Critical Intelligence Unit

Clinical applications of artificial intelligence: living evidence

Living evidence tables provide high level summaries of key studies and evidence on a particular topic, and links to sources. They are reviewed regularly and updated as new evidence and information is published.

This page is part of the Artificial Intelligence series.

This table describes areas where artificial intelligence is being used for direct clinical care. This includes examples of established tools, pilot studies, or research projects near the stage of being ready for implementation trials.

To develop this living table:

  • Systematic reviews on artificial intelligence published from 2021 to May 2023 were screened in PubMed.
  • Since May 2023, a weekly PubMed search on artificial intelligence has been conducted to continually update the table.
  • Articles are screened for those that describe established uses of AI in clinical care, pilot studies of implementation, or large research studies and/or systematic reviews which identify areas where AI has positive benefits and is close to implementation / trial stage.
  • Grey literature sources are identified via Google or through publications and websites of well-known organisations with relevance to the Australian clinical context (e.g. World Health Organization, OECD, NICE, The King’s Fund, US Centers for Disease Control and Prevention, etc).
  • Initiatives involving robots or robotic surgery without any other AI integrated, are excluded.

Content is grouped by patient journey stage.

Regular checks are conducted for new content and any updates are highlighted.

On this page:

Diagnosis and prognosis

AI application Scientific evidence Actual use in healthcare systems

Clinical risk prediction models

Tools that predict health outcomes either at present (diagnostic) or in the future (prognostic).1

These are mainly produced in the academic context and face several barriers to clinical implementation. Therefore, there are few examples currently rolled out.2

Emerging positive evidence. These have been used for:

Diagnostic tools

  • multiple types of AI, but most commonly artificial neural networks, to diagnose and prognosticate acute appendicitis3
  • deep learning, and in particular convolutional neural network models, to diagnose breast, lung, liver, brain and cervical cancer4
  • discriminant analysis and artificial neural network predictive models which have demonstrated strong ability to differentiate between glaucoma patients and controls5
  • machine learning algorithms to identify patients at high risk of left ventricular hypertrophy. This could reduce unnecessary next step MRI tests6
  • predicting epidermal growth factor receptor mutation status in non-small cell lung cancer7
  • deep learning models of multi-omics data for lung cancer prediction8
  • machine learning techniques for pulmonary embolism diagnosis and risk prediction9
  • AI driven data analysis to identify ovarian cancer10
  • identification of hypertension and assessment of its secondary effects11
  • cardiac diagnostic accuracy, particularly in cardiac imaging and electrocardiogram analysis12
  • diagnosis, classification, and treatment of cardiovascular disease13
  • increasing the efficiency of diagnosis in neuro-ophthalmology14
  • predicting community-acquired pneumonia-related hospitalisation risks, complications, and mortality15
  • predicting lymph node metastasis in early-stage colorectal cancers, potentially refining clinical decisions and improving outcomes16
  • deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments17
  • AI-assisted total body positron emission tomography to facilitate rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort18
  • machine and deep learning techniques to diagnose and classify leukaemia quickly19
  • machine learning analysis of a wide range of different data for the detection and diagnosis of cognitive impairment in Parkinson’s Disease20
  • various AI models which can predict falls risk with a high level of accuracy21
  • detecting new-onset atrial fibrillation in ICU-treated patients22
  • machine learning to improve the accuracy and efficiency of epilepsy diagnosis23 and seizure characterisation24
  • deep learning models which are highly accurate in the detection of voice pathology, including laryngoscopy images acoustic input25
  • ultrasound-based radiomics as a novel approach to predicting breast cancer markers26
  • CT, MRI and PET-CT-based radiomics as a novel approach to detecting genetic mutation status in patients with lung cancer27
  • deep learning to advance diagnostic accuracy and personalised care in heart failure management28

