Collaborating Authors


AI's potential in skin cancer management comes with a warning


Artificial intelligence (AI) use in dermatology is primed to become a powerful tool in skin cancer assessment, but it remains to be seen how diagnostic devices in dermatology will influence decision making in the clinic and affect patient outcomes, according to the authors of a Perspective published online today by the Medical Journal of Australia. In dermatology the primary focus for the use of AI has been on developing machine learning systems that facilitate classification and decision support for skin cancer management. "Recent studies show that machine learning algorithms have the potential to surpass the diagnostic performance of experts, and the challenge now is how to implement this new technology safely into clinical practice," wrote the authors, led by Associate Professor Victoria Mar, a consultant dermatologist and Director of the Victorian Melanoma Service at Alfred Hospital. "There are two potentially negative implications for clinical practice: first, clinicians may have difficulty upskilling by following the algorithms' outputs; and second, there exists the potential for deskilling and underperforming due to an over-reliance on technology. Algorithm performance is dependent on both the size and quality of the training image dataset and on whether the algorithm is used in situations for which it was intended," wrote Mar and colleagues.

Artificial intelligence in health care is already here, but where to next?


Artificial intelligence (AI) in health care has arrived, with enormous potential for change in the delivery of care, but experts published in the Medical Journal of Australia today are asking if we are ready. "AI, machine learning, and deep neural network tools can assist medical decision making and management, and have already permeated into at least three different levels: AI-assisted image interpretation; AI-assisted diagnosis; and, AI-assisted prediction and prognostication," wrote the authors, Joseph Sung, the Mok Hing Yiu Professor of Medicine at the Chinese University of Hong Kong, Cameron Stewart, Professor of Health, Law and Ethics at the University of Sydney, and Professor Ben Freedman, the Deputy Director of Research Strategy at the Heart Research Institute and the University of Sydney's Charles Perkins Center and Concord Clinical School. "From diagnosing retinopathy to cardiac arrhythmias, from screening for skin cancer to breast cancer, from predicting outcome of stroke to self-management of chronic diseases, AI and machine learning devices can replace many time-consuming, labor-intensive, repetitive and mundane tasks of clinicians and give possible suggestions of management plans," Sung and colleagues wrote. The quality of AI in health care is dependent on the quality of the data on which it is based. "Algorithms are being developed and validated on data generated by health care systems where current practices may already be inequitable," they wrote.

Human-Artificial intelligence collaborations best for skin cancer diagnosis


Artificial intelligence (AI) improved skin cancer diagnostic accuracy when used in collaboration with human clinical checks, an international study including University of Queensland researchers has found. The global team tested for the first time whether a'real world', collaborative approach involving clinicians assisted by AI improved the accuracy of skin cancer clinical decision making. UQ's Professor Monika Janda said the highest diagnostic accuracy was achieved when crowd wisdom and AI predictions were combined, suggesting human-AI and crowd-AI collaborations were preferable to individual experts or AI alone "This is important because AI decision support has slowly started to infiltrate healthcare settings, and yet few studies have tested its performance in real world settings or how clinicians interact with it," Professor Janda said. "Inexperienced evaluators gained the highest benefit from AI decision support and expert evaluators confident in skin cancer diagnosis achieved modest or no benefit. "These findings indicated a combined AI-human approach to skin cancer diagnosis may be the most relevant for clinicians in the future." Although AI diagnostic software has demonstrated expert level accuracy in several image-based medical studies, researchers have remained unclear on whether its use improved clinical practice. "Our study found that good quality AI support was useful to clinicians but needed to be simple, concrete, and in accordance with a given task," Professor Janda said. "For clinicians of the future this means that AI-based screening and diagnosis might soon be available to support them on a daily basis.

La Trobe University Uses AI to Bring Mental Health Care to Cancer Patients


The Centre for Data Analytics and Cognition (CDAC) at Australia's La Trobe University worked with international cancer researchers to develop an artificial intelligence patient-reported information multidimensional framework to help detect and analyze a patient's mental health status while undergoing cancer treatment. The Centre for Data Analytics and Cognition (CDAC) at Australia's La Trobe University has teamed up with international cancer researchers to develop an artificial intelligence patient-reported information multidimensional framework (PRIME) to detect and analyze a patient's mental health status while undergoing cancer treatment. According to CDAC director and La Trobe University head of analytics discipline, Damminda Alahakoon, using PRIME can help understand a patient's behaviour, emotions, and decision-making based on data shared by the patient. He said the data can be text provided by a patient to an online chatbot, an online cancer support group, or other online support services. "PRIME addresses the challenges associated with understanding the unlabelled and unstructured nature of this data, allowing it to efficiently identify trends and anomalies -- such as when a patient is struggling emotionally -- and effectively adapt to the changing nature of that data," he said.

