AI Uses in the Health Industry

Scope: Practical, clinician-facing overview of how artificial intelligence (AI) is being used across health care today — diagnostics, drug discovery, clinical decision support, telemedicine, operations, and patient engagement — plus opportunities, risks, regulation and practical tips for safe adoption.

Executive summary (one paragraph)

AI is already reshaping medicine: from faster, more sensitive image interpretation and triage to accelerating drug discovery, tailoring treatments with genomics, automating documentation, and supporting remote monitoring. These gains come with important caveats — bias, data governance, explainability, clinical validation and liability remain active challenges. Responsible adoption means using AI to augment clinicians, not replace them, and implementing rigorous evaluation, monitoring and governance in each local setting. PMC+2PMC+2

1. What do we mean by “AI” in health care?

“AI” is an umbrella term that includes machine learning (ML), deep learning, natural language processing (NLP), and — more recently — large (multimodal) generative models. In practice this ranges from classical predictive models (risk scores), to convolutional neural nets that read images, to transformer models that interpret notes or generate patient-facing text. AI systems may be embedded in devices (software as a medical device — SaMD), cloud platforms, electronic health records (EHRs) or point-of-care apps. PMC+1

2. High-value clinical applications (what’s being used today)

2.1 Medical imaging and diagnostic support

Radiology, pathology and ophthalmology have been the most mature clinical areas for AI adoption. Deep-learning models can detect lung nodules, diabetic retinopathy, intracranial hemorrhage and breast-cancer changes on images — sometimes matching expert-level sensitivity for specific tasks — and are widely used to prioritise critical cases (triage) and speed reporting. However, tools are typically narrow (one task, one modality) and require human oversight. Real-world deployment studies show improved workflow and earlier detection but also highlight false positives and variable generalisability across sites. PMC+2The Lancet+2

2.2 Clinical decision support (CDS) and risk prediction

AI-powered clinical decision support systems (AI-CDSS) can synthesise EHR data, labs, imaging and vitals to predict deterioration (e.g., sepsis), support diagnosis, recommend medication adjustments or flag drug interactions. Recent systematic reviews and implementation studies show potential to reduce workload and improve adherence to guidelines, but integration into clinician workflows and alert fatigue are common barriers. Successful systems are co-designed with clinicians and continuously monitored after deployment. PMC+1

2.3 Drug discovery and development

AI accelerates target identification, virtual screening, de-novo molecule design, and optimisation of lead compounds. Machine learning models reduce the search space of chemical libraries and predict ADMET (absorption, distribution, metabolism, excretion, toxicity) properties earlier, shortening timelines and costs. AI is also assisting in trial design and patient selection for precision enrolment. While promising, most AI-discovered candidates still require the full clinical trial pathway. PMC+1

2.4 Genomics and precision medicine

AI methods enable rapid interpretation of sequencing data, variant classification, and phenotype prediction from multi-omics datasets. In oncology, AI helps match mutations to therapies and predicts likely responders, improving personalization of treatment plans. These algorithms often power tumor-board discussions and research pipelines. Nature

2.5 Telemedicine, remote monitoring & digital triage

Smart algorithms interpret continuous sensor data (wearables, implanted devices) to identify arrhythmias, blood glucose trends, or early signs of decompensation. In telehealth, AI triage chatbots and symptom checkers help direct patients to the right level of care; in remote monitoring, ML models detect actionable trends and prioritise clinician review. Early evidence indicates improved access and better chronic-disease follow-up when systems are integrated thoughtfully. PMC+1

2.6 Natural language processing (NLP) & documentation

NLP models extract problem lists, medications, and key findings from free-text notes; generative models can draft visit summaries and automate coding or prior-authorisation requests. These capabilities reduce administrative burden and free clinician time — but must be validated for accuracy and audited for hallucinations when generative outputs are used. The Lancet+1

