# AI w medycynie i ochronie zdrowia

EN: **AI in healthcare and medicine** · id `healthai` · aliases: medical AI, healthcare AI, clinical AI, AI w ochronie zdrowia, medyczna AI

Path: [AI Atlas](https://aiatlasgraph.com/llms.txt) › AI w medycynie i ochronie zdrowia

## Description

- PL: Uczenie maszynowe w diagnostyce, dokumentacji klinicznej, prognozowaniu ryzyka i monitoringu pacjentów, wraz z walidacją kliniczną i regulacjami wyrobów medycznych.
- EN: Machine learning for diagnosis, clinical documentation, risk prediction and patient monitoring, together with clinical validation and medical-device regulation.

## In one sentence

- PL: Programy, które pomagają lekarzom i pacjentom: czytają badania obrazowe, piszą notatki, ostrzegają przed pogorszeniem zdrowia i odpowiadają na pytania.
- EN: Software that helps doctors and patients: it reads scans, writes notes, warns about worsening health and answers medical questions.

## Problem it solves

- PL: Lekarzy brakuje, kolejki rosną, a specjaliści nie mają czasu, by dokładnie obejrzeć każde zdjęcie i wypełnić każdy dokument.
- EN: There are too few doctors, queues keep growing, and specialists lack the time to study every scan and fill in every form.

## What it does

- PL: Działa jak drugi, niestrudzony asystent: robi wstępny przegląd i zaznacza, co wymaga uwagi, a decyzję zostawia człowiekowi.
- EN: Works like a tireless second assistant: it does a first pass and flags what needs attention, leaving the decision to a person.

## Everyday example

- PL: Lekarz patrzy na pacjenta, a nie w ekran, bo program sam spisuje rozmowę do karty; wieczorem zegarek pacjenta sygnalizuje nieregularne tętno.
- EN: The doctor looks at the patient, not the screen, because software writes the conversation into the record; that evening the patient's watch flags an irregular pulse.

## This concerns you if you…

- PL: korzystasz z publicznej lub prywatnej opieki zdrowotnej
- PL: nosisz zegarek lub opaskę mierzącą tętno
- PL: pracujesz w szpitalu, przychodni lub laboratorium
- EN: use public or private healthcare
- EN: wear a watch or band that measures your pulse
- EN: work in a hospital, clinic or lab

## Keywords

medical imaging · clinical NLP · EHR · FDA AI-enabled devices · SaMD · MIMIC-IV · MONAI · clinical decision support · wearables · HealthBench · TRIPOD+AI · digital health

## How it works

- PL: Modele uczą się na danych klinicznych: obrazach medycznych, notatkach, elektronicznej dokumentacji i sygnałach fizjologicznych. Wiele dopuszczonych narzędzi ma wąskie deklarowane przeznaczenie, np. wykrywanie, segmentację lub triaż. Systemy do dokumentacji i odpowiadania na pytania mogą podlegać innym wymaganiom zależnie od ich przeznaczenia.
- EN: Models learn from clinical data: medical images, notes, electronic health records and physiological signals. Many authorised tools have a narrow intended purpose, such as detection, segmentation or triage. Documentation and question-answering systems may fall under different requirements depending on their intended purpose.

## Limits

- PL: Dane są rozproszone, chronione i różne w każdym szpitalu, więc modele słabo się przenoszą. Dobry wynik na zbiorze testowym nie dowodzi korzyści dla pacjenta: potrzebne są badania prospektywne, nadzór po wdrożeniu i zgoda regulatora.
- EN: Data are scattered, protected and different in every hospital, so models transfer poorly. A good test-set score does not prove patient benefit: prospective studies, post-deployment monitoring and regulatory approval are needed.

## Key terms

- PL: **SaMD**: Oprogramowanie, które samo jest wyrobem medycznym i wymaga dopuszczenia.
- PL: **EHR**: Elektroniczna dokumentacja pacjenta: rozpoznania, leki, wyniki badań i notatki.
- PL: **walidacja zewnętrzna**: Sprawdzenie modelu na danych z innego szpitala niż dane treningowe.
- EN: **SaMD**: Software that is itself a medical device and needs approval.
- EN: **EHR**: The electronic patient record: diagnoses, drugs, test results and notes.
- EN: **external validation**: Testing a model on data from a hospital other than the training one.