Predicting disease progression

  • machine learning algorithms that predict clinical deterioration in hospitalised adult patients29
  • machine learning algorithms to predict kidney disease progression30
  • machine learning algorithms to predict future epilepsy in people at risk31
  • machine learning algorithms to predict individualised risk and time to diabetic retinopathy progression over 5 years, potentially allowing personalised screening intervals32
  • estimating the course and progression of Alzheimer’s disease at the early stages, which has treatment implications33
  • machine learning algorithms have supported the update of a risk stratification tool for acute coronary syndrome, which predicts mortality and adverse outcomes34
  • large language models can extract more predictive information from clinical notes than can be collected from traditional statistical modelling, for predicting likelihood of a single epileptic event becoming recurrent35
  • deep learning combined with imaging and molecular analysis enables more accurate prognostication of patients with gliomas36
  • deep neural network analysis of the posterior tibial artery waveform can provide independent prediction of death, major adverse cardiac events, and major adverse limb events in patients evaluated for peripheral artery disease37
  • machine learning is a viable approach for developing non-time-based predictions for HIV deaths.38

Prognostic factors

  • machine learning to identify time to surgery as the most important variable associated with colon cancer survival39
  • deep learning to stratify patients with colorectal cancer into risk groups and survival outcomes40
  • deep learning analysis of ECG data to identify patients at high risk of diabetes type 241
  • The CHA(2)DS(2)-VASc score is used to predict the risk of stroke in patients with atrial fibrillation.42 It is the gold-standard risk prediction model for atrial fibrillation management as recommended by the National Institute of Health and Care Excellence.
  • Machine learning has been used to analyse survey data from the ‘45 and up study’,43 to assess the risk of Australians developing cardiovascular disease.
  • In Westmead Hospital’s emergency department, the Sepsis Risk Tool   Dashboard combines a patient’s age, gender and vitals and calculates a sepsis   risk percentage for each patient to support the clinician in assessing if   sepsis is a risk or not. It ensures that patients who are waiting for care   are not missed or deteriorate.44

Early detection of illness via real-time data analysis

 
  • eHealth have a current project detecting sepsis in Emergency waiting rooms from patients’ vital signs.45
  • Multi-lingual AI in Western Sydney LHD has been used to diagnose long COVID via self-report questionnaires conducted over the phone.46

Image analysis

  • Software typically uses deep learning AI, particularly deep learning, to assist with radiology and pathology processes.47
  • Automated whole slide imaging (WSI) scanners are able to render high-resolution images of entire glass slides and combine these images with innovative digital pathology tools to make it possible to integrate imaging into all aspects of pathology reporting including anatomical, clinical, and molecular pathology.48

Consistent positive evidence foridentifying and interpreting mammographic regions suspicious for cancer,49-56

  • including when predicting survival outcomes55
  • particularly positive results when AI assist with double screening52
  • results have included generalisability across datasets from multiple countries.52

Emerging positive evidence onimproved diagnosis and/or prognosis for:

Abdomen / Gastrointestinal

  • a number of common liver diseases including focal liver lesions and fatty liver57-59
  • detection of adenomas and polyps in colonoscopy to facilitate earlier diagnosis of colorectal neoplasia and cancer60-66
  • AI-assisted colonoscopy to automatically detect and characterise endoscopic lesions or abnormalities and facilitate IBD management67-69
  • AI-assisted identification of patients with rectal cancer who could achieve pathological complete response following neoadjuvant chemoradiotherapy70
  • AI-assisted detection, classification, and segmentation of pancreatic lesions71
  • detection of early-stage oesophageal squamous cell carcinoma via computer-aided detection systems72
  • detection of dysmotility within the oesophagus using oesophageal physiologic testing73
  • AI analysis of hepatocellular carcinoma digital slides, to serve as a biomarker for progression-free survival in patients treated with atezolizumab–bevacizumab74
  • detection and differentiation of pancreatic cancer lesion subtypes75
  • detection of pancreatic space-occupying lesion via AI-assisted endoscopic ultrasound76
  • kidney transplant biopsy analysis and digital pathology to identify appropriate donor organs and early signs of organ rejection77
  • identification of patterns in complex histopathology data from renal biopsy78
  • abdominal organ lesions due to improved image quality available via deep learning image reconstruction79
  • the   VisioCyt® test using deep learning automated image processing for urothelial   bladder cancers has improved sensitivity, but lower specificity compared to   standard cytology80
  • identification of lymph node metastasis in  patients with pancreatic ductal adenocarcinoma via CT-based radiomics algorithms and deep learning models81