2.7 Surgical robotics and workflow optimisation

Robotic systems augmented with AI can assist with instrument tracking, surgical planning and intraoperative guidance. AI is also used behind the scenes to optimise operating-room scheduling, staff allocation and supply-chain logistics, increasing throughput and decreasing delays. PMC

3. System and operational uses (often overlooked but high impact)

AI is not only “clinical” — it’s transforming system operations: hospital bed management, supply-chain forecasting, revenue-cycle automation, automated coding and fraud detection. These operational gains can free resources for frontline care and are often lower-risk entry points for health systems to gain experience with AI governance. Healthcare Bulletin

4. Evidence, performance and real-world effectiveness

A rapid expansion of AI research has produced hundreds of models and thousands of publications. Reviews in 2023–2025 show that while many algorithms excel in retrospective validation, fewer have robust prospective or randomized evaluations that demonstrate patient-level benefit in routine practice. High-performing models in controlled datasets may underperform in new populations due to dataset shift, different imaging protocols, demographic differences or selection biases. Real-world monitoring and post-market surveillance are therefore crucial. PMC+1

5. Safety, ethics and governance — what keeps clinicians awake at night

Key concerns that must be addressed for safe AI use include:

Bias and equity: Models trained on skewed datasets reproduce or amplify disparities (e.g., poorer performance in underrepresented ethnic groups). Equity-focused evaluation and diverse training data are essential. PMC
Explainability & trust: “Black box” models are harder to trust in clinical decisions; clinicians often need interpretable outputs or calibrated confidence intervals. World Health Organization
Data privacy & security: Health data are sensitive; robust de-identification, governance frameworks and secure pipelines are required. World Health Organization
Clinical validation & monitoring: Continuous performance assessment post-deployment (monitoring for drift) and clinical outcome studies must be mandatory components of adoption. PMC
Regulation and liability: Regulatory bodies are catching up: the FDA and other regulators now publish guidance for SaMD and AI-enabled devices, stressing transparency, risk management and quality systems; but legal liability (who’s responsible when AI contributes to harm) remains a thorny issue for clinicians and institutions. U.S. Food and Drug Administration+1
Generative AI specific risks: Large multimodal and generative models (LLMs) introduce risks of hallucination, incorrect medical advice and data leakage; WHO and other bodies have issued guidance on governance and ethical use. World Health Organization+1

6. Regulatory landscape (practical snapshot)

Regulators have moved from permissive experimentation to closer oversight:

FDA: Has long-standing SaMD frameworks and recently published draft guidance specifically for AI-enabled medical devices and adaptive algorithms (2024–2025), emphasising transparency, monitoring, and bias mitigation. Developers are expected to provide performance evidence, risk-management plans, and post-market monitoring strategies. U.S. Food and Drug Administration+1
WHO: Published ethics and governance guidance for AI in health, and specific guidance for large multimodal models — stressing equity, human oversight, explainability, and global accessibility. World Health Organization+1
Journals & professional societies: Many specialty societies publish position statements on acceptable use (e.g., radiology societies on AI triage and human oversight). Clinicians should check local and speciality guidance before clinical use. PMC

7. Practical advice for clinicians and health managers (how to adopt thoughtfully)

7.1 Evaluate clinical need first

Start with a clear problem statement: wait-time reduction, mortality reduction, diagnostic accuracy improvement, or administrative efficiency. Align AI selection with measurable outcomes and stakeholder needs. PMC

7.2 Insist on external validation and regulatory status

Prefer solutions with independent external validation, preferably in settings similar to yours. Check regulatory clearances (FDA, CE marking) and published clinical-impact studies. PMC+1

7.3 Pilot, measure, iterate

Run supervised pilots with clinician oversight, collect metrics (sensitivity/specificity, false alarms, time saved, patient outcomes), and iterate. Do not deploy “at scale” without controlled rollout and monitoring. PMC

7.4 Governance, consent and documentation

Create an AI governance committee (clinical, IT, legal, ethics), define data-use agreements, patient consent procedures where applicable, and maintain explicit documentation of model versions, training data provenance and performance metrics. World Health Organization