## Subtopics (8 direct, 37 in total)

- [Analiza obrazów medycznych (Medical image analysis)](https://aiatlasgraph.com/t/medimaging.md): Classification, detection and segmentation on X-ray, CT, MRI, ultrasound, mammography and histopathology images, usually as decision support for clinicians.
  - [Automatyczne opisy badań obrazowych (Radiology report generation)](https://aiatlasgraph.com/t/radreportgen.md): Generating the radiology report text (findings and impression) from a medical image with models that couple an image encoder to a language model.
  - [Ocena znamion i zmian skórnych ze zdjęć (AI classification of skin lesions)](https://aiatlasgraph.com/t/dermatologyai.md): Neural-network classification of clinical and dermoscopic skin-lesion images (e.g. melanoma versus naevus), with a focus on performance across skin tones.
  - [Patologia cyfrowa: analiza skanów preparatów tkankowych (Computational pathology on whole-slide images)](https://aiatlasgraph.com/t/comppathology.md): Analysis of digital whole-slide images (WSI): cancer classification, metastasis detection and biomarker prediction from tissue images.
    - [Modele bazowe w patologii (Pathology foundation models)](https://aiatlasgraph.com/t/pathologyfm.md): Tile and slide encoders pretrained self-supervised on millions of histopathology images, used as general feature extractors for pathology tasks.
    - [Uczenie z etykiety całego preparatu (wiele instancji) (Multiple instance learning for whole-slide images)](https://aiatlasgraph.com/t/pathologymil.md): Weakly supervised learning in which a slide is a bag of tiles with one label, and attention pooling combines tile features into a slide-level prediction.
  - [Przesiewowa ocena zdjęć dna oka (AI screening of retinal images (diabetic retinopathy))](https://aiatlasgraph.com/t/retinalscreening.md): CNN classification of fundus photographs to detect referable diabetic retinopathy, including autonomous operation without a human reader.
  - [Segmentacja narządów i zmian na obrazach medycznych (Medical image segmentation)](https://aiatlasgraph.com/t/medsegmentation.md): Assigning each voxel of a 2D or 3D image an organ or lesion label, for radiotherapy planning, volumetry and surgery among others.
  - [Wykrywanie zmian na zdjęciach radiologicznych (Computer-aided detection and triage in radiology)](https://aiatlasgraph.com/t/radiologyai.md): CADe/CADx and triage systems that detect, localise and prioritise findings in radiology images, for example in screening mammography.
    - [AI w przesiewowej mammografii (AI in mammography screening)](https://aiatlasgraph.com/t/mammographyai.md): Supporting screening mammography with models that score cancer risk, used to triage between single and double reading and to mark suspicious regions.
- [Język kliniczny i dokumentacja medyczna (Clinical language processing and documentation)](https://aiatlasgraph.com/t/clinicaldocs.md): NLP on clinical text: note generation, information extraction, diagnosis coding, de-identification and summarisation of patient records.
  - [Automatyczne notatki z wizyty (ambient scribe) (Ambient clinical documentation (AI scribes))](https://aiatlasgraph.com/t/ambientscribe.md): Systems that turn a visit recording into a structured clinical note (e.g. SOAP) for the clinician to review and sign.
  - [Kodowanie rozpoznań i wyciąganie danych z dokumentacji (Automated clinical coding and information extraction)](https://aiatlasgraph.com/t/clinicalcoding.md): Multi-label classification of clinical notes into ICD codes, plus extraction of clinical entities (diseases, drugs, doses) and mapping to terminologies.
  - [Szkice odpowiedzi na wiadomości od pacjentów (AI-drafted replies to patient messages)](https://aiatlasgraph.com/t/patientmessagedrafts.md): Language-model generation of draft replies to patient messages in a portal integrated with the health record, for clinician editing and approval.
  - [Usuwanie danych osobowych z dokumentacji medycznej (Clinical text de-identification)](https://aiatlasgraph.com/t/clinicaldeid.md): Detecting and removing or replacing protected health information (PHI) in clinical text, framed as named-entity recognition.
- [Koncepcja wszechstronnych modeli medycznych (Generalist medical AI (multimodal medical models))](https://aiatlasgraph.com/t/generalistmedai.md): A proposed foundation-model paradigm intended to combine many medical modalities (images, EHRs, results, genomics and text) and perform tasks specified by instructions.
- [Modele językowe w medycynie (Medical large language models)](https://aiatlasgraph.com/t/medicalllm.md): General and medically tuned LLMs used for question answering, work with clinical cases and health communication, together with evaluation of their accuracy and safety.
  - [Ocena modeli na pytaniach i rozmowach medycznych (Evaluating LLMs on medical questions and conversations)](https://aiatlasgraph.com/t/medllmeval.md): Benchmarks and protocols for evaluating medical LLMs: from exam questions to multi-turn conversations scored with physician rubrics and harm review.
  - [Odpowiedzi oparte na literaturze i zaleceniach medycznych (RAG) (Retrieval-augmented generation for medicine)](https://aiatlasgraph.com/t/medicalrag.md): RAG over medical corpora (literature, textbooks and guidelines) that supplies retrieved passages and can improve accuracy while making the basis of an answer easier to inspect.
  - [Wspomaganie diagnozy różnicowej (LLM-assisted differential diagnosis)](https://aiatlasgraph.com/t/diagnosticreasoning.md): Using LLMs to generate a ranked differential-diagnosis list from a case description, standalone or as clinician support.
