One of the few positive outcomes of the ongoing COVID-19 pandemic is that it has enhanced the pace of digitalisation of healthcare. Among various facets of digital health, Digital Therapeutics (DTx) is unique as it offers evidence-based interventions for preventing, managing, or treating specific disorders. It is different from wellness apps because it requires evidence from a clinical trial or real-world settings and is bound by regulatory approval and clearance. The DTx market is expected to grow exponentially in the post-COVID era, and there are multiple drivers for the same. After the onset of the pandemic, the viewpoint of various stakeholders of the DTx market, including the patients, prescribers, payers, and pharma industry, have changed significantly. Regulatory bodies have also started to realise the importance of DTx. This review provides an overview of the present status and future potential of DTx considering the COVID-19 pandemic.
Background & objectives:
Artificial intelligence (AI) and machine learning (ML) have shown promising results in cancer diagnosis in validation tests involving retrospective patient databases. This study was aimed to explore the extent of actual use of AI/ML protocols for diagnosing cancer in prospective settings.
Methods:
PubMed was searched for studies reporting usage of AI/ML protocols for cancer diagnosis in prospective (clinical trial/real world) setting with the AI/ML diagnosis aiding clinical decision-making, from inception till May 17, 2021. Data pertaining to the cancer, patients and the AI/ML protocol were extracted. Comparison of AI/ML protocol diagnosis with human diagnosis was recorded. Through a
post hoc
analysis, data from studies describing validation of various AI/ML protocols were extracted.
Results:
Only 18/960 initial hits (1.88%) utilized AI/ML protocols for diagnostic decision-making. Most protocols used artificial neural network and deep learning. AI/ML protocols were utilized for cancer screening, pre-operative diagnosis and staging and intra-operative diagnosis of surgical specimens. The reference standard for 17/18 studies was histology. AI/ML protocols were used to diagnose cancers of the colorectum, skin, uterine cervix, oral cavity, ovaries, prostate, lungs and brain. AI/ML protocols were found to improve human diagnosis, and had either similar or better performance than the human diagnosis, especially made by the less experienced clinician. Validation of AI/ML protocols was described by 223 studies of which only four studies were from India. Also there was a huge variation in the number of items used for validation.
Interpretation & conclusions:
The findings of this review suggest that a meaningful translation from the validation of AI/ML protocols to their actual usage in cancer diagnosis is lacking. Development of regulatory framework specific for AI/ML usage in healthcare is essential.
:
The ever-increasing use of digital technologies is rapidly changing the face of modern healthcare delivery. Healthcare systems are embracing digital health solutions to improve patient outcomes, enhance healthcare delivery, and reduce costs. Digital therapeutics (DTx) are now a popular category of digital health solutions aimed at preventing, managing, or treating medical disorders. These evidence-based technologies/products either complement a conventional therapy or are prescribed as stand-alone treatments for a range of conditions, including chronic diseases and mental health disorders. Many pharmaceutical companies and healthcare start-ups are developing DTx products for different health conditions. Despite similarities between DTx and conventional medicines, DTx products are not covered under reimbursement at present in many countries. There are no uniform regulations for DTx prescription and reimbursement. This review aims to analyse the current DTx scenario, particularly highlighting the regulatory aspect and reimbursement of DTx products globally.
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