Five Healthcare AI/ML Trends to Watch for in 2023


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2022 has been an interesting year for healthcare AI/ML. It saw, among others, a consolidation of pandemic-induced practices, such as a shift towards virtual care (especially in primary care), into the ‘new normal’ of healthcare delivery. The COVID-19 pandemic has spurred providers and payors alike to rethink traditional models of care and offer more cost-efficient virtual visits that suit the lifestyles of younger patients, who are used to on-demand services on their schedule rather than appointments at fixed times, as well as older and more vulnerable patients who have often experienced barriers in receiving care due to a lack of transportation and access. Payors are now looking towards automating some routine tasks using conversational Natural Language Processing (NLP).

We expect these trends to continue in 2023. As medical AI/ML applications become both increasingly widespread and routine, their adoption is likely to become more prevalent, too. This was given a much-awaited regulatory push by the FDA issuing final guidance on its regulatory practice with respect to clinical decision support (CDS) systems, exempting, among others, knowledge management systems that support clinical practice by providing recommendations and guidelines relevant to a particular disease or condition from the FDA’s device regulatory purview. This is likely to open up a major interest in helping physicians manage the wealth of clinical information and research that is generated at an increasingly rapid pace. In addition, there is a growing awareness for the role of AI in patient safety and privacy, especially for devices that interface directly with a patient (e.g. robotic surgery instruments).

Processing Exascale Unstructured Data

According to a 2019 article in Nature Medicine, healthcare generates around 150 exabytes of data a year, growing around 48% annually, most of it unstructured. The management of data assets of this scale remains challenging, and exascale computing remains generally unavailable to the public. The challenges of handling even a fraction of this data is highlighting the importance of an emerging new profession: the healthcare data engineer, a clinical informatics specialist experienced with creating stable, reliable data lakes and data pipelines of clinical data, at scale and in a cost-efficient manner.

The fundamental challenge is that speed, safety and cost-efficiency are rarely attainable at the same time. Healthcare data engineers will continue to face the challenge of managing the data assets of the industry that is estimated to produce a little under a third of all data stored in digital form. While the first decades of clinical informatics were largely focused on creating interoperability through APIs like FHIR/HL7, the emerging challenges have to do more with managing data at scale to both facilitate operational tasks (patient care, medical record-keeping, imaging and PACS functionalities &c.) and have the ability to draw insights from it. As health systems, payors and other institutions realize the value of their clinical data assets, the demand for clinical data engineers will skyrocket.

AI-assisted Radiology

The COVID-19 pandemic highlighted that in a surge of patients, radiologists can often become overwhelmed with work. During the pandemic, a number of quite viable models emerged to differentiate normal, non-COVID and COVID viral pneumonia chest X-ray images. While most of these models are not mature enough for clinical use, the rapid and relatively inexpensive development of such models has highlighted the viability of deep learning and computer vision in assisting radiologists.

A key difficulty of AI-assisted radiology is that, beyond the more trivial functionalities (e.g. segmentation models to quantify lesion size, count and volume), issues of regulation and liability remain unsettled. As the use of such solutions expands, we may expect the regulatory bodies to catch up and determine these issues. Radiologists can benefit from the assistance of AI-driven image analysis solutions to increase their throughput, decrease time spent on relatively menial parts of the image analysis process and automatically prioritize cases if the demand for their services exceeds capacity.

AI in Patient Safety

There is an increased drive towards leveraging AI to ensure patient safety and privacy through applying advanced analytics. This is of particular importance in the context of medical devices that are directly interfacing with patients, from robotic surgery through insulin pumps to brachytherapy machines. For much of the past, such devices relied on hard interlocks, trained personnel and/or air-gapped installation to prevent intentional or unintentional misuse that might harm the patients. AI can increase patient safety by monitoring inputs and identifying anomalies, whether it is spotting an accidentally added zero on the dose field of a radiotherapy prescription, an employee attempting to obtain narcotics from an automated dispensing machine with intent to divert it or a malicious hacker gaining access to critical equipment.

AI can also prevent unintentional medication errors by correlating its knowledge of the patient with the prescription, such as a physician accidentally prescribing a normal dose to a patient with renal impairment, resulting in an excessive dose, or an oversight causing a recent surgical patient to be discharged without the required anticoagulant prescription. Through forming an overall view of what “normal” care would entail for a patient with the given characteristics and clinical history, anomaly detection can highlight potential deviations and create a “speed bump” to focus a provider’s attention to a potential risk to patient safety.

Decentralized Clinical Trials

The face of clinical trials is changing rapidly. Decentralized Clinical Trials (DCTs), often enabled by smart devices and wearables, have become popular in recent years. This is partly thanks to the wide popularity of and patient familiarity with smart devices and partly to the cost-efficiencies it provides. AI-driven adaptive monitoring decreases the monitoring burden on the patient while maintaining data quality through identifying patients who are more likely to require monitoring and increasing monitoring frequency.

A key challenge for DCTs remains data availability and the difficulties of engineering safe and reliable systems to store trial data. Users are looking for end-to-end, turnkey ecosystems that are nonetheless adaptable and seamlessly link from onboarding, consent, data management and storage to analytics, intelligence and evaluation. A “real-time analytics” capability of DCTs can be helpful for Data Safety Monitoring Committees (DSMCs) in identifying risks or potential reasons to consider discontinuation. While clinical trials should not be discontinued for economic reasons, understanding trials as ongoing processes of accumulating data rather than a series of “snapshots” allows planning, adjustments and strategic decision-making.

The Knowledge Management Challenge

In the first year and half of COVID-19, scientists published over 200,000 papers relating to the disease. While this certainly shows a vivid interest and the ability of the global scientific community to respond rapidly to an outbreak, it also poses unique challenges to clinicians who need to consolidate information and integrate it into their clinical practice. The knowledge management challenge is likely to become increasingly acute with the ability to rapidly publish via Open Access and preprint servers.

Language models can help physicians overcome this flood of data by distilling it down to a manageable form. Until relatively recently, the very specific and sometimes counterintuitive language of scientific writing, especially in the biomedical and life sciences, has hampered the use of language models trained on common conversational language on academic publications. However, the rise of Transformer models and their increasing efficiency and rapidly dropping costs are helping AI-based solutions analyze, collate and summarize academic output, guidelines and diagnostic criteria or treatment protocols into an evolving, up-to-date picture of a clinical entity. Such clinical knowledge management solutions will greatly benefit from the FDA’s September 2022 guideline that indicates it would not consider such systems to be subject to “device” regulation, and we can expect some of these solutions to rapidly rise to the forefront in the coming years.

About the author

Chris von Csefalvay is the Practice Director of AI/ML for the healthcare portfolio at HCL Tech, where he advises Fortune 500 pharmaceutical, medical technology and life sciences enterprises on AI/ML strategy, policy and technologies. He is a Fellow of the Royal Society for Public Health and the author of Computational Modeling of Infectious Disease.



Chris von Csefalvay CPH FRSPH MTOPRA
HCLTech-Starschema Blog

Practice director for biomedical AI at HCLTech, computational epidemiologist board certified in public health, Golden Retriever dad, &wheelchair rugby player.