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
Curative care
Supportive care

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 Oncology deep learning, and in particular convolutional neural network models, to diagnose breast, lung, liver, brain and cervical cancer³ predicting epidermal growth factor receptor mutation status in non-small cell lung cancer⁴ deep learning models of multi-omics data for lung cancer prediction⁵ AI driven data analysis to identify ovarian cancer⁶ predicting lymph node metastasis in early-stage colorectal cancers, potentially refining clinical decisions and improving outcomes⁷ AI assisted polyp detection used via colonoscopy for improving accuracy of colorectal cancer diagnosis ⁸ various forms of AI to diagnose and classify leukaemia quickly^{9, 10} AI, particularly machine learning for pancreatic cancer risk prediction¹¹ AI-driven liver cancer diagnosis, including biomarker and image analysis¹² and prediction of recurrence¹³ ultrasound-based radiomics as a novel approach to predicting breast cancer markers¹⁴ CT, MRI and PET-CT-based radiomics as a novel approach to detecting genetic mutation status in patients with lung cancer¹⁵ AI use in radiotherapy to improve prediction accuracy, dose calculation, treatment design and dose delivery¹⁶ machine learning applied to cytopathological data for diagnosing solid tumours¹⁷ Cardiovascular diagnosis, classification, and treatment of cardiovascular disease¹⁸ AI models for high accuracy diagnosis of cardiovascular disorders including reduced ejection fraction, valvular heart disease and cardiomyopathes¹⁹ machine learning algorithms to identify patients at high risk of left ventricular hypertrophy. This could reduce unnecessary next step MRI tests²⁰ use of AI in cardiac intensive care to analyse patient data to make prompt and accurate clinical decisions²¹ identification of hypertension and assessment of its secondary effects²² cardiac diagnostic accuracy, particularly in cardiac imaging and electrocardiogram analysis²³ deep learning techniques to automate transthoracic echocardiogram analysis and support clinical judgments²⁴ deep learning to advance diagnostic accuracy and personalised care in heart failure management²⁵ AI-based methods for detecting left ventricular hypertrophy²⁶ AI-enabled ECG for accurate risk prediction of short and long-term mortality from future cardiovascular events²⁷ Acute illness multiple types of AI, but most commonly artificial neural networks, to diagnose and prognosticate acute appendicitis²⁸ machine learning techniques for pulmonary embolism diagnosis and risk prediction²⁹ predicting community-acquired pneumonia-related hospitalisation risks, complications, and mortality³⁰ detecting new-onset atrial fibrillation in ICU-treated patients³¹ AI/machine learning to detect and identify patients at high risk of UTIs³² Neurology/Psychology AI analysis of CT images to improve efficiencies in the diagnosis of stroke ³³ machine learning analysis of a wide range of different data for the detection and diagnosis of cognitive impairment in Parkinson’s Disease³⁴ machine learning to improve the accuracy and efficiency of epilepsy diagnosis³⁵ and seizure characterisation³⁶ machine learning to assist in diagnosis of attention deficit hyperactivity disorder³⁷ AI to predict adverse events in patients deteriorating in psychiatric settings³⁸ Other discriminant analysis and artificial neural network predictive models which have demonstrated strong ability to differentiate between glaucoma patients and controls³⁹ increasing the efficiency of diagnosis in neuro-ophthalmology⁴⁰ AI-assisted total body positron emission tomography to facilitate rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort⁴¹ various AI models which can predict falls risk with a high level of accuracy⁴² deep learning models which are highly accurate in the detection of voice pathology, including laryngoscopy images acoustic input⁴³ deep learning algorithms to detect mucosal healing in ulcerative colitis⁴⁴ deep learning for obesity diagnosis and prediction⁴⁵ AI-assisted imaging demonstrates high diagnostic efficacy for developmental dysplasia of the hip⁴⁶ Predicting disease progression Acute care AI assessment tools to guide timely treatment and resuscitation services for people injured in disasters⁴⁷ machine learning algorithms that predict clinical deterioration in hospitalised adult patients⁴⁸ AI triage systems used to classify