doi: 10.56294/saludcyt2023450




The utilization of artificial intelligence in life science research and healthcare delivery


Utilización de la inteligencia artificial en la investigación de las ciencias de la vida y la asistencia sanitaria


Harshal Shah1 e9815d877cd092a19918df74e04f0415_400x400 *, Bhuvana Jayabalan2 e9815d877cd092a19918df74e04f0415_400x400 *, Amali Mery3 e9815d877cd092a19918df74e04f0415_400x400 *


1Department of Computer Science and Engineering, Parul University. Gujarat, India.

2Department of CSIT, Jain (deemd to be University). Bangalore, India.

3College of Nursing, Teerthanker Mahaveer University. Moradabad, Uttar Pradesh, India.


Cite as: Shah H, Jayabalan B, Mery A. The utilization of artificial intelligence in life science research and healthcare delivery. Salud, Ciencia y Tecnología. 2023;3(S1):450.  


Submitted: 23-05-2023                       Revised: 14-06-2023                                       Accepted: 01-08-2023                       Published: 02-08-2023


Editor: Dr. William Castillo-González e9815d877cd092a19918df74e04f0415_400x400  

Associate Editor: Fasi Ahamad Shaik e9815d877cd092a19918df74e04f0415_400x400 




The goal of this research is to evaluate the effectiveness of the system for innovation and identify the reasons that prevent Artificial intelligence (AI) healthcare technology advancements connected to the life sciences sector from being implemented. To evaluate the structural and efficient dynamics of AI healthcare technology advances associated with the life science business in West Sweden, the socio-technical analytical framework of Technological innovation systems (TIS) was employed. Using a mixed-methods research methodology, the case study triangulates qualitative and quantitative information since subordinate distributed sources and discussions with twenty-one experts and twenty-five life science industry leaders. According to the findings, the functioning of the innovation system is largely constrained by its limitations, which include a lack of resources and inadequate statements from top healthcare experts about their requirements for advancing healthcare via the use of AI technological advances. This research demonstrates that to enhance the performance of the innovation system, governmental interventions aimed at expanding the pool of resources as well as creating vision and purpose statements for the advancement of healthcare via AI technology breakthroughs may be promoted. By using the socio-technical TIS paradigm in a hospital setting, this research advances our knowledge of the processes and interdependencies across system works.


Keywords: Artificial Intelligence (AI); Healthcare; Life Science; Technological Innovation Systems (TIS).




El objetivo de esta investigación es evaluar la eficacia del sistema para la innovación e identificar las razones que impiden que se lleven a la práctica los avances en tecnología sanitaria de inteligencia artificial (IA) relacionados con el sector de las ciencias de la vida. Para evaluar la dinámica estructural y eficiente de los avances de la tecnología sanitaria de IA relacionados con el sector de las ciencias de la vida en Suecia Occidental, se empleó el marco analítico sociotécnico de los sistemas de innovación tecnológica (SIT). Utilizando una metodología de investigación de métodos mixtos, el estudio de caso triangula información cualitativa y cuantitativa desde fuentes distribuidas subordinadas y discusiones con veintiún expertos y veinticinco líderes de la industria de las ciencias de la vida. Según las conclusiones, el funcionamiento del sistema de innovación se ve restringido en gran medida por sus limitaciones, entre las que se incluyen la falta de recursos y las declaraciones inadecuadas de los principales expertos sanitarios sobre sus requisitos para hacer avanzar la atención sanitaria mediante el uso de los avances tecnológicos de la IA. Esta investigación demuestra que, para mejorar el rendimiento del sistema de innovación, pueden promoverse intervenciones gubernamentales dirigidas a ampliar el conjunto de recursos, así como a crear declaraciones de visión y propósito para el avance de la asistencia sanitaria mediante los avances tecnológicos de la IA. Mediante el uso del paradigma socio-técnico TIS en un entorno hospitalario, esta investigación avanza en nuestro conocimiento de los procesos y las interdependencias a través de las obras del sistema.


Palabras clave: Inteligencia Artificial (IA); Asistencia Sanitaria; Ciencias de la Vida; Sistemas de Innovación Tecnológica (SIT).





