Digital: Tech and Clinical Collaboration

IPT talks to Robert Strzelecki at TenderHut Group about the development of AI in the pharma industry and how its implementation will grow the sector

Robert Strzelecki at TenderHut Group

IPT: How has the use of tech in medical settings changed over the past decade?

Robert Strzelecki (RS): Perhaps no one can imagine modern medicine without advanced technologies. Over the last decade, so much has changed that it’s hard to list all the technological solutions that have become integral to the medical world. Currently, tech is an essential part of a doctor’s profession − from diagnosing and treating patients to managing medical documentation. It has improved both the quality of healthcare and the efficiency of medical staff.

The widespread adoption of electronic medical records (EMR) and electronic health records (EHR) has been revolutionary. The first is the digital version of paper patient charts containing the medical history of individuals within a healthcare facility. It allows, among other things, for the tracking of data over time, as well as identifying patients who should undergo screening or follow-up tests, and those who are due for vaccinations. On the other hand, EHR offers many more possibilities by gathering all the health-related data of a person from various sources such as clinics, laboratories or hospitals; its concept being to ‘travel’ with the patient who will then have constant access to their data. This ultimately speeds up diagnostics by streamlining the collection and exchange of patients’ medical information among specialists and can also reduce the duplication of prescribed tests. The development of the Internet of Things (IoT) has enabled the creation of medical devices that monitor patients’ health in real time and transmit data to EHR systems, which further supports healthcare.

The ever-increasing popularity of mobile applications related to healthcare has been remarkable. By using wearables such as watches and wristbands, these apps monitor the patient's health status and help them to follow medical recommendations in order to maintain good mental and physical conditions. Thanks to them, patients can seek medical advice through smartphones and other mobile devices, while doctors have constant access to data informing them not only about the vital signs of their patients, but also about the times when they take medications or their level of physical activity.

The development of telemedicine, which involves diagnosing and treating patients remotely, has increased access to healthcare for people living in smaller towns far from specialised medical facilities, as well as for people with disabilities. We especially appreciated its advantages during the COVID-19 pandemic. Thanks to telemedicine, patients can schedule online visits and consult with doctors without leaving their homes, while physicians can seek advice from specialists working in the most distant corners of the world. This has sped up diagnosis and, consequently, treatment.

The use of big data in medicine allows for the analysis of vast data sets, leading to a better understanding of health trends, predicting epidemic outbreaks and personalising treatment. Machine learning (ML) and artificial intelligence (AI) have found applications in various areas of medicine, such as disease diagnosis, analysis of imaging test results, treatment planning and optimisation of processes in medical facilities – as well as research for new drugs. The use of surgical robots allows for the performance of highly precise operations, minimising the risk of errors and accelerating the healing process. We aim to have AI-controlled robots carry out medical procedures with the utmost precision in the future.

Clinical decision support systems (CDSS) assist doctors in making accurate diagnoses and therapeutic recommendations, using available data and the latest research. CDSS enables doctors to quickly check whether there are new scientific research results or recommendations available for a specific medical condition that could be applied in patient treatment.

All the aforementioned technological solutions have significantly influenced the functioning of medical facilities, improving the quality of care, reducing costs and increasing access to medical services. However, as technology advances, we must pay increasing attention to issues related to the security of patient data, the methods of their collection, analysis and processing. This applies to both internet usage and AI. We must develop appropriate methods of teaching AI to eliminate the risk of errors resulting from improper data selection and violations of widely understood ethical principles.

IPT: What use do clinicians currently have for AI?

RS: For patients, the most visible application of AI is through chatbots and voice bots on patient support portals, answering frequently asked questions. Only a few of them realise how much AI helps healthcare professionals in their daily work. AI-based systems analyse medical data, medical images, laboratory test results and other health parameters to assist in disease diagnosis. Thanks to ML and AI algorithms, computers can recognise characteristic patterns and symptoms, helping doctors make more accurate diagnoses and better tailor treatment methods for patients.

AI also analyses epidemiological and clinical data in real time, predicting epidemic outbreaks or the spread of infectious diseases, thus allowing for faster responses and prevention of epidemic development. Furthermore, in the case of CDSS systems, AI can provide doctors with the latest research treatment protocols and recommendations based on the analysis of medical data. Robots using AI support doctors in precise and invasive surgical procedures, ensuring surgeries are safer and more efficient. AI-based solutions are used by healthcare professionals worldwide. For example, Aier Eye Hospital Group, a network of ophthalmic hospitals in China, uses them for diagnosing eye diseases and treatment planning. Proscia (a company founded by scientists from Johns Hopkins University) employs ML algorithms for the analysis of skin images and melanoma detection. The Israeli Sheba Medical Centre uses medical data analysis and AI for personalised therapy in heart disease.

AI is not only used to directly support the processes of diagnosis and treatment but also for better resource management, as seen during the COVID-19 pandemic when Cambridge scientists developed a tool for predicting the usage of ventilators and intensive care beds.

IPT: What are some ways to encourage clinicians’ trust in AI?

RS: Before we begin convincing anyone to use a new tool, it is worth considering whether they genuinely do not want to use it and what concerns or resistance they might have. Only when we understand this, can we proceed to present specific arguments. Building trust among doctors in AI requires time, openness to discussions and involvement from implementers and AI experts. It is important to emphasise that AI is meant to support doctors, not replace them.