triage levels and predict mortality and ICU admission⁴⁹ deep learning-based frameworks have high accuracy for automated lung CT segmentation and predicting acute respiratory distress syndrome⁵⁰ machine learning for prediction of neonatal sepsis⁵¹ machine learning to differentiate between Kawasaki disease and other febrile illnesses⁵² machine learning for predicting mortality in adult critically ill patients with sepsis⁵³ Neurology machine learning algorithms to predict future epilepsy in people at risk⁵⁴ 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 recurrent⁵⁵ estimating the course and progression of Alzheimer’s disease at the early stages, which has treatment implications⁵⁶ machine learning algorithms have increasing accuracy at predicting delayed cerebral ischaemia⁵⁷ deep learning combined with imaging and molecular analysis enables more accurate prognostication of patients with gliomas⁵⁸ machine and deep learning improve spinal cord injury management by predicting quality of life, functional and neurological outcomes and estimating occurrence of adverse events^{59, 60} AI, in particular convolutional neural networks, are highly accurate at diagnosing dementia and Alzheimer’s, however, it has been noted that almost all studies in the field rely on one particular dataset^{61, 62} Oncology machine learning models have demonstrated considerable potential in predicting post-treatment survival and disease progression in head and neck cancer⁶³ early prediction of radiation pneumonitis in lung cancer patients using machine learning⁶⁴ AI has high sensitivity and specificity in automating the assessment of HER2 immunohistochemistry for breast cancer diagnosis⁶⁵ AI is promising for automating the assessment of HER2 immunohistochemistry for breast cancer diagnosis⁶⁵ Chronic illness progression and survival machine learning algorithms to predict individualised risk and time to diabetic retinopathy progression over 5 years, potentially allowing personalised screening intervals⁶⁶ 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 disease⁶⁷ machine learning algorithms have supported the update of a risk stratification tool for acute coronary syndrome, which predicts mortality and adverse outcomes⁶⁸ machine learning algorithms to predict kidney disease progression⁶⁹ Infectious diseases machine learning is a viable approach for developing non-time-based predictions for HIV deaths⁷⁰ Prognostic factors machine learning to identify time to surgery as the most important variable associated with colon cancer survival⁷¹ deep learning to stratify patients with colorectal cancer into risk groups and survival outcomes⁷² deep learning analysis of ECG data to identify patients at high risk of diabetes type 2⁷³	The CHA(2)DS(2)-VASc score is used to predict the risk of stroke in patients with atrial fibrillation.⁷⁴ 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’,⁷⁵ 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.⁷⁶
Early detection of illness via real-time data analysis		eHealth have a current project detecting sepsis in Emergency waiting rooms from patients’ vital signs.⁷⁷ Multi-lingual AI in Western Sydney LHD has been used to diagnose long COVID via self-report questionnaires conducted over the phone.⁷⁸
Image analysis Software typically uses deep learning AI, particularly deep learning, to assist with radiology and pathology processes.⁷⁹ 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.⁸⁰	Consistent positive evidence foridentifying and interpreting mammographic regions suspicious for cancer,^81-88 including when predicting survival outcomes⁸⁷ particularly positive results when AI assist with double screening⁸⁴ improved cancer detection rates with improved workflows and reduced recall rates⁸⁹ results have included generalisability across datasets from multiple countries.