AI is the emulation of human intellect in devices that have been designed to learn, understand, and solve problems similar to people. It includes a broad variety of methods and strategies aimed at helping machines to carry out operations like voice recognition, visual perception, decision-making, and natural language processing that ordinarily need human intellect. AI systems may be created to function alone or to supplement human talents while collaborating with people. They are based on a variety of subdivisions, including robotics, computer vision, deep learning, natural language processing, and machine learning.(1) The systematic exploration and study of organisms and how they interact with their surroundings is referred to as life science research. It includes a broad spectrum of scientific fields, such as biology, genetics, biochemistry, microbiology, ecology, and more. Expanding the knowledge of the basic principles and processes that control life at all scales, from the molecule to the ecosystem, is the goal of life science research. To study and understand the complexity of living beings, scientists use a variety of approaches, techniques, and technologies in life science research. To address certain research topics, they could carry out experiments, gather data, make observations, analyses it, and create hypotheses. Research in the subject of life sciences may be carried out in a variety of contexts, such as labs, field investigations, clinical trials, and computer modelling. These insights relate not just to AI as a field of study, but also to the most effective strategies for using AI in corporate settings.(2)

Medicine, biotechnology, agriculture, conservation, and environmental science are only a few of the fields where life science research is important. It forces improvements in healthcare by assisting with the creation of fresh medications, diagnostic equipment, and disease-specific therapy. Genetically modified organisms (GMOs) for food security and increased crop output are other agricultural advancements it supports. Furthermore, by examining threatened ecosystems and species, life science research contributes to efforts to conserve by generating sustainable preservation and management plans. AI can transform life science research by allowing effective data analysis, speeding up the drug development process, enabling customized therapy, enhancing image analysis, assisting with literature reviews, and improving the experimental design. By using AI tools, scientists may gain new knowledge, better comprehend intricate biological processes, and eventually progress medical research and the creation of innovative treatments.(3) The process of delivering health care, services, and treatments to people and communities that are in need is referred to as healthcare delivery. Preventive, diagnostic, curative, rehabilitative, and palliative care are all included in the system of planning, directing, and providing healthcare services. Healthcare providers (doctors, nurses, technicians, etc.), healthcare facilities (hospitals, clinics, pharmacies), medical equipment and technologies, pharmaceuticals, health insurance companies, and government agencies in charge of regulating and overseeing healthcare systems are just some of the many components and stakeholders involved in healthcare delivery.(4)

AI can change the healthcare delivery system, improve patient outcomes, and increase efficiency by enhancing the skills of healthcare personnel, enhancing diagnoses, and allowing individualized treatment. To fully use AI in healthcare delivery, more study, development, and cooperation between AI specialists and healthcare professionals are required.(5) The application of cutting-edge computational tools and algorithms to enhance different elements of life science research and healthcare delivery is referred to as AI in these domains. AI is a wide phrase that includes several subfields, including computer vision, deep learning, natural language processing, and machine learning. AI may be used in life science research to examine big biological datasets, such as genomics, proteomics, and metabolomics data.(6)

It may assist in locating patterns, connections, and biomarkers that conventional statistical techniques would find difficult to distinguish. Based on already collected data, AI systems may be taught to make predictions and provide research ideas. To supplement human knowledge, hasten research breakthroughs, increase diagnostic precision, and improve patient care outcomes, AI in life science research and healthcare delivery intends to make use of modern computing capabilities.(7) AI is the use of computing techniques and technology to analyses massive amounts of data, derive valuable insights, and support decision-making in the life sciences and healthcare industries. AI systems are created to imitate human intellect and learn from data patterns to carry out activities that traditionally demand human cognitive ability. AI is used in life science research in a variety of fields, including genomes, drug development, and illness detection. Massive genomic data may be analyzed by AI systems to find genetic variations linked to illnesses, forecast medication reactions, and create individualized treatment regimens.(8)

