Clinician Engineers – The future of healthcare

On Sep 22nd, 2021, Dr. Neel Sharma, Queen Elizabeth Hospital Birmingham, UK, gave a talk entitled "Clinician Engineers - The future of healthcare" as part of the SN Applied Sciences ( webinar series.
Clinician Engineers – The future of healthcare

In this blog, Dr. Sharma highlights the importance of building such a cohort of professionals in the future healthcare landscape.

Engineering solutions are the basis of diagnosis and management. Take the case of a young newly diagnosed colitis patient. While we ensure to take a thorough history and physical exam, our minds are also processing how quickly we can get a CT (computerised tomography) scan and endoscopy. As clinicians what would you feel comfortable with as diagnostic measures, a physical exam alone? Unlikely.

Engineering is guiding the way. And this is not just true for digestive diseases, across all specialties; engineering platforms are now the gold standard. Our acute kidney injury patients being filtered, our hypoxic pneumonia patients being ventilated, our cardiac patients being stented; the list is limitless. Of course, there is always a divide and debate mentality in medicine. Many seniors may shudder at the thought of rapid technology diffusion into medical practice. Yet they probably would admit to the fact they would be glad of its availability if they became unwell. And as healthcare providers, we know for sure, the public are always keen for engineering-based solutions. Yet something is amiss.

Robust measures need to be in place to ensure that clinicians and technologies understand each other – the dawn of the clinician engineer. Recent key technologies that are expected to be transformative include for instance optics, wearable sensors and artificial intelligence to name but a few. Full integration of such technologies requires clinicians with broad engineering expertise. The ability to understand the fundamentals of how medical technologies work could enable specialists to evaluate the efficacy of medical devices and provide essential feedback. Specialists with technical knowledge can be gatekeepers for medical devices that may seem to be technologically innovative but provide no significant outcomes in clinical settings. Understanding differences in technology platforms can also allow for the identification of device failures. The ability to understand medical device technologies can also create an ecosystem for clinician engineers as entrepreneurs. With the emergence of the Clinician Engineer the critical thinking and appreciation of engineering principles in medical practice can move forward.

The recording of the SN Applied Sciences webinar is available at The SN Applied Sciences Topical Collection The Clinician Engineer, guest edited by Dr. Neel Sharma, is open for submissions through

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Subscribe to the Topic

Research Communities > Community > Sustainability

Related Collections

With collections, you can get published faster and increase your visibility.

Engineering: Artificial Intelligence in Smart Computing, Industry and Societies

The Topical Collection looks for submissions covering the following aspects: (1) Artificial Intelligence in Smart Computing; (2) Artificial Intelligence for Industry and Societies; (3) Multi-Agent Systems (Theory and Applications); (4) Social Simulation and Modelling. Further detail of the different subject areas is given below. (1) Artificial Intelligence in Smart Computing: With the advance of computer networks and hardware technology, it is now possible not only to integrate miniaturized computers into the things in our surroundings but also possible to connect those things over computer networks and the Internet. (2) Artificial Intelligence for Industry and Societies: Societies and industries face many challenges to be more intelligent and sustainable. In this context, many good practices and approaches are being explored, and numerous new contributions are being generated daily. Many good practices and procedures are being investigated and several unique contributions emerge without benefitting from sufficient visibility. In these, A.I. will have an even stronger role, as complex multi-disciplinary projects need its support. In this evolution, Industry and Societies must be holding hands, and this is a truly multi-disciplinary endeavour, whereby A.I. is a key hinge and facilitator between technologies, goods and services. (3) Multi-Agent Systems: Theory and Applications: The purpose of this thematic track is to provide a high-profile, internationally respected discussion forum on the most recent and innovative scientific research in the area of agents and multiagent systems (MAS). The MASTA thematic track will cover not only traditional topics related to agent theory and multi-agent engineering but also issues associated with using evolving autonomous systems in real-world scenarios where humans are involved, including conversational systems. (4) Social Simulation and Modelling: Social Simulation is a multi-disciplinary effort that has increasingly established new challenges for the Artificial Intelligence and Multiagent Systems (MAS) community, by bringing the agent technology to face complex phenomena such as the ones found in social sciences. In addition, since social life could not be conceived without social interactions, other areas such as social network analysis have contributed to characterising and modelling the structures of networks, so as to understand the flow of relevant factors between network nodes (i.e., the agents).

Publishing Model: Open Access

Deadline: Aug 01, 2024

Applied Life Sciences: Artificial Intelligence and Biological Intervention for Reaching Sustainable Medical and Environmental Goals

Artificial Intelligence (AI) has shown enormous potential in changing healthcare by enhancing diagnosis, treatment, and preventative measures. AI systems can swiftly and reliably analyse large volumes of data, enabling more precise and prompt diagnoses. Machine learning algorithms, for example, may analyse medical images such as X-rays, CT scans, and MRIs to find anomalies and assist clinicians in making more accurate diagnoses. AI may also analyse patient data including medical history, vital signs, and test findings to forecast and prevent illnesses before they emerge. AI has the ability to improve therapy and medication development as well. AI can assist healthcare practitioners tailor treatment strategies for individual patients by analysing patient data, enhancing treatment efficacy and lowering adverse effects. AI may also help with medication research by analysing massive volumes of data to find possible targets for novel medicines, therefore speeding up the drug development process.

Biological interventions entail the use of biotechnology in the development of innovative cures and medications. These interventions have the potential to address illnesses at the molecular level, resulting in more precise and effective therapies. Gene therapy, for example, involves inserting genes into a patient's cells to fix or replace defective genes, perhaps offering treatment for hereditary illnesses. Cell-based treatments, including stem cell therapy, use cells to restore damaged tissues or organs. Tissue engineering is creating tissues or organs in the lab and transplanting them into patients, potentially solving the organ scarcity problem.

Healthcare providers can enhance patient outcomes, cut costs, and encourage sustainability in medical practice by combining AI and biological interventions. AI can help identify patients who are most likely to contract particular diseases and create individualized treatment plans utilizing biological interventions. This individualized method can cut down on pointless procedures and enhance patient outcomes. AI can also aid in the optimization of clinical trials for novel therapies, cutting down on the time and expense involved in drug development. Combining these two factors may result in more affordable and effective healthcare, supporting the sustainability of the medical and environmental profession. In this Topical Collection, original research articles and reviews are welcome. Research areas may include (but are not limited to) all AI application studies in the biological fields.

Publishing Model: Open Access

Deadline: Jun 30, 2024