Chest

  • screening of COVID-19 pneumonia on chest CT, compared to radiologists82
  • ultrasound imaging for predicting lymph node metastasis in breast cancer patients83
  • lung lesions on chest x-ray, compared to non-radiologist physicians84
  • lung cancer screening, especially in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules85
  • pulmonary embolism detection on computed tomography   pulmonary angiogram86
  • diagnosis of pneumothorax via deep learning   (requires training with local data)87
  • identifying phenotypes and risk categories in   patients with normal results on myocardial perfusion imaging88
  • AI-based screening of cardiac amyloidosis-suggestive   uptake in patients undergoing scintigraphy89
  • AI-enhanced electrocardiography for accurate   diagnosis and management of cardiovascular diseases90

Head and neck

  • machine learning for the analysis and evaluation of   various hair and skin assessments91
  • artificial intelligence for analysis of ultrasound images   to diagnose malignant thyroid nodules, particularly when compared to   radiologists and surgeons with less experience92
  • segmenting, detecting, and classifying head and neck   cancers via deep learning93
  • meningioma classification, grading, outcome   prediction, and segmentation via deep or machine learning94
  • differentiating thyroid nodules via deep learning95
  • intracranial haemorrhage assessed via non-contrast CT scans96
  • intracranial aneurysms, including accurate detection and improved clinician sensitivity and reading times97, 98
  • temporomandibular joint diagnosed via AI-analysed cone-beam computed tomography, which eliminates the subjectivity associated with clinician-led diagnosis99
  • caries lesions as diagnosed via various neural networks100
  • classification of glioblastomas from primary CNS lymphomas via MRI-based machine or deep learning techniques101
  • deep learning models for the detection and classification of seizure semiology via video analysis102
  • radiographic estimation of the jaw bone for age and sex can be for use in medico legal scenarios and disaster victim identification103
  • differential diagnosis of neuromyelitis optica spectrum disorder and multiple sclerosis via AI analysis of MRI images104
  • classification of brain tumours via machine learning.105

Eyes106

  • sight-threatening eye diseases, via retinal image analysis, as well as prediction of complex systemic disorders such as heart failure and myocardial infarction107
  • classification of distinct multiple sclerosis subtypes based on retinal features, aiding in disease characterisation and guiding tailored therapeutic strategies108
  • keratoconus diagnosis supported by AI triaging tools109
  • Al diagnostic systems that autonomously diagnoses diabetic retinopathy have been shown to save clinicians in real-world settings significant amounts of time110 as well as being cost-saving in both Indigenous and non-Indigenous populations in Australia111
  • AI detection of diabetic macular oedema112
  • detection of pathological myopia from colour fundus images113
  • screening and early diagnosis of the main causes of blindness via smartphone technology (such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration)114

Other

  • AI in theranostics to analyse personalised patient risk classification, provide prognostic forecasts, or personalised dosimetry115
  • AI assistance in detecting and grading prostate   cancer, predicting patient outcomes, and identifying molecular subtypes116
  • AI supported tumour origin differentiation using   cytological histology117
  • deep learning supported diagnosis of osteoporosis118
  • MRI data analysis allowing for more accurate tumour   characterisation and small tumour segmentation119
  • detecting and segmenting brain metastases via MRI   image analysis by deep learning120
  • artificial intelligence algorithms in osteoarthritis   detection121
  • diagnosis of skin cancers when AI is used to assist   with double screening^122
  • diagnosis of lymphoma by AI (deep learning and   machine learning)123
  • MRI diagnosis of knee ligament or meniscus tears via   deep learning124
  • AI-detected scaphoid and distal radius fractures125
  • rheumatology MRI analysis to address diagnostic   support, disease classification, activity assessment and progression   monitoring126
  • AI-based video monitoring systems offer improved   efficiency and objectivity in the diagnosis and treatment of movement   disorders127
  • generative AI models produce reports of similar clinical accuracy and textual quality to radiologist reports while providing higher textual quality than teleradiologist reports. Implementation of these models could enable timely alerts to life-threatening pathology while aiding imaging interpretation and documentation128

