritize testing, target vaccination, guide communication, and evaluate interventions. They may use laboratory reports, syndromic data, wastewater signals, mobility data, clinic visits, emergency calls, school reports, pharmacy trends, or social indicators.

Surveillance systems can support early warning and prevention, but they also raise privacy, trust, equity, and interpretation challenges. Public-health models must communicate uncertainty and avoid treating incomplete data as complete population truth.

Public-health algorithms should support prevention while preserving trust, privacy, and equity.

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Resource Allocation and Health-System Capacity

Health systems allocate scarce resources: beds, clinicians, operating rooms, appointments, medications, vaccines, diagnostic tests, ambulances, outreach staff, equipment, and public-health attention. Algorithms can forecast demand, optimize schedules, prioritize queues, allocate capacity, and identify bottlenecks.

Resource allocation is not merely logistics. It is an ethical and institutional decision. Optimization models can hide tradeoffs if their objectives are narrow. A system that maximizes throughput may neglect dignity, access, equity, continuity, or care quality.

Health-system optimization should serve care, not simply throughput.

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Population Health and Prevention

Population-health algorithms identify groups at risk, forecast burden, detect care gaps, target outreach, evaluate programs, and support prevention. These systems can help move health care upstream by identifying people who may benefit from support before crisis occurs.

But population-health models can also misrepresent need if they rely on cost, utilization, or prior care as proxies for health status. People who have been underserved may have fewer records and lower costs, leading models to underestimate their needs. Responsible population-health algorithms should distinguish health need from health spending, access from absence, and recorded care from actual vulnerability.

Population-health algorithms should help institutions see unmet need, not merely recorded utilization.

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Insurance, Claims, and Utilization Management

Health insurers and administrators use algorithms to process claims, detect fraud, review medical necessity, authorize services, route cases, estimate cost, manage networks, and monitor utilization. These systems can improve consistency and reduce administrative burden, but they can also delay care, deny services, obscure responsibility, or shift burden onto patients and clinicians.

Utilization management algorithms are especially sensitive because they sit between clinical judgment, payment rules, patient need, institutional incentives, and legal obligations. Decisions affecting access to care must be explainable, appealable, timely, and reviewable by qualified humans when stakes are high.

Administrative health algorithms should reduce burden, not turn care access into an opaque procedural maze.

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Fairness, Bias, and Health Equity

Health algorithms can reproduce inequity when data reflect unequal access, biased measurement, historical exclusion, differential treatment, underdiagnosis, environmental exposure, structural disadvantage, or proxy variables. Bias in health algorithms is not only a statistical issue. It is connected to institutions, communities, access, history, and care delivery.

Equity review should examine who is included in data, who is missing, how labels were produced, whether proxies are appropriate, how errors differ across groups, whether predictions translate into beneficial action, and whether the system increases or reduces care disparities.

A health algorithm is equitable only if its benefits and burdens are evaluated in the care system where it operates.

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Validation, Safety, and Clinical Usefulness

Validation asks whether a health algorithm works for its intended purpose, population, setting, workflow, and decision role. Technical performance is necessary but not sufficient. A model must also be clinically useful, safe, interpretable enough for its role, integrated responsibly, monitored over time, and connected to effective action.

Clinical usefulness depends on whether the model improves decisions, reduces harm, supports workflow, and leads to better care. A high-performing prediction may be useless if no intervention exists, if clinicians ignore it, if it arrives too late, if it increases workload, or if it worsens inequity.

Health algorithm validation must answer the care question, not only the prediction question.

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Privacy, Security, and Data Governance

Health information is deeply sensitive. Algorithms can reveal diagnoses, risk status, genetic information, reproductive history, mental health indicators, disability, medication use, location patterns, insurance status, or family relationships. Responsible health algorithms require data minimization, access controls, consent or legal basis where required, security, de-identification where appropriate, governance review, and clear limits on secondary use.

Privacy is also relational. Health data can affect families, communities, and groups. Public-health surveillance may involve population-level tradeoffs between prevention and privacy. Digital-health platforms may collect behavioral data that users do not understand as medical.

Health data governance should protect people while allowing responsible learning and public benefit.

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Human Judgment and Clinical Responsibility

Human judgment remains essential in health care because patients are not data points, symptoms are contextual, disease evolves, care involves values, and health decisions often occur under uncertainty. Algorithms can support attention, but they cannot fully understand a patient’s goals, fears, social context, lived experience, or clinical complexity.