AL-Hashimi et al.(9) offered a thorough examination of the use of AI technology in the healthcare industry and highlights its contributions to the battle against the deadly COVID-19 epidemic. AI is a potentially effective weapon in the battle against COVID-19's proliferation. During the pandemic's start, the usage of AI in the healthcare industry has been clearer. Bickman(10) provided an overview of the present mental health treatment system, highlights key issues, and proposes potential solutions. In this regard, I use the evolution of my research over the last half-century to stress the necessity for new approaches to studying mental health care delivery.Wan(11) examined AI research in healthcare outlined in this article. Partnerships between academic researchers, healthcare professionals, and industry specialists in software design and data science are necessary due to the convergence of several disciplines in the conduct of healthcare research. A patient-centered care system that improves treatment outcomes while decreasing costs is important to a value-based approach to healthcare.

Browning et al.(12) looked at how COVID-19 has affected clinical and academic pathology and how digital pathology and AI may play a crucial role in protecting clinical services and pathology-based research in the future. Changes to protocols halts in investigations, and reallocation of resources are only some of how COVID-19 has affected scientific inquiry and clinical trials. Carriere et al.(13) offered a point of view on how AI and Machine Learning technologies like Natural Language Processing may be utilized to aid in the evaluation and rehabilitation of both acute and chronic diseases. The global healthcare system and healthcare delivery have been severely impacted by the COVID-19 epidemic. Although efforts are underway to develop and test antiviral therapies and vaccines for COVID-19, policymakers have implemented social distance and isolation regulations to mitigate the virus's spread. Raza(14) provided an explainable AI-based healthcare model built on the Internet of Medical Things for monitoring the well-being of the elderly. Due in large part to the rapid demographic shift toward an elderly population and the consequent rise in healthcare expenditures, disease prognosis, and management via various healthcare devices and services have lately received considerable attention.

Zhao et al.(15) discussed 163 accounts on COVID-19 technology uptake from the press and scientific literature, then organized them by kind of use case and tested their functionality. Fifty proposals were chosen to be reviewed, all of which included some kind of technology connected to robotics, AI, or digital technology. The possible influence of the COVID-19 pandemic and the engineering features of each instance were evaluated. Mahadevaiah et al.(16) provided recommendations for selecting, accepting, commissioning, implementing, and ensuring the quality of a CDSS, with an emphasis on machine and deep learning-based technologies. CDSSs have demonstrated promising results in enhancing healthcare quality, boosting patient safety, and decreasing expenses. An insufficient or flawed CDSS, however, has the potential to lower healthcare quality and put patients in danger, therefore its usage is not without risks. El-Sherif et al.(17) determined the concerns and looks forward to an intelligent healthcare future by drawing on the successes and failures of telemedicine and AI during the COVID-19 pandemic. The World Health Organization has said that AI might help deal with the epidemic. The current worldwide healthcare crisis, the development of next-generation pandemic preparedness, and the restoration of resilience all need the use of non-medical interventions, and AI is one of the most important technologies of the fourth industrial revolution. Alhashmi et al.(18) provided a critical assessment of what AI entails and how it may be related to the Technology Acceptance Model to talk about people's openness to using AI technologies. To address the study issue of what elements are most important when implementing AI initiatives in the health sector, an organized assessment of 23 research publications was released and conducted.

Jungwirth et al.(19) examined the viability of deploying AI as a scientific co-author and assessed GPT-3's potential to improve public health. Human writers solicited input from the AI, which included scientific quotes, and checked it for validity. The use of AI to automate the analysis of data, provide fresh perspectives, and facilitate the discovery of new information has the potential to radically alter the academic landscape. Gonzalez-Argote(20) discussed the current status of medical education and propose a paradigm for incorporating AI into this field. Medical education must be revised. Skilled medicine-machine interaction will become more important as medicine enters the age of AI, as data is increasingly used to enhance clinical decision-making.



To fully comprehend the procedures for AI developments connected to the life science industry, it is necessary given the nature of the topic under investigation in this study to thoroughly review specialists' viewpoints. Given that this strategy is intended to investigate a phenomenon inside a specific environment, a case study of quality is an appropriate Method of research in this situation. A mixed-methods technique was used for this case study and thought to be appropriate since it was anticipated that the triangulation of qualitative and quantitative data would provide a thorough grasp of the research. The life science sector comprises businesses focused on creating, manufacturing, and distributing healthcare technology including drugs, equipment, and medical devices. Based on end-user applications, the researchers in this study categorized advancements in AI healthcare technology, as shown in table 1.