Worth the cost? A closer look at the da Vinci robot's impact on prostate cancer surgery


Urology fellow, Jeremy Fallot, and nurse, Shauna Harnedy, assist in robotic surgery by Ruban Thanigasalam (out of view) in Sydney, Australia.Credit: Ken Leanfore for Nature Loved by surgeons and patients alike for its ease of use and faster recovery times, the da Vinci surgical robot is less invasive than conventional procedures, and lacks the awkwardness of laparoscopic (keyhole) surgery. But the robot's US$2-million price tag and negligible effect on cancer outcomes is sparking concern that it's crowding out more affordable treatments. There are more than 5,500 da Vinci robots globally, manufactured by California-based tech giant, Intuitive. The system is used in a range of surgical procedures, but its biggest impact has been in urology, where it has a market monopoly on robot-assisted radical prostatectomies (RARP), the removal of the prostate and surrounding tissues to treat localized cancer. Uptake in the United States, Europe, Australia, China and Japan for performing this procedure has been rapid.

Can online support groups address psychological morbidity of cancer patients? An artificial intelligence based investigation of prostate cancer trajectories.


Online Cancer Support Groups (OCSG) are becoming an increasingly vital source of information, experiences and empowerment for patients with cancer. Despite significant contributions to physical, psychological and emotional wellbeing of patients, OCSG are yet to be formally recognised and used in multidisciplinary cancer support programs. This study highlights the opportunity of using Artificial Intelligence (AI) in OCSG to address psychological morbidity, with supporting empirical evidence from prostate cancer (PCa) patients. A validated framework of AI techniques and Natural Language Processing (NLP) methods, was used to investigate PCa patient activities based on conversations in ten international OCSG (18,496 patients- 277,805 conversations). The specific focus was on activities that indicate psychological morbidity; the reasons for joining OCSG, deep emotions and the variation from joining through to milestones in the cancer trajectory.

Australia's Fires, Artificial Intelligence, Fentanyl: RAND Weekly Recap


Massive bushfires have destroyed millions of acres in Australia over the past few months. RAND's Melissa Finucane, a community resilience expert who grew up in a remote region of New South Wales, has watched in anguish. Experiences from previous disasters have highlighted concrete steps that can help communities start to recover right away, she says. She also notes that rural Australians have "a special kind of resilience," with perspectives and wisdom from years of hard experience. But still, measuring the effects that fires and other disasters have on people's mental health, social, and economic needs remains a unique challenge.

Adaptive Initialization Method for K-means Algorithm Machine Learning

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initialization methods have been proposed, but none of them can dynamically adapt to datasets with various characteristics. In our previous research, an initialization method for K-means based on hybrid distance was proposed, and this algorithm can adapt to datasets with different characteristics. However, it has the following drawbacks: (a) When calculating density, the threshold cannot be uniquely determined, resulting in unstable results. (b) Heavily depending on adjusting the parameter, the parameter must be adjusted five times to obtain better clustering results. (c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets. In the current paper, we proposed an adaptive initialization method for the K-means algorithm (AIMK) to improve our previous work. AIMK can not only adapt to datasets with various characteristics but also obtain better clustering results within two interactions. In addition, we then leverage random sampling in AIMK, which is named as AIMK-RS, to reduce the time complexity. AIMK-RS is easily applied to large and high-dimensional datasets. We compared AIMK and AIMK-RS with 10 different algorithms on 16 normal and six extra-large datasets. The experimental results show that AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Furthermore, AIMK-RS can significantly reduce the complexity of applying it to extra-large datasets with high dimensions. The time complexity of AIMK-RS is O(n).

Large expert-curated database for benchmarking document similarity detection in biomedical literature search


Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations.

The computer will see you now: six examples of AI in healthcare


As an industry defined by the relationship between patient and carer, at first glance it may seem incongruous to nudge healthcare towards a robotic future. In fact, artificial intelligence (AI) has the potential to completely reshape the health industry, offering greater support to human capabilities and allowing healthcare organizations to deliver higher-quality services more efficiently. AI is a broad term for computer systems that can "think" and act like humans. They can sense their environment, absorb information, learn from past experience, make decisions and take action. AI has transformative power for two reasons: the explosive growth in data, coupled with huge computational advances and processing speeds.