Human review must be meaningful. A clinician who receives a score without explanation, time, training, authority, or context may either ignore it or overtrust it. Responsible implementation should define when human review is required, what evidence is available, how overrides are recorded, and who is accountable for decisions.

Health automation should support care teams, not shift responsibility onto opaque systems.

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Representation Risk

Representation risk appears when health algorithms reduce people, illness, care, or population health to simplified scores and categories. A patient becomes a risk score. A symptom becomes a coded feature. A community becomes a cluster. A care gap becomes a dashboard item. A social need becomes a variable. A clinical note becomes extracted text.

These representations can help institutions act, but they are incomplete. If treated as reality, they can erase context, uncertainty, dignity, and lived experience.

Health representations should support care, not replace the person or community being represented.

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Mathematics, Computation, and Modeling

A diagnostic classifier may estimate the probability of a condition:

A binary classifier can be evaluated through sensitivity and specificity:

Risk stratification often turns a score into an action threshold:

Public-health models often track population states over time:

These formulas are useful only when uncertainty, validation, clinical context, equity, action pathways, and governance responsibilities are explicit.

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Python Workflow: Health Algorithm Safety Audit

The Python workflow below creates a dependency-light audit for algorithms in health care and public health. It simulates health algorithm systems, scores patient impact, population impact, clinical validation, equity readiness, privacy readiness, human review, workflow integration, monitoring, governance readiness, and health algorithm risk, then writes reproducible CSV and JSON outputs.

# algorithms_in_health_care_and_public_health_audit.py
# Dependency-light workflow for clinical decision support, diagnostics,
# triage, public health, equity, privacy, validation, and governance.

from __future__ import annotations

from dataclasses import asdict, dataclass
from pathlib import Path
from statistics import mean
import csv
import json
from datetime import datetime, timezone

ARTICLE_ROOT = Path(__file__).resolve().parents[1]
TABLES = ARTICLE_ROOT / "outputs" / "tables"
JSON_DIR = ARTICLE_ROOT / "outputs" / "json"


@dataclass(frozen=True)
class HealthAlgorithmConfig:
    article: str = "algorithms_in_health_care_and_public_health"
    high_health_risk_threshold: float = 0.70
    low_governance_threshold: float = 0.65
    high_patient_impact_threshold: float = 0.80
    high_population_impact_threshold: float = 0.80


def timestamp_utc() -> str:
    return datetime.now(timezone.utc).isoformat()


def write_csv(path: Path, rows: list[dict[str, object]]) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    if not rows:
        path.write_text("", encoding="utf-8")
        return
    fieldnames = sorted({key for row in rows for key in row.keys()})
    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore")
        writer.writeheader()
        writer.writerows(rows)


def write_json(path: Path, payload: object) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8")


def health_systems() -> list[dict[str, object]]:
    return [
        {"system_id": "sepsis_alert_model", "patient_impact": 0.92, "population_impact": 0.54, "clinical_validation": 0.70, "equity_readiness": 0.58, "privacy_readiness": 0.72, "human_review": 0.66, "workflow_integration": 0.62, "monitoring": 0.64, "governance": 0.60},
        {"system_id": "radiology_triage_classifier", "patient_impact": 0.88, "population_impact": 0.42, "clinical_validation": 0.78, "equity_readiness": 0.62, "privacy_readiness": 0.76, "human_review": 0.74, "workflow_integration": 0.68, "monitoring": 0.70, "governance": 0.68},
        {"system_id": "public_health_outbreak_detector", "patient_impact": 0.48, "population_impact": 0.92, "clinical_validation": 0.66, "equity_readiness": 0.64, "privacy_readiness": 0.56, "human_review": 0.72, "workflow_integration": 0.74, "monitoring": 0.82, "governance": 0.70},
        {"system_id": "care_management_risk_list", "patient_impact": 0.78, "population_impact": 0.76, "clinical_validation": 0.62, "equity_readiness": 0.50, "privacy_readiness": 0.70, "human_review": 0.60, "workflow_integration": 0.58, "monitoring": 0.54, "governance": 0.56},
    ]


