AI w medycynie i ochronie zdrowia

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

Uczenie maszynowe w diagnostyce, dokumentacji klinicznej, prognozowaniu ryzyka i monitoringu pacjentów, wraz z walidacją kliniczną i regulacjami wyrobów medycznych.

Machine learning for diagnosis, clinical documentation, risk prediction and patient monitoring, together with clinical validation and medical-device regulation.

In one sentence

Programy, które pomagają lekarzom i pacjentom: czytają badania obrazowe, piszą notatki, ostrzegają przed pogorszeniem zdrowia i odpowiadają na pytania.

Software that helps doctors and patients: it reads scans, writes notes, warns about worsening health and answers medical questions.

Problem it solves

Lekarzy brakuje, kolejki rosną, a specjaliści nie mają czasu, by dokładnie obejrzeć każde zdjęcie i wypełnić każdy dokument.

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

Działa jak drugi, niestrudzony asystent: robi wstępny przegląd i zaznacza, co wymaga uwagi, a decyzję zostawia człowiekowi.

Works like a tireless second assistant: it does a first pass and flags what needs attention, leaving the decision to a person.

Everyday example

Lekarz patrzy na pacjenta, a nie w ekran, bo program sam spisuje rozmowę do karty; wieczorem zegarek pacjenta sygnalizuje nieregularne tętno.

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…

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

Subtopics

Analiza obrazów medycznych (Medical image analysis): 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): 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): 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): Analysis of digital whole-slide images (WSI): cancer classification, metastasis detection and biomarker prediction from tissue images.
  - Modele bazowe w patologii (Pathology foundation models): 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): 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)): 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): 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): 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): 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): 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)): 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): 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): 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): 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)): 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): 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): 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): 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): 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): 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): 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): 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): 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)): 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): 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): 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): 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): Deep networks for classifying ECG recordings, especially rhythms and arrhythmias; other uses require separate validation.
- Automatyczna ocena faz snu (Automated sleep staging): 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): 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): 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)): 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): 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): 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): 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): Reporting guidelines (TRIPOD+AI, CONSORT-AI, SPIRIT-AI, DECIDE-AI, TRIPOD-LLM) for studies of prediction models and AI interventions in medicine.

Sources

List of Artificial Intelligence-Enabled Medical Devices · U.S. Food and Drug Administration