There is tremendous potential for the use of AI in healthcare: imagine the benefits of combining the knowledge of different companies and organisations to create one unified AI system focused on treating people. Such a collaborative effort could lead to breakthroughs in personalised medicine, early diagnosis and effective treatment for many diseases. However, it is understandable as to why some doctors have concerns regarding the use of AI.

Ultimately, they are responsible for human lives and want to have maximum control over the treatment process to ensure it is best suited to their patients’ needs. Therefore, it makes sense to start implementing AI in smaller and more easily controllable areas of medicine. This helps doctors gain experience and build trust in this technology before its full implementation. New solutions for medicine should be developed in close collaboration with healthcare professionals to make them as effective and useful as possible.

By actively participating in clinical trials and the development of new algorithms, they can independently assess the technology being developed.

One of the most effective persuasive methods is providing reliable data and examples of usage. Credible scientific studies and clinical outcomes will confirm the effectiveness of AI in diagnosis, treatment and patient care. Adequate training and education on AI and its applications in medicine, as well as the benefits it can bring, are also needed. Let’s help doctors understand how AI works, its limitations and the possibilities of its application in their daily work. Increasing trust in AI can also be achieved through explanations of how algorithms function and what data and parameters AI uses to make decisions.

It is also necessary to ensure proper safeguards and adherence to ethical principles related to the use of AI in medicine, as highlighted by both the World Health Organization (WHO) and the European Parliament in their publications and recommendations concerning AI in healthcare.

IPT: How can clinicians assure patients that the wide adoption of new tech and AI is in their best interests?

RS: The patient, by putting their health and life into the hands of a doctor, bestows immense trust upon them. When a doctor proposes new treatment methods to a patient, they must be convinced of their efficacy themselves. When going for a lung X-ray, we don’t expect the radiologist to give us a lecture on how the image is taken. We trust that they know what they are doing and that they act in our best interest. However, the COVID-19 pandemic and the anti-vaccination movements have shown starkly that patient education is necessary. People need to understand why certain procedures are implemented and how they impact their lives.

Medical education delivered in a manner tailored to the recipients is necessary not only regarding the use of AI in medicine but also many other aspects. To achieve this, close collaboration between solution creators, doctors and state healthcare authorities is required. Adequate education of medical students is also essential. When entrusting themselves to a doctor, patients want to be certain that the doctor acts in their best interest. It becomes much easier for them when they encounter an empathetic specialist who takes the time and uses appropriate language to explain the disease and the way to combat it in an understandable manner. Just as in any other case, in the context of utilising AI, the doctor should be willing to listen to the patient, understand their concerns and answer questions. They should explain how AI can improve diagnosis, personalise treatment and monitor the patient’s health.

In building patient trust in technology, providing simple and real-life examples, sharing positive stories and successes of clinical AI usage can be highly useful. The doctor should also openly explain how AI works and what its limitations are. Clarifying that AI is a tool that supports doctors’ decisions and does not replace them can help alleviate patients’ concerns about losing individual medical care.

IPT: Where do you see digital health tech making the most improvements in the clinical sphere over the next five years?

RS: When we talk about the future of medicine, we cannot overlook the topic of the metaverse, which is a virtual world where we will eventually function as we do in the real world. The development of the metaverse and related technologies such as AI, XR – combining virtual (VR), augmented (AR) and mixed reality (MR) – IoT and blockchain technology, as well as edge and cloud solutions, offers tremendous opportunities for the advancement of medicine (diagnosis, treatment, therapy and post-operative support).

Boston Consulting Group (BCG) predicts that within the next 15 years, metaverse technology will become standard in many areas of healthcare. AR and VR will support patient care and XR will aid in clinical trials. The widespread use of digital twins will become common in remote hospital treatment. Blockchain will be used for safer and more efficient data management and creating medical identifiers for patient data, facilitating access to medical care. XR will expand global collaboration and knowledge exchange in operations performed by robots. The combination of XR with M-worlds (a type of live virtual ‘places’ where users gather and create content) will popularise virtual hospitals and clinics, reducing waiting times for medical assistance. These two technologies will also assist in recognising patients’ real needs more efficiently and providing them with the best care methods.

Personalised medicine will see significant development. Through remote peripherals such as blood composition monitors, blood pressure monitors and other parameters, algorithms will detect conditions such as heart attacks early and promptly respond by calling for help or instructing the environment on providing first aid. AI is already collecting volumes of data that were unimaginable not long ago. Once we teach it how to properly utilise this data, it has the potential to significantly improve the quality, comfort and pace of treatment.

An intriguing solution that enables data collection during medical procedures and has the potential to revolutionise the use of AI in surgical procedures is the application of MR during surgeries and medical procedures. The solution can track and record every movement of the surgeon and the patient’s vital signs, creating a personalised anatomical model of the patient. Gathering unbiased data from multiple surgeries creates a vast data set that can help train AI to make more accurate diagnoses, predict patient responses to treatment and educate intelligent robots to perform surgeries with greater precision, safety and avoidance of errors.

AI can prove to be a saviour in the face of an ageing population and the ever-growing group of people suffering from chronic diseases, as well as the problems faced by healthcare systems such as staffing shortages and related difficulties in accessing medical services.