def score_system(row: dict[str, object], config: HealthAlgorithmConfig) -> dict[str, object]:
    governance_readiness = mean([
        float(row["clinical_validation"]),
        float(row["equity_readiness"]),
        float(row["privacy_readiness"]),
        float(row["human_review"]),
        float(row["workflow_integration"]),
        float(row["monitoring"]),
        float(row["governance"]),
    ])
    impact = mean([
        float(row["patient_impact"]),
        float(row["population_impact"]),
    ])
    health_algorithm_risk = mean([
        impact,
        1.0 - float(row["clinical_validation"]),
        1.0 - float(row["equity_readiness"]),
        1.0 - governance_readiness,
    ])

    recommendation = "governed_use_with_monitoring"
    if health_algorithm_risk >= config.high_health_risk_threshold and governance_readiness < config.low_governance_threshold: recommendation = "redesign_before_clinical_or_public_health_use" elif float(row["patient_impact"]) >= config.high_patient_impact_threshold and governance_readiness < 0.75: recommendation = "clinical_safety_review_required" elif float(row["population_impact"]) >= config.high_population_impact_threshold and governance_readiness < 0.75:
        recommendation = "public_health_governance_review_required"
    elif float(row["equity_readiness"]) < 0.60:
        recommendation = "health_equity_review_required"
    elif governance_readiness < config.low_governance_threshold:
        recommendation = "governance_review_required"

    return {
        "system_id": row["system_id"],
        "patient_impact": round(float(row["patient_impact"]), 6),
        "population_impact": round(float(row["population_impact"]), 6),
        "clinical_validation": round(float(row["clinical_validation"]), 6),
        "equity_readiness": round(float(row["equity_readiness"]), 6),
        "privacy_readiness": round(float(row["privacy_readiness"]), 6),
        "human_review": round(float(row["human_review"]), 6),
        "workflow_integration": round(float(row["workflow_integration"]), 6),
        "monitoring": round(float(row["monitoring"]), 6),
        "governance": round(float(row["governance"]), 6),
        "impact_score": round(impact, 6),
        "governance_readiness_score": round(governance_readiness, 6),
        "health_algorithm_risk_score": round(health_algorithm_risk, 6),
        "recommendation": recommendation,
    }


def health_governance_register() -> list[dict[str, str]]:
    return [
        {"control": "algorithm_inventory", "review_question": "Is the health algorithm recorded with owner, purpose, data, setting, and decision role?", "status": "required"},
        {"control": "clinical_validation", "review_question": "Has performance been validated for the intended population, setting, workflow, and use?", "status": "required"},
        {"control": "equity_review", "review_question": "Are subgroup performance, missingness, access barriers, and proxy labels reviewed?", "status": "required"},
        {"control": "privacy_and_security_review", "review_question": "Are data minimization, access controls, security, and secondary-use limits documented?", "status": "required"},
        {"control": "human_review_protocol", "review_question": "Are clinician review, override authority, escalation, and accountability defined?", "status": "required"},
        {"control": "monitoring_and_drift_review", "review_question": "Are outcomes, errors, alert fatigue, drift, overrides, and harms monitored?", "status": "required"},
        {"control": "stop_rule", "review_question": "Can the system be paused, limited, rolled back, or retired when unsafe?", "status": "required"},
    ]


def main() -> None:
    config = HealthAlgorithmConfig()
    systems = health_systems()
    audit = [score_system(row, config) for row in systems]
    controls = health_governance_register()

    summary = {
        "article": config.article,
        "timestamp_utc": timestamp_utc(),
        "systems_reviewed": len(audit),
        "systems_requiring_redesign": sum(1 for row in audit if row["recommendation"] == "redesign_before_clinical_or_public_health_use"),
        "systems_requiring_clinical_safety_review": sum(1 for row in audit if row["recommendation"] == "clinical_safety_review_required"),
        "systems_requiring_public_health_review": sum(1 for row in audit if row["recommendation"] == "public_health_governance_review_required"),
        "systems_requiring_equity_review": sum(1 for row in audit if row["recommendation"] == "health_equity_review_required"),
        "mean_health_algorithm_risk_score": round(mean(float(row["health_algorithm_risk_score"]) for row in audit), 6),
        "mean_governance_readiness_score": round(mean(float(row["governance_readiness_score"]) for row in audit), 6),
        "mean_impact_score": round(mean(float(row["impact_score"]) for row in audit), 6),
        "governance_controls": len(controls),
        "interpretation": "Health algorithm governance should connect patient impact, population impact, clinical validation, equity readiness, privacy readiness, human review, workflow integration, monitoring, audit trails, and stop authority.",
    }