Mixed evidence on the effectiveness ofimage analysisas used for diagnosis, for example:

  • of basal cell carcinoma by non-invasive diagnostic modalities via handcrafted radiomics and deep-learning models129
  • diagnosis and management of pigmented skin lesions via  mobile phone-powered AI technology.130
  • NICE has recommended the use of AI contouring technologies to speed up treatment planning for those undergoing external beam radiotherapy for cancers.131
  • Automated whole slide imaging (WSI) scanners are now approved for use in primary diagnosis by the FDA.48
  • BreastScreen NSW has an AI evaluation and clinical integration project underway to compare deep machine learning AI technology for detecting cancers in screening mammograms, to usual practice with radiologists only .132
  • RPA Virtual hospital   is piloting a wound care app which stores photos of patients’ wounds at  different timepoints in the cloud. It uses image recognition technology to   accurately measure the wound's dimensions, perimeters, and surface area and   analyses the tissue composition of the wound over time as healing takes   place.133

Curative care

AI application Scientific evidence Actual use in healthcare systems

Clinical prediction models

Tools that predict health outcomes either in the future, depending on a variety of patient and treatment parameters.1

Emerging positive evidence.These have been used for:

  • predicting treatment success, such as
    • which patients with COVID-19 would benefit from cortico-therapy to avoid pulmonary fibrosis.134 Results were collated into a visual decision-making tree for easy clinical use
    • appropriate treatment and disease management strategies in kidney disease30
    • pre-operative risk stratification via ECG for high-risk surgical candidates, to inform individualised treatment strategies135
    • tracking and predicting responses to medical and surgical treatments in epilepsy31
    • which patients with prostate cancer may be eligible for clinical trials or have faster progressing cancers136
    • using radiomics to assess the tumour microenvironment, in order to monitoring breast cancer treatments137
    • identifying new categories for treatment outcomes across large cohorts, to better understand responses to methotrexate in juvenile idiopathic arthritis138
    • predicting drug resistance and patient prognosis in pulmonary tuberculosis139
    • enhancing personalised treatments and advancing precision oncology via deep learning in cancer genomics and histopathology140
    • using causal machine learning to predict drug treatment outcomes including efficacy and toxicity141
    • stereotactic radiosurgery outcomes in patients with brain metastasis142
    • using machine learning in melanoma immunotherapy response and prognosis.143
  • predicting severity in order to inform treatment options:
    • predicting the severity of postoperative scars to aid clinicians in scar management treatment decisions.144
  • A machine learning-powered cardiovascular disease risk tool, developed by Apollo Hospitals in India, has proven more accurate than conventional risk stratification approaches commonly used in Europe and India.145 The tool is now being used in at least eight countries and has been adapted for other non-communicable diseases, such as diabetes, asthma and liver fibrosis.145
  • A US hospital uses a model to predict which patients are at highest risk of metastatic disease, to inform their cancer treatment approach.146

AI powered image analysis

Can be used for planning treatment.

Emerging positive evidence. These have been used for:

  • acute medical monitoring
    • monitoring and treatment of acute and hard-to-heal wounds and burns147
  • surgical planning, such as
    • machine learning to improve neuroanatomic localisation and lateralisation in epilepsy31
    • automatic assessment of aortic root morphology for transcatheter aortic valve implantation planning.148
  • OSAIRIS,   the first cloud-based open-source AI imaging software is already saving   radiologists around two hours per patient at Cambridge University Hospitals.   The machine learning based tool is used for the three dimensional   segmentation of normal tissue from cancerous tissue during radiotherapy   planning for patients with head and neck cancer or prostate cancer.149
AI driven surgical decision making

Emerging positive evidence for:

  • assisting in surgical decision-making in real time150
    • Neural network driven rapid nanopore sequencing to enable molecular subclassification of central nervous system tumours.151
 

AI assisted antimicrobial treatment decisions

Emerging positive evidence. This has been used for:

  • improving the rate of appropriate empirical antibiotic treatment while reducing antibiotic costs and the use of broad-spectrum antibiotic treatment.152

Supportive care

AI application Scientific evidence Actual use in healthcare systems

Chatbots – a computer program designed to have a text-based ‘conversation’ with a patient.