⁸⁴ Emerging positive evidence onimproved diagnosis and/or prognosis for: Abdominal / Gastrointestinal a number of common liver diseases including focal liver lesions and fatty liver^90-92 AI based pathology tool for scoring metabolic dysfunction associated with steatohepatitis⁹³ detection of adenomas and polyps in colonoscopy to facilitate earlier diagnosis of colorectal neoplasia and cancer^94-100 AI-assisted colonoscopy to automatically detect and characterise endoscopic lesions or abnormalities and facilitate IBD management^101-103 AI-assisted identification of patients with rectal cancer who could achieve pathological complete response following neoadjuvant chemoradiotherapy^{104, 105} AI-assisted detection, classification, and segmentation of pancreatic lesions¹⁰⁶ detection of early-stage oesophageal squamous cell carcinoma via computer-aided detection systems¹⁰⁷ detection of dysmotility within the oesophagus using oesophageal physiologic testing¹⁰⁸ AI analysis of hepatocellular carcinoma digital slides, including for diagnosis¹⁰⁹ and to serve as a biomarker for progression-free survival in patients treated with atezolizumab–bevacizumab¹¹⁰ detection and differentiation of pancreatic cancer lesion subtypes¹¹¹ detection of pancreatic space-occupying lesion via AI-assisted endoscopic ultrasound¹¹² kidney transplant biopsy analysis and digital pathology to identify appropriate donor organs and early signs of organ rejection¹¹³ identification of patterns in complex histopathology data from renal biopsy¹¹⁴ urolithiasis diagnosis, monitoring and treatment using AI modalities¹¹⁵ abdominal organ lesions due to improved image quality available via deep learning image reconstruction¹¹⁶ the VisioCyt® test using deep learning automated image processing for urothelial bladder cancers has improved sensitivity, but lower specificity compared to standard cytology¹¹⁷ identification of lymph node metastasis in patients with pancreatic ductal adenocarcinoma via CT-based radiomics algorithms and deep learning models¹¹⁸ automated detection of urinary stones from non-contrast computed tomography¹¹⁹ Pulmonary/Chest screening of COVID-19 pneumonia on chest CT, compared to radiologists¹²⁰ ultrasound imaging for predicting lymph node metastasis in breast cancer patients¹²¹ breast cancer diagnosis using deep learning and multimodal ultrasound¹²² lung lesions on chest x-ray, compared to non-radiologist physicians¹²³ lung cancer screening, especially in enhancing nodule detection sensitivity, reducing false-positive rates, and classifying nodules¹²⁴ pulmonary embolism detection on computed tomography pulmonary angiogram¹²⁵ diagnosis of pneumothorax via deep learning (requires training with local data)¹²⁶ identifying phenotypes and risk categories in patients with normal results on myocardial perfusion imaging¹²⁷ AI-based screening of cardiac amyloidosis-suggestive uptake in patients undergoing scintigraphy¹²⁸ AI-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases¹²⁹ diagnosis of different degrees of coronary artery stenosis via deep learning based on coronary angiography or coronary CT angiography images¹³⁰ diagnosis and management of chronic obstructive pulmonary disease via machine and deep learning¹³¹ diagnosis of cardiac amyloidosis via AI-enhanced ECG models¹³² myocarditis diagnosis, prognosis and better understanding of molecular dynamics using AI applications¹³³ aortic stenosis diagnosis using deep learning approaches¹³⁴ Head and neck machine learning for the analysis and evaluation of various hair and skin assessments¹³⁵ artificial intelligence for analysis of ultrasound images to diagnose malignant thyroid nodules, particularly when compared to radiologists and surgeons with less experience^{136, 137} segmenting, detecting, and classifying head and neck cancers via deep learning¹³⁸ meningioma classification, grading, outcome prediction, and segmentation via deep or machine learning¹³⁹ differentiating thyroid nodules and accurately forecasting lymph node metastasis via deep learning^{140, 141} intracranial haemorrhage assessed via non-contrast CT scans¹⁴² intracranial aneurysms, including accurate detection and improved clinician sensitivity and reading times^143-145 temporomandibular joint diagnosed via AI-analysed cone-beam computed tomography, which eliminates the subjectivity associated with clinician-led diagnosis¹⁴⁶ caries lesions as diagnosed via various neural networks¹⁴⁷ classification of glioblastomas from primary CNS lymphomas via MRI-based machine or deep learning techniques¹⁴⁸ deep learning models for the detection and classification of seizure semiology via video analysis¹⁴⁹ radiographic estimation of the jaw bone for age and sex can be for use in medico legal scenarios and disaster victim identification¹⁵⁰ differential diagnosis of neuromyelitis optica spectrum disorder and multiple sclerosis via AI analysis of MRI images¹⁵¹ classification of brain tumours via machine learning^{152, 153} diagnosis of Moyamoya disease via deep learning algorithms¹⁵⁴ classification and segmentation of oral squamous