    write_csv(TABLES / "health_systems.csv", systems)
    write_csv(TABLES / "health_algorithm_safety_audit.csv", audit)
    write_csv(TABLES / "health_governance_register.csv", controls)
    write_csv(TABLES / "health_algorithm_summary.csv", [summary])

    write_json(JSON_DIR / "health_algorithm_config.json", asdict(config))
    write_json(JSON_DIR / "health_algorithm_safety_audit.json", audit)
    write_json(JSON_DIR / "health_governance_register.json", controls)
    write_json(JSON_DIR / "health_algorithm_summary.json", summary)

    print("Algorithms in health care and public health audit complete.")
    print(TABLES / "health_algorithm_summary.csv")


if __name__ == "__main__":
    main()

This workflow turns health algorithm governance into a reproducible review artifact: patient impact, population impact, clinical validation, equity readiness, privacy readiness, human review, workflow integration, monitoring, governance readiness, health algorithm risk, and recommendation are documented together.

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R Workflow: Health Algorithm Diagnostics

The R workflow reads the generated CSV outputs, summarizes health algorithm risk and governance readiness, visualizes system components, and writes an additional diagnostic table.

# algorithms_in_health_care_and_public_health_summary.R
args <- commandArgs(trailingOnly = FALSE)
file_arg <- grep("^--file=", args, value = TRUE)

if (length(file_arg) > 0) {
  script_path <- normalizePath(sub("^--file=", "", file_arg[1]), mustWork = TRUE)
  article_root <- normalizePath(file.path(dirname(script_path), ".."), mustWork = TRUE)
} else {
  article_root <- getwd()
}

setwd(article_root)

tables_dir <- file.path(article_root, "outputs", "tables")
figures_dir <- file.path(article_root, "outputs", "figures")
dir.create(tables_dir, recursive = TRUE, showWarnings = FALSE)
dir.create(figures_dir, recursive = TRUE, showWarnings = FALSE)

audit_path <- file.path(tables_dir, "health_algorithm_safety_audit.csv")
summary_path <- file.path(tables_dir, "health_algorithm_summary.csv")

if (!file.exists(audit_path)) {
  stop(paste("Missing", audit_path, "Run the Python workflow first."))
}

audit <- read.csv(audit_path, stringsAsFactors = FALSE)
summary <- read.csv(summary_path, stringsAsFactors = FALSE)

png(file.path(figures_dir, "health_algorithm_components.png"), width = 1200, height = 850)
score_matrix <- t(as.matrix(audit[, c("patient_impact", "population_impact", "clinical_validation", "equity_readiness", "privacy_readiness", "governance_readiness_score")]))
barplot(score_matrix,
        beside = TRUE,
        names.arg = audit$system_id,
        las = 2,
        ylim = c(0, 1),
        ylab = "Score",
        main = "Algorithms in Health Care and Public Health: Impact, Validation, Equity, and Governance")
legend("bottomright",
       legend = rownames(score_matrix),
       cex = 0.72,
       bty = "n")
grid()
dev.off()

png(file.path(figures_dir, "health_algorithm_risk_by_system.png"), width = 1000, height = 750)
barplot(audit$health_algorithm_risk_score,
        names.arg = audit$system_id,
        las = 2,
        ylab = "Health Algorithm Risk Score",
        main = "Health Algorithm Risk by System")
grid()
dev.off()

r_summary <- data.frame(
  systems_reviewed = summary$systems_reviewed[1],
  systems_requiring_redesign = summary$systems_requiring_redesign[1],
  systems_requiring_clinical_safety_review = summary$systems_requiring_clinical_safety_review[1],
  systems_requiring_public_health_review = summary$systems_requiring_public_health_review[1],
  systems_requiring_equity_review = summary$systems_requiring_equity_review[1],
  mean_health_algorithm_risk_score = summary$mean_health_algorithm_risk_score[1],
  mean_governance_readiness_score = summary$mean_governance_readiness_score[1],
  mean_impact_score = summary$mean_impact_score[1],
  governance_controls = summary$governance_controls[1],
  diagnostic_note = "Health algorithm governance should connect patient impact, population impact, clinical validation, equity readiness, privacy readiness, human review, workflow integration, monitoring, audit trails, and stop authority."
)

write.csv(r_summary, file.path(tables_dir, "r_health_algorithm_diagnostic_summary.csv"), row.names = FALSE)
print(r_summary)

The R layer turns patient impact, population impact, clinical validation, equity readiness, privacy readiness, governance readiness, and health algorithm risk into visible diagnostic summaries that support safety review, equity review, privacy review, public-health governance, and responsible care-system implementation.