Emerging positive evidence when used for:

  • promoting healthy lifestyles
    • increasing physical activity, fruit and vegetable consumption, sleep duration and sleep quality  across a range of populations and age groups, for both short- and longer-term interventions.153
    • promoting healthy lifestyles, smoking cessation, treatment/medication adherence and reducing substance abuse154
  • self-management of mental health conditions155
  • self-management of insulin dosing in type 2 diabetes156

Mixed evidence on effectiveness when used for

  • chronic disease management
    • some apps and wearables are designed to communicate only to the patient, while others also send data back to managing health teams157
    • users find chatbots acceptable and easy to use, but there remains a lack of reliable and comparable evidence on their efficacy155, 157-159
  • supporting physical activity goals154, 160

Limited but negative evidence when usedfor:

  • weight loss and healthy diet support160
  • A multilingual chatbot in Victoria provided support and information on COVID-19. It responded to user queries and links to further information and support services, and is currently being expanded to provide support on broader issues.161
  • MyEleanor, an AI-powered conversational platform, has been used to remind patients in the US of appointments, medication schedules, check in on their health and flag them for clinician follow-up as needed. It was built on thousands of hours of real phone conversations between patients, nurses, and occupational therapists and leverages multiple Machine learning models including Large Language models.162
  • A US hospital is designing ‘virtual nurses’ for chronic care that use an automated ‘voice’ to speak to, patients. They are designed to remind patients to take medication, follow through with care plans, schedule appointments, review medication issues and help patients navigate care-access issues.146

Clinical prediction models for supportive patient care and managing chronic conditions

Emerging positive evidence when used for early identification of at-risk patients.For example:

  • predicting the risks of complications
    • in diabetes care, prediction models can contribute to better quality of care, better autonomy of patients and reduction of complications, costs of medical care and mortality.163
  • predicting risk of readmission
    • AI models can predict older patients at high risk for readmission as well as provide recommendations for transitional care management to effectively reduce readmission rates.164
 

AI-interpreted remote patient monitoring

Limited but emerging positive evidence.This technology can be used for patients at home or for continuous monitoring of inpatients:165

  • AI support for chronic disease management
    • AI-assisted insulin management for type 1 and 2 diabetes166

  • identification of patient health status
    • identification of patients affected by cardiovascular disease, via smartwatches167
    • identification of acceptable ECG data via ‘smartvests’ that monitor vital signs167
  • alerting health services to patient status and risk
    • abnormal heartbeats are identified via a portable ECG device which then alerts clinicians167
    • inpatient remote patient monitoring can free up clinician time and reduce workload by removing the need for frequent in person observation checks.168
  • Nepean Blue Mountains LHD are trialling the use of a telehealth lab to remote monitor patient vital signs. It currently calculates heartrate from facial analysis.169
AI-delivered self-guided interventions

Limited but emerging positive evidence. AI can deliver standardised interventions directly to patients without the need for direct clinician involvement.

  • for patient-guided,   online mental health interventions where AI-delivered care can enhance   treatment effectiveness and adherence.170

Notes

^ denotes grey literature, conference proceeding or pre-peer review source.

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Living evidence tables include some links to low quality sources and an assessment of the original source has not been undertaken. Sources are monitored regularly but due to rapidly emerging information, tables may not always reflect the most current evidence. The tables are not peer reviewed, and inclusion does not imply official recommendation nor endorsement of NSW Health.

Last updated on 5 Jul 2024

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