7.5 Train clinicians & communicate limits

Train end-users on how the tool works, its intended use, limitations, and failure modes. Make it explicit that AI augments — not replaces — clinical judgement. Provide clear escalation and override pathways. PMC

7.6 Monitor for bias and drift

Implement routine audits to detect differential performance by sex, age, ethnicity or other variables. Monitor model drift and have retraining/recalibration plans. PMC

7.7 Prepare for liability & incident response

Work with legal and risk teams to define responsibility for AI-assisted decisions, and build incident response plans for adverse events involving AI outputs. Keep thorough logs for traceability. The Guardian

8. Use-cases with demonstrated benefit (illustrative examples)

Stroke triage: AI that detects large-vessel occlusion on CT can flag urgent cases, shortening door-to-treatment times. PMC
Diabetic retinopathy screening: Autonomous or assistive AI tools have enabled scaled screening programs, increasing early detection in primary care settings. PMC
Sepsis early warning: ML models monitoring vitals and labs have reduced time to recognition in pilot programmes when tightly integrated into workflows. PMC
Drug discovery acceleration: AI predicted candidate small molecules and repurposed drugs faster than traditional screens in multiple proof-of-concept pipelines. PMC

9. Limitations and open research questions

Causal vs correlational inferences: Many models learn correlations but not causal mechanisms — interventions based on predictions may not change outcomes unless causal pathways are understood.
Generalisability: How to ensure models trained in one health system generalise to another is still an open problem.
Clinical trial evidence: We need more randomized, prospective trials showing patient-level benefit (mortality, morbidity, cost-effectiveness).
Explainability vs performance tradeoffs: High-performing models are often less interpretable. Work is ongoing to provide clinically meaningful explanations. PMC+1

10. The future: where AI is heading in health care

Generative AI as an assistant: LLMs that summarise charts, draft notes, generate patient education, and synthesise literature will become commonplace — provided hallucination and safety issues are solved. The Lancet+1
Closed-loop adaptive care: Integrated AI systems that combine monitoring, prediction and automated interventions (e.g., insulin dosing algorithms) will expand, especially in chronic disease management.
Precision therapeutics: AI will increasingly aid N-of-1 treatment choices by integrating genomics, proteomics and longitudinal data. PMC
Decentralised AI validation networks: Federated learning and privacy-preserving methods will allow models to learn across institutions without centralising sensitive data. World Health Organization

11. Key takeaways (short)

AI already delivers value in imaging, drug discovery, monitoring and admin workflows — but benefits are typically task-specific and require human oversight. PMC+1
Rigorous validation, governance and monitoring are non-negotiable prerequisites for safe clinical use. PMC+1
Start small, measure carefully, scale responsibly — prioritize problems with clear clinical benefit and measurable outcomes. PMC
Ethics, equity and regulation must drive adoption — clinicians and health leaders must insist on transparent performance data and equitable validation. PMC+1

Selected further reading & references

Najjar R. et al., Redefining Radiology: A Review of Artificial Intelligence (2023). PMC

Nature — AI in health care (collection & reviews). Nature

WHO — Harnessing artificial intelligence for health & Ethics and governance of artificial intelligence for health (guidance). World Health Organization+1

FDA — Artificial Intelligence and Software as a Medical Device (SaMD) and recent draft guidance for AI-enabled medical devices (Jan 2025). U.S. Food and Drug Administration+1

PMC review — Artificial-Intelligence-Based Clinical Decision Support (2024). PMC

PMC review — Artificial intelligence in drug discovery and development (2024). PMC

Lancet Digital Health & eClinicalMedicine — recent pieces on generative AI and diagnostic radiology (2024–2025). The Lancet+1

Review — Ethical and regulatory challenges of AI technologies in healthcare (2024). PMC

Dr.Pothireddy Surendranath reddy

Search This Blog

Artificial Intelligence Uses in the Health Industry – Expert Insights by Dr. Pothireddy Surendranath Reddy

AI Uses in Health Industry