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GitHub Repository

The companion repository contains reproducible workflows, synthetic data, audit outputs, calculators, documentation, and multilingual examples for this article.

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A Practical Method for Responsible Health Algorithms

Responsible health algorithms should begin with care purpose, affected people, clinical context, and public-health responsibility before model optimization. The central question is not “Does this model predict?” but “Does this system safely improve care, prevention, access, or public health in the setting where it will be used?”

This method treats health algorithms as care-system interventions that require safety, equity, privacy, and accountability.

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Common Pitfalls

Algorithms in health care and public health can fail when institutions confuse prediction with care, cost with need, documentation with reality, validation with safety, alerting with action, or surveillance with trust.

Health algorithms should make care safer and more equitable, not merely more computational.

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Why Health Algorithms Require Responsible Care Governance

Algorithms in health care and public health show how computational reasoning can support diagnosis, triage, prevention, surveillance, capacity planning, claims review, population health, and clinical decision-making. These systems can help institutions see risk, coordinate care, detect outbreaks, allocate resources, and reduce missed information. They can also intensify bias, burden, overreliance, privacy risk, unsafe automation, and unequal access.

Responsible health algorithms require clinical validation, workflow integration, equity review, privacy safeguards, human judgment, audit trails, monitoring, patient and population impact review, appeal or correction pathways where appropriate, and stop rules.

The central question is not whether a health algorithm is technically impressive. It is whether it safely supports care, prevention, public health, dignity, and accountable health-system responsibility. AI belongs in the toolkit, not in control.

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Related Articles

• Algorithms in Finance, Markets, and Risk
• Algorithms in Climate, Energy, and Infrastructure
• Decision Under Uncertainty and Computational Risk
• Automation Bias and Human Overreliance
• Algorithmic Risk Management and AI Governance

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Further Reading

• Topol, E. (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York: Basic Books.
• Obermeyer, Z. et al. (2019) ‘Dissecting racial bias in an algorithm used to manage the health of populations’, Science, 366(6464), pp. 447–453.
• Rajkomar, A., Dean, J. and Kohane, I. (2019) ‘Machine learning in medicine’, New England Journal of Medicine, 380, pp. 1347–1358.
• Char, D.S., Shah, N.H. and Magnus, D. (2018) ‘Implementing machine learning in health care — addressing ethical challenges’, New England Journal of Medicine, 378, pp. 981–983.
• Wiens, J. et al. (2019) ‘Do no harm: A roadmap for responsible machine learning for health care’, Nature Medicine, 25, pp. 1337–1340.
• World Health Organization (2021) Ethics and Governance of Artificial Intelligence for Health. Geneva: WHO.
• National Academy of Medicine (2019) Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril.

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References

• Char, D.S., Shah, N.H. and Magnus, D. (2018) ‘Implementing machine learning in health care — addressing ethical challenges’, New England Journal of Medicine, 378, pp. 981–983.
• National Academy of Medicine (2019) Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. Available at: https://nam.edu/artificial-intelligence-special-publication/.
• Obermeyer, Z., Powers, B., Vogeli, C. and Mullainathan, S. (2019) ‘Dissecting racial bias in an algorithm used to manage the health of populations’, Science, 366(6464), pp. 447–453.
• Rajkomar, A., Dean, J. and Kohane, I. (2019) ‘Machine learning in medicine’, New England Journal of Medicine, 380, pp. 1347–1358.
• Topol, E. (2019) Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. New York: Basic Books.
• Wiens, J., Saria, S., Sendak, M. et al. (2019) ‘Do no harm: A roadmap for responsible machine learning for health care’, Nature Medicine, 25, pp. 1337–1340.
• World Health Organization (2021) Ethics and Governance of Artificial Intelligence for Health. Geneva: WHO. Available at: https://www.who.int/publications/i/item/9789240029200.

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