An overview and a roadmap for artificial intelligence in hematology and oncology

AI methods can be used to address a broad range of clinical problems in hematology and oncology. In a consensus agreement of the AI working group, six classes of AI methods with established medical applications or potential clinical relevance were summarized (Fig. 1B). These methods are applicable to a broad range of clinical problems and can process a range of different data formats (Table 1). However, it should also be noted that there are AI approaches that can fall into several categories, e.g., when image and -omics data are jointly analyzed. In the following sections, each of these AI systems will be explained and examples for practical use cases will be given.

The analysis of digital images is one of the most common applications of AI in oncology (Kleppe et al. 2021; Shmatko et al. 2022; Farina et al. 2022; Shreve et al. 2022; Luchini et al. 2022; Echle et al. 2021). In fact, most clinically approved algorithms using AI in medicine are related to image data (Benjamens et al. 2020; Muehlematter et al. 2021; Alexander et al. 2020). Images are often prone to subjective human interpretation, and especially reading medical imaging data requires years of training. Given sufficient training data, computer models have been shown to be able to perform on narrow tasks at the level of human experts (Shmatko et al. 2022; Shen et al. 2019; Nagendran et al. 2020; Tschandl, et al. 2020). For example, AI has been used successfully to diagnose diseases such as diabetic retinopathy (Natarajan et al. 2019; Sosale 2020; Quellec et al. 2021), melanoma (Balasubramaniam 2021; Brinker et al. 2019), or lung cancer (Jacobs et al. 2021; Ibrahim et al. 2021) from image data. In addition, AI may be able to detect features that are not immediately apparent to the naked eye. For example, the prognosis of lung cancer patients can be predicted from routine computer tomography image data. Traditionally, in the 2010s, “Radiomics” machine-learning methods have used sets of expert-defined visual “features”, coupled with simple ML models (Aerts et al. 2014). More recently, end-to-end Deep Learning has been increasingly applied to such tasks (Ghaffari Laleh et al. 2022).For example, AI has been used to prognosticate the course of colorectal cancer from digitized histopathology image data (Skrede et al. 2020), or to predict the response to immunotherapy from radiological imaging data (Trebeschi et al. 2019; Wu et al. 2019; Ligero et al. 2021). Also, AI has been used to predict the presence of genetic alterations from image data (Shmatko et al. 2022; Kockwelp, et al. 2022; Kather et al. 2020), and is being discussed as a potential way to pre-screen patients for targeted molecular testing (Shmatko et al. 2022). Thus, AI-based image analysis systems can serve two purposes in oncology: they can speed up diagnostic processes, make them more consistent and readily available even in low-resource settings. On the other hand, AI-based image analysis systems can in some circumstances extract prognostic or predictive information from images, and thus serve as a biomarker for precision oncology. In both types of applications—automation and biomarkers—rigorous clinical evidence is required before these systems are used broadly in clinical routine (Geis et al. 2019).

Omics technologies (e.g., genomics, proteomics, metabolomics) generate large amounts of data that can be difficult for humans to interpret (Eraslan et al. 2019; Elmarakeby et al. 2021; Lipkova et al. 2022). In the last decades, computer-based methods to analyze these data have co-evolved with the laboratory assays (Shmatko et al. 2022). For example, the development of genome sequencing assays has been accompanied by the development of algorithms for sequence alignment and variant calling. Many bioinformatic machine-learning methods were developed for selecting molecular features and constructing molecular classifiers for disease entities or prognosis (Horn et al. 2018; Staiger et al. 2020). The development of all these methods has predated the era of Deep Learning. In fact, Deep Learning does not have a role in standard genetic diagnostics of cancer. However, additional useful information may be hiding in genome sequences and other -omics data. Several studies in the last few years also proposed Deep Learning methods to identify subtle patterns that were not identifiable by classical statistical approaches (Eraslan et al. 2019; Zeng et al. 2021; Tran et al. 2021). For example, AI has been used to predict outcomes and treatment response from sequencing data in cancer (Huang et al. 2020). Furthermore, -omics data might be combined with other data types (e. g., histopathology) to predict the clinical outcome of cancer patients more accurately (Chen et al. 2022). Researchers hope that by understanding genetic variants and their interplay in cellular networks in tumors they can find new therapeutic approaches, such as identifying potentially targetable neoantigens. Another field of application is the analysis of single-cell sequencing data often relying on neural network approaches such as variational auto-encoders. However, there is still a long way to go from academic research studies to embedding modern AI methods in clinical routine practice of genomically guided precision oncology.