cell carcinoma via deep learning¹⁵⁵ Ocular¹⁵⁶ sight-threatening eye diseases, via retinal image analysis, as well as prediction of complex systemic disorders such as heart failure and myocardial infarction¹⁵⁷ classification of distinct multiple sclerosis subtypes based on retinal features, aiding in disease characterisation and guiding tailored therapeutic strategies¹⁵⁸ keratoconus diagnosis supported by AI triaging tools¹⁵⁹ Al diagnostic systems that screen for diabetic retinopathy have been shown to have good accuracy,¹⁶⁰ save clinicians in real-world settings significant amounts of time¹⁶¹ as well as being cost-saving in both Indigenous and non-Indigenous populations in Australia¹⁶² AI detection of diabetic macular oedema¹⁶³ detection of pathological myopia from colour fundus images¹⁶⁴ screening and early diagnosis of the main causes of blindness via smartphone technology (such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration)¹⁶⁵ deep learning algorithms for detecting angle closure in patients with glaucoma¹⁶⁶ AI screening tool for detecting retinopathy of prematurity¹⁶⁷ Radiology and medical imaging MRI data analysis allowing for more accurate tumour characterisation and small tumour segmentation¹⁶⁸ identifying benign and soft tissue tumours via radiomics and deep learning¹⁶⁹ detecting and segmenting brain metastases via MRI image analysis by deep learning¹⁷⁰ MRI diagnosis of ACL or knee meniscus tears via deep learning^{171, 172} AI for bone fracture detection – where AI can be of great value in cases where radiologists are not available but isn’t as accurate as radiologists at present¹⁷³ AI-detected scaphoid and distal radius fractures¹⁷⁴ 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 documentation¹⁷⁵ fracture diagnosis using AI and human assessment showed higher diagnostic accuracy in wrist and ankle fractures when compared with spine and rib fractures¹⁷⁶ Other AI in theranostics to analyse personalised patient risk classification, provide prognostic forecasts, or personalised dosimetry¹⁷⁷ AI assistance in detecting and grading prostate cancer, predicting patient outcomes, and identifying molecular subtypes ^178-181 AI supported tumour origin differentiation using cytological histology¹⁸² deep learning supported diagnosis of osteoporosis^{183, 184} artificial intelligence algorithms in osteoarthritis detection¹⁸⁵ detection and classification of skin cancer via deep and machine learning, either independently or for double screening^186-189 diagnosis of lymphoma by AI (deep learning and machine learning)¹⁹⁰ rheumatology MRI analysis to address diagnostic support, disease classification, activity assessment and progression monitoring¹⁹¹ AI-based video monitoring systems offer improved efficiency and objectivity in the screening, diagnosis and treatment of movement disorders^{192, 193} gait analysis for diagnosis of Parkinson’s Disease via deep learning¹⁹⁴ 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 models¹⁹⁵ diagnosis and management of pigmented skin lesions via mobile phone-powered AI technology.¹⁹⁶	NICE has recommended the use of AI contouring technologies to speed up treatment planning for those undergoing external beam radiotherapy for cancers.¹⁹⁷ Automated whole slide imaging (WSI) scanners are now approved for use in primary diagnosis by the FDA.⁸⁰ 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.¹⁹⁸ 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.¹⁹⁹

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.²⁰⁰ Results were collated into a visual decision-making tree for easy clinical use appropriate treatment and disease management strategies in kidney disease⁶⁹ pre-operative risk stratification via ECGdem for high-risk surgical candidates, to inform individualised treatment strategies²⁰¹ tracking and predicting responses to medical and surgical treatments in epilepsy⁵⁴ which patients with prostate cancer may be eligible for clinical trials or have faster progressing cancers²⁰² using radiomics to assess the tumour microenvironment, in order to monitoring breast cancer treatments²⁰³ identifying new categories for treatment outcomes across large cohorts, to better understand responses to methotrexate in juvenile idiopathic arthritis²⁰⁴ predicting drug resistance and patient prognosis in pulmonary tuberculosis²⁰⁵ enhancing personalised treatments and advancing precision oncology via deep learning in cancer genomics and histopathology²⁰⁶ using causal machine learning to predict drug treatment outcomes including efficacy and toxicity²⁰⁷ stereotactic radiosurgery outcomes in patients with brain metastasis²⁰⁸ using machine learning in melanoma immunotherapy response and prognosis²⁰⁹ AI processing of omics data through protein and gene pattern classification to tailor breast cancer treatment²¹⁰ using machine learning to predict post-operative outcomes for patients undergoing gastrointestinal surgery²¹¹ predicting severity in order to inform treatment options: predicting the severity of postoperative scars to aid clinicians in scar management treatment decisions.