Table 1. Classification of AI-based medical technology advances based on end-user applications

End-user application


Decision support

Making smarter judgments requires the use of advanced picture analysis


Healthcare personnel are trained using medical simulators


Tools for figuring out links between symptoms and illnesses

Medical device therapy

Signals from the brain are translated by artificial body components

Patient support

Patients employ digital applications to improve compliance

Pharmaceutical development

Drug development and discovery


Data collection and analysis

Data were gathered through secondary data from authoritative and generally available publications and qualitative interviews. The discussions were separated into three stages (1) Studies with empirical data, (2) evidence gathering, and (3) analysis and exploration of expert experiences.


Phase-I- in-depth interviews

Eight stakeholders were interviewed in-depth to learn more about the foundational components, perceived limitations, as well as perceived strengths of the field. To enable participants to communicate their opinions and experiences, an interviewing guide with open-ended questions was created. The snowball sampling method was employed to pick the samples. Three researchers, one doctor, one local policymaker, two businesspeople, and one investor were all interviewed. Each interview lasted 30 to 60 minutes on average. Using the qualitative content analysis application Navigo, interview notes were transcribed and examined.


Phase II- Phone surveys

To gather empirical data, phone interviews were performed with life science businesses that have created or are creating AI technologies. Secondary data and those who took part in phase I were examined to find firms. Additional information, including financial results and the number of workers, was gathered from public origins of information. Thirty businesses was chosen, and mobile calls and emails were sent to them. Five firms were not included in the study because they had not yet considered using AI technology. We conducted phone interviews and asked each corporate representative to describe their AI ambitions based on end-user applications. The researchers opted to include all apps for further study since in 6 instances, there was the utilization of many consumer-facing programs. To analyze commercially deployed technologies employed in healthcare, three apps that were explicitly used for pharmaceutical research by the industry were left out. The researchers classified the forms of innovation in the data that were collected. The implementation stage, as shown in table 2, was ranked by each firm representative.


Table 2. The acceptance of AI healthcare technology advances is graded using metrics



Level of adoption


New technologies have emerged to replace the old ones.



Narrow applications of the technology were introduced.



Applications involving technology were accepted in mature markets.



Pilot projects were produced from previously conceived ideas or prototypes.



Ideas and frameworks are in the works.



Phase III- semi-structured expert interviews

Open-ended questions were used in semi-structured expert interviews to enable a deeper investigation of the phenomenon. In light of the available knowledge and theoretical context on the phenomena obtained in phase I, an interview guide was developed. Phase II stakeholders were invited to appoint AI specialists inside their organizations if they rated the adoption as five or higher. Experts were defined as those who have gained an in-depth knowledge of AI technologies through study or work in fields such as study and development, IT, computer science (CS), advertisement, or company growth. All of the specialists who were contacted consented to take part in the investigation. Representatives from each firm were asked to rate the extent to which they have used the methods laid forth in table 3. The average length of an interview was between twenty and thirty minutes. Using the qualitative content analysis application NVivo, interview notes were transcribed and categorized by themes that matched the study questions.


Table 3. Sorting employees from firms A, B, C, D, E, and F into categories

Enterprise dimension






Research and development

B (4)

E (1,0)

F (1,0)

CS or IT

B (1,0)

E (1,0)

C (1,0)

Market and business progress

B (1,0)

A (1,0)

C (1,0)

D (1,0)



AI is developing rapidly and has the potential to revolutionize healthcare in many ways, including better patient outcomes, more efficiency, and a deeper knowledge of the illness. Despite AI's promising future, there are serious concerns that must be addressed, including those involving personal information, government oversight, and ethics.


Structural components

The healthcare industry-specific definition shown in figure 1 was generated using knowledge supplied by researchers and additional information from sources that have been published. It features major players categorized into educational, marketplace, and management areas on the vertical plane in addition to the value chain along the flat axis. Central players in the academic arena