NLP is a branch of AI that deals with the interpretation and manipulation of human language (Yim et al. 2016; Sorin et al. 2020; Kung, et al. 2022; Yang et al. 2022; Singhal, et al. 2022). For example, NLP is used in chatbots and digital assistants such as Siri or Alexa, which are capable of understanding natural language commands and providing relevant information in response. In medicine, language is often used as an unstructured way to store and transmit information. In this context, NLP can be used to extract information from clinical reports or electronic health records (Yang et al. 2022; Thomas et al. 2014). It should be noted that current systems do not simply search and extract text but integrate the context (e.g., it is essential whether a diagnosis is present or excluded or how the temporal dependencies between reported events are). This information can then be used to support decision-making or generate predictions about disease progression or response to therapy. Although applications of NLP in oncology have been proposed more than five years ago (Yim et al. 2016), limitations of NLP methods have precluded widespread use. However, the research field of NLP is evolving rapidly and applications which were unimaginable as little as one or two years ago are now reality. Most recently, at the end of 2022, new large language models (LLMs) GPT-3 and its variant chatGPT by the company OpenAI have raised broad interest. Modern LLMs can converse like humans, can respond to questions in medical examinations (Kung, et al. 2022) and generally can be used as a search tool, in particular to answer medical questions. In the next few years, we expect an exponential increase in the application of NLP in oncology. Human-level NLP systems are just emerging and hence, the process to translate this technology to clinical value in oncology is also just beginning.

Electronic health records (EHR) are at the core of documenting any patient contact in oncology, and also integrate multimodal data related to the diagnosis of cancer and biomarkers for precision oncology (Parikh et al. 2022; Morin et al. 2021; Araki et al. 2022). Much EHR data are unstructured or just loosely structured, making it difficult to mine historically. Moreover, such data is often distributed across different primary IT systems (e.g., laboratory information system or a hospital information system), which in turn may not be designed to support interoperability (the ability of a system to function effectively with other systems). AI methods have been applied to EHR data and promise to make the data available in a structured way and extract hidden value from the EHR data (Morin et al. 2021; Araki et al. 2022). Poor design of EHR is an unpleasant experience for many doctors and contributes to physician burnout (Muhiyaddin et al. 2022; Tajirian et al. 2020; Kroth et al. 2019). Thus, AI-based support systems to parse EHR data could be useful for users. EHRs often contain time series data which are challenging to analyze, but have been analyzed with neural networks or dynamical models (Kheifetz and Scholz 2019; Tomašev et al. 2019). While EHR comprises mostly data generated by healthcare staff, the patient perspective can be underrepresented. This gap is filled by patient-reported outcome- and experience measures (PROMs, PREMs) including a data source of the patient’s perspective which is increasingly being acknowledged in oncology as clinically relevant outcome measures in clinical trials and in certification processes evaluating health care in cancer centers (Parikh et al. 2022). EHR and PROM/PREM data are part of the loosely defined category of “real-world data” (RWD). We expect the AI-based analysis of RWD to be of much higher importance in the coming years, based on technological advances in multimodal AI models, NLP, and structured efforts to extract value from these data (Hegselmann, et al. 2022). The technology is now ready to be applied to many use cases, but progress in the field will be limited by asking the right medical questions, identifying useful ways to apply this technology in the clinic and the data quality of the original documentation.