- [Prognozowanie ryzyka z dokumentacji pacjenta (Clinical risk prediction from health records)](https://aiatlasgraph.com/t/clinicalprediction.md): Diagnostic and prognostic models on electronic health record (EHR) data: early warning, complication risk, survival and readmissions.
  - [Modele bazowe na danych z dokumentacji pacjenta (Foundation models for electronic health records)](https://aiatlasgraph.com/t/ehrfoundation.md): Models pretrained on sequences of structured EHR events (codes, medications, lab results) that produce patient representations for many prediction tasks.
  - [Szacowanie, komu leczenie pomoże (efekt indywidualny) (Individualised treatment effect estimation)](https://aiatlasgraph.com/t/treatmenteffects.md): Estimating the conditional average treatment effect (CATE) from randomised or observational data with causal machine learning, as a basis for treatment recommendations.
  - [Uczenie maszynowe dla czasu do zdarzenia (Machine learning for time-to-event outcomes)](https://aiatlasgraph.com/t/survivalanalysis.md): Models predicting time to an event with censored data, from the Cox model to survival forests and neural networks.
  - [Wczesne ostrzeganie o pogorszeniu stanu (np. sepsa) (Early warning systems for clinical deterioration (e.g. sepsis))](https://aiatlasgraph.com/t/earlywarning.md): Models running continuously on EHR data that flag patients at rising risk of sepsis or deterioration and alert staff.
- [Sprawdzanie objawów i kierowanie po pomoc (Symptom checkers and automated triage)](https://aiatlasgraph.com/t/symptomtriage.md): Patient-facing tools that use answers from an interactive symptom interview to suggest possible diagnoses and the urgency of care.
- [Sygnały z ciała i urządzenia noszone (Physiological signals and wearables)](https://aiatlasgraph.com/t/biosignals.md): Machine learning on physiological time series (ECG, PPG, EEG, polysomnography, wearable data) for detecting and monitoring disease.
  - [Analiza EEG i wykrywanie napadów padaczkowych (Deep learning for EEG and seizure detection)](https://aiatlasgraph.com/t/eegai.md): Detecting seizures in intracranial EEG and epileptiform discharges in scalp EEG with convolutional, recurrent and transformer networks.
  - [Analiza zapisu EKG przez sieci neuronowe (Deep learning for ECG analysis)](https://aiatlasgraph.com/t/ecgai.md): Deep networks for classifying ECG recordings, especially rhythms and arrhythmias; other uses require separate validation.
  - [Automatyczna ocena faz snu (Automated sleep staging)](https://aiatlasgraph.com/t/sleepstaging.md): Classifying 30-second polysomnography epochs (EEG, EOG, EMG) into sleep stages under AASM rules with convolutional and recurrent networks.
  - [Monitorowanie zdrowia z zegarków i opasek (Wearable health monitoring)](https://aiatlasgraph.com/t/wearablemonitoring.md): Algorithms analysing consumer wearable data (PPG, accelerometer and single-lead ECG) for screening, trend tracking and selected remote-monitoring uses.
- [Walidacja kliniczna i dopuszczenie do użytku (Clinical validation and regulatory approval)](https://aiatlasgraph.com/t/clinicalvalidation.md): Evaluating medical models: external validation and prospective studies, reporting standards, medical-device approval and post-deployment monitoring.
  - [Dopuszczenie AI jako wyrobu medycznego (Regulation of AI as a medical device (SaMD))](https://aiatlasgraph.com/t/medicaldevicereg.md): Regulatory frameworks for AI software with a medical purpose: risk classes, approval pathways, change control plans and post-market surveillance.
  - [Nadzór nad modelem po wdrożeniu w szpitalu (Post-deployment monitoring of clinical AI)](https://aiatlasgraph.com/t/postdeploymonitoring.md): Continuous checking of a clinical model's inputs, outputs and performance after deployment, with drift detection, subgroup analysis and a response procedure.
  - [Przenoszenie modeli między szpitalami i ukryte skróty (Cross-site generalisation and shortcut learning in medical AI)](https://aiatlasgraph.com/t/crosssiteshift.md): Loss of medical model performance when the site, equipment or population changes, due to distribution shift and shortcut learning (confounders).
  - [Sprawdzanie stronniczości modeli wobec grup pacjentów (Bias and equity audits of clinical algorithms)](https://aiatlasgraph.com/t/clinicalbias.md): Assessing differences in clinical models' performance and consequences across patient groups and finding bias sources, including proxy labels.
  - [Standardy raportowania badań nad AI w medycynie (Reporting guidelines for clinical AI studies)](https://aiatlasgraph.com/t/reportingguidelines.md): Reporting guidelines (TRIPOD+AI, CONSORT-AI, SPIRIT-AI, DECIDE-AI, TRIPOD-LLM) for studies of prediction models and AI interventions in medicine.

## Related topics

Untyped, symmetric "see also" links.

- [AI w edukacji (AI in education)](https://aiatlasgraph.com/t/aied.md)
- [AI w finansach (AI in finance)](https://aiatlasgraph.com/t/finance.md)

## Sources

- [List of Artificial Intelligence-Enabled Medical Devices](<https://www.fda.gov/medical-devices/artificial-intelligence-enabled-medical-devices/list-artificial-intelligence-enabled-medical-devices>) `fda-ai-devices-list` · by U.S. Food and Drug Administration

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AI Atlas revision 122 (2026-10-30) · HTML: https://aiatlasgraph.com/t/healthai · index: https://aiatlasgraph.com/llms.txt · everything: https://aiatlasgraph.com/llms-full.txt · updated 2026-10-30