How Can Artificial Intelligence Make Medicine More Preemptive?

Iqbal, Usman; Celi, Leo Anthony; Li, Yu-Chuan

doi:10.2196/17211

Cited by 13 publications

(8 citation statements)

References 18 publications

(18 reference statements)

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“…Through achieving this "Earlier Medicine" vision, we may one day catch most of the cancer patients at stage zero and cure them, spot a heart failure at stage A or predict a stroke and avoid long-term disabilities, etc. [7]. The possibilities are endless.…”

Section: Discussionmentioning

confidence: 99%

Temporal Phenomics – A Powerful Approach Using AI to Achieve “Earlier Medicine”

2022

Studies in Health Technology and Informatics

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The resurgence of machine learning AI has triggered the importance of collecting “personal big data” over a long period of time from wearable devices and EHRs. Collecting data from this large number of variables over a significant period of time has further induced the study on “Temporal Phenomics”, which can be a powerful approach to achieve pre-emptive and “earlier medicine”. The paper presents a methodology to make studying “Temporal Phenomics” more feasible and convenient without limitations on the number of variables and the length of time periods.

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Section: Discussionmentioning

confidence: 99%

Temporal Phenomics – A Powerful Approach Using AI to Achieve “Earlier Medicine”

2022

Studies in Health Technology and Informatics

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“…We synthesized the relevant parts of 3 publications in particular—(1) the World Health Organization Technical Series on Safety in Primary Care: Diagnostic Errors , which addresses how to improve the safety of multiple aspects of diagnostic and administrative work in primary care [ 31 ]; (2) Murphy and colleagues’ [ 32 ] Safer Dx Trigger Tools Framework, which outlines good practice for the development of electronic tools to improve diagnostic safety; and (3) Vincent and Almaberti’s [ 33 ] compendium of safety strategies. Some additional papers and our own knowledge of safety-netting and e–safety-netting tools were used to construct an emergent framework for e–safety-netting tool development ( Table 2 ) [ 34 - 36 ]. This framework may be useful for audits, for e–safety-netting tool development and improvement, and for guiding future research.…”

Section: Establishing a Framework For What A Good E–safety-netting To...mentioning

confidence: 99%

“…The tool supports continuous improvements in data quality and decision-making during the consultation, and it offers memory aids and alerts for both professionals and patients. Support diagnostic processes before, during, and after consultations [34].…”

Section: Establishing a Framework For What A Good E-safety-netting To...mentioning

confidence: 99%

Harnessing the Electronic Health Care Record to Optimize Patient Safety in Primary Care: Framework for Evaluating e–Safety-Netting Tools

Black¹,

Bhuiya²,

Smith³

et al. 2022

JMIR Med Inform

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The management of diagnostic uncertainty is part of every primary care physician’s role. e–Safety-netting tools help health care professionals to manage diagnostic uncertainty. Using software in addition to verbal or paper based safety-netting methods could make diagnostic delays and errors less likely. There are an increasing number of software products that have been identified as e–safety-netting tools, particularly since the start of the COVID-19 pandemic. e–Safety-netting tools can have a variety of functions, such as sending clinician alerts, facilitating administrative tasking, providing decision support, and sending reminder text messages to patients. However, these tools have not been evaluated by using robust research designs for patient safety interventions. We present an emergent framework of criteria for effective e–safety-netting tools that can be used to support the development of software. The framework is based on validated frameworks for electronic health record development and patient safety. There are currently no tools available that meet all of the criteria in the framework. We hope that the framework will stimulate clinical and public conversations about e–safety-netting tools. In the future, a validated framework would drive audits and improvements. We outline key areas for future research both in primary care and within integrated care systems.

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“…Empathy refers to the ability to understand or feel what another person is experiencing, and showing empathy may lead to better behavioral outcomes [ 9 ]. Empathy can be learned, and the use of AI technology introduces a promising approach to incorporating artificial empathy in the doctor-patient therapeutic relationship [ 11 ]. However, human emotions are very complex.…”

Section: Introductionmentioning

confidence: 99%

How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

Yang¹,

Rahmanti²,

Huang³

et al. 2022

J Med Internet Res

Self Cite

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We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However, face recognition data sets are often difficult to acquire; many researchers struggle with small samples of face recognition data sets. Further, sharing medical images or videos has not been possible, as this approach may violate patient privacy. The use of deepfake technology is a promising approach to deidentifying video recordings of patients’ clinical encounters. Such technology can revolutionize the implementation of facial emotion recognition by replacing a patient's face in an image or video with an unrecognizable face—one with a facial expression that is similar to that of the original. This technology will further enhance the potential use of artificial empathy in helping doctors provide empathic care to achieve good doctor-patient therapeutic relationships, and this may result in better patient satisfaction and adherence to treatment.

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How Can Artificial Intelligence Make Medicine More Preemptive?

Cited by 13 publications

References 18 publications

Temporal Phenomics – A Powerful Approach Using AI to Achieve “Earlier Medicine”

Temporal Phenomics – A Powerful Approach Using AI to Achieve “Earlier Medicine”

Harnessing the Electronic Health Care Record to Optimize Patient Safety in Primary Care: Framework for Evaluating e–Safety-Netting Tools

How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

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