²¹²	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.²¹³ 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.²¹³ A US hospital uses a model to predict which patients are at highest risk of metastatic disease, to inform their cancer treatment approach.²¹⁴
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 burns²¹⁵ surgical planning, such as deep learning for lung tumour segmentation accuracy²¹⁶ machine learning to improve neuroanatomic localisation and lateralisation in epilepsy⁵⁴ automatic assessment of aortic root morphology for transcatheter aortic valve implantation planning.²¹⁷	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.²¹⁸
AI driven surgical monitoring and decision making	Emerging positive evidence for: assisting in surgical decision-making in real time²¹⁹ random forest driven surgical decision-making for total knee arthroplasty²²⁰ neural network driven rapid nanopore sequencing to enable molecular subclassification of central nervous system tumours²²¹ monitoring patient vitals during surgery the hypotension prediction index can predict intra-operative hypotensive episodes in real time with high accuracy and hence reduce the duration of hypotension²²²
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 treatment²²³ supporting clinical decisions by accurately predicting antimicrobial resistance in critical pathogens²²⁴

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.²²⁵ promoting healthy lifestyles, smoking cessation, treatment/medication adherence and reducing substance abuse²²⁶ self-management of mental health conditions²²⁷ self-management of insulin dosing in type 2 diabetes²²⁸ 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 teams²²⁹ users find chatbots acceptable and easy to use, but there remains a lack of reliable and comparable evidence on their efficacy^{227, 229-231} supporting physical activity goals^{226, 232} Limited but negative evidence when usedfor: weight loss and healthy diet support²³²	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.²³³ 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.²³⁴ 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.²¹⁴
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 cardiovascular disease, prediction models have identified the important roles of sex and social determinants of health in outcome predictions²³⁵ in heart failure, natural language processing of clinical documentation can improve analysis of disease progression and response to treatment²³⁶ 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.²³⁷ 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 rates²³⁸ integrating electronic medical record datasets and machine learning algorithms to predict 30 day heart failure readmission²³⁹ predicting individualised medication responses in hypertension care, machine learning can support individualised recommendations for drug choices and dosages to achieve certain blood pressure targets.²⁴⁰
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:²⁴¹ AI support for chronic disease management AI-assisted insulin management for type 1 and 2 diabetes²⁴² AI-supported non-invasive blood glucose monitoring²⁴³ identification of patient health status identification of patients affected by cardiovascular disease, via smartwatches²⁴⁴ identification of acceptable ECG data via ‘smartvests’ that monitor vital signs²⁴⁴ alerting health services to patient status and risk abnormal heartbeats are identified via a portable ECG device which then alerts clinicians²⁴⁴ inpatient remote patient monitoring can free up clinician time and reduce workload by removing the need for frequent in person observation checks.²⁴⁵	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.²⁴⁶
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.²⁴⁷	·

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 3 Dec 2024