Several AI systems have been developed to automate part of the complex decision-making process in oncology (Kheifetz and Scholz 2019; Schmidt 2017; Rodríguez Ruiz et al. 2022). Some of them use multidisciplinary tumor boards as a blueprint. The defining feature of such boards is that specialists from different disciplines (e.g., surgery, medical oncology, radiation oncology, radiology, pathology) come together to discuss individual patient cases and make treatment recommendations. The clinical history of a given patient, all available results from diagnostic tests, patient preferences and current medical evidence are integrated in this process (Frank 2022). The level of complexity in clinical decision-making in multidisciplinary tumor boards is increasing with the expanding significance of genomic and molecular data for personalized treatment recommendations in cancer care (Büttner et al. 2019; Horak et al. 2021). As early as 2012, large-scale and well-funded programs have aimed to automate such recommendations, but so far, they have not reached clinical routine due to the complexity of extracting standardized recommendations from inconsistent data (Schmidt 2017). Despite these experiences in the past, the use of AI to automate decision-making in a way analogous to multidisciplinary tumor boards is still being commonly mentioned as a promising application. (Rodríguez Ruiz et al. 2022)

For users, the boundaries between medical hardware devices and consumer devices are blurring more and more and wearable sensors are becoming more common. For example, smart watches are widely used nowadays and can collect a wide variety of data, including information on heart rate, oxygenation, and movement. AI algorithms may be able to make sense of these high-dimensional data and provide insights into a patient's health (Sabry et al. 2022). For example, wearable sensors have been used successfully to detect early signs of disease such as sepsis (Ghiasi 2022). In hematology, this could be used to identify patients at risk for severe side effects and impending organ failure, including patients after myelosuppressive chemotherapy or stem cell transplantation (Nessle et al. 2022). Despite the obvious potential in this area, clinical evidence is still scarce and only a few dozen published studies have investigated an application of AI-based analysis of wearable sensor data in oncology (Sabry et al. 2022; Ghiasi 2022). Often, such systems do not meet the criteria for regulatory approval as medical devices—especially if led by academic researchers. Again, this area will depend on hematologists and oncologists identifying the clinical need for new studies, running proof-of-concept studies and ultimately creating clinically relevant evidence how AI can be used for a patient benefit using properly designed clinical trials.

In this article, we defined six main application areas in hematology and oncology where AI is already contributing or can contribute in the future. Some of these areas are already quite mature, for example, in AI-based image analysis, there are already dozens of approved medical devices that can be used in everyday clinical practice. Basic research or proof-of-concept work is still required in other areas, such as the support or partial automation of tumor board recommendations, modeling of individual time series data or the evaluation of sensor data with AI. Because of recent technological advances in AI, this technology is permeating our society more and more, and it is very likely that clinical management of patients in hematology and oncology will further benefit from AI approaches. As a result, it is critical that the transition to AI-assisted hematology and oncology is closely accompanied by a respective medical informatics infrastructure, new clinical trial concepts, and improved acceptance by doctors and patients.

Whenever personal digital data are collected and linked together, the question of data privacy for the subject arises. It goes without saying that data in medicine must be securely stored, transferred, and protected from unauthorized access. This raises new issues in the age of artificial intelligence. Under certain conditions, it is possible to extract raw data from an AI network that has been trained on medical data and then published or sold for further use. In recent years, technological advancements such as differential privacy and secure multi-party computation have attempted to address this problem by introducing noise into the raw data of the training set or by privacy preserving computational approaches. However, in any case, it is critical that if possible, anonymized raw data are included from the start of the training process, and that, as is standard in medicine, an ethical approval and informed consent of the patient is available prior to the use and evaluation of patient-related data. (Seastedt et al. 2022)

Biases are another weakness of artificial intelligence and are particularly apparent in data-driven supervised machine-learning approaches (Andaur Navarro, et al. 2023). Machine-learning networks recapitulate and learn subtle patterns from training data, but these patterns are distorted in medicine and almost every other area of society due to prejudices and structural disadvantages for specific groups of people. If, for example, in the population represented in the training data, the implementation of complex molecular diagnostics is affected by place of residence, socio-economic factors, education, age, gender, or other factors, AI networks will learn these patterns on multiple levels and will eventually be able to apply them. Thus, use of an AI algorithm in the clinic may not generalize well enough to represent the diversity of future patients and therefore have an adverse effect on certain patient groups. The same is true if AI algorithms are trained and tested even on very large data sets from single institutions. Finally, high-parametric models are prone to overfitting, which reduces the performance in samples not used for model calibration. There is no perfect technical solution against such biases, but it is critical that both scientists and developers who set up the machine learning / AI system, as well as the end users, are made aware of them. Moreover, it should also be remembered that an appropriate study design is key to answer certain research questions (i.e., to evaluate the efficacy of an AI system, new concepts for randomized controlled trials will be needed). Further basic research in oncology is required to quantify the existence of such biases and to identify suitable areas of applications of these approaches and the potential harm to patients. It is precisely here that we see an important role for hematologists and oncologists, who, together with patients and patient advocacy groups, must ensure that such new technologies ultimately serve the well-being of patients and do not disadvantage specific patient groups based on their ethnicity, age, or other characteristics.

Despite the possibilities of AI methods for hematology and oncology, these technologies often lack explainability since the underlying quantifications are based on highly complex calculations or learned network parameters which are not always directly relatable to biological mechanisms or structures. Despite the success of so-called explainable AI strategies (Minh et al. 2022), there are still many challenges, especially once these algorithms are used in critical situations such as clinical diagnoses and predictions (Ghassemi et al. 2021). Therefore and especially in the healthcare context, additional strategies have to be developed to enable informed clinical decisions. A possible approach could be to inform hypotheses-free neural network models by biological knowledge or by relating learned network structures or classifiers to biological quantities or risk factors. Integration of AI into clinical reasoning has to be done carefully, meaning that the computed results need to be treated as additional evidence helping clinicians to gain a more complete picture. We see this as an important area of fundamental research for the next few years.

We expect that physicians will be increasingly confronted with artificial intelligence applications in the future. (Mosch et al. 2022) It is, therefore, imperative that the ability to assess the outputs of such artificial intelligence systems is part of medical education and training. Even today, simple computer skills pose a challenge to some doctors, such as typing quickly on keyboards or the intuitive use of graphical user interfaces. In the future, the complexity of our world will continue to increase massively due to artificial intelligence, and that makes it necessary for doctors to continuously learn the required skills during their studies, and later, in their careers. We see a particularly important role here for medical professional societies, which will set up and implement the appropriate further training curriculum for doctors.

We expect that AI will be broadly used to aid clinical decision-making and improve the quality of care in the near future. While these technologies are maturing fast, the sensible clinical use of AI in hematology and oncology is determined by defining appropriate areas of application and by establishing the required IT infrastructures. Moreover, as for every new treatment concepts, AI-based approaches need to demonstrate their superiority in well-designed clinical trials. AI methods could result in disruptive changes in the clinical practice. For example, access to data may change, and treatment decisions may become more and more influenced by IT systems rather than practitioners. Thus, the way medicine is performed requires monitoring and guidance. In Germany, large-scale medical informatics initiatives have been proposed as platforms to facilitate data-intensive research with clinical routine data. However, on top of this, new clinical trial strategies are also required to prove the advantage of such AI-guided individual therapy concepts. In all of these efforts, patients, physicians and a network of experts in methodology should guide and lead the AI transformation of hematology and oncology and that professional medical societies such as the German Society for Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the German Informatics Society (GI), Special Interest Group Digital Health, together with other established initiatives and professional societies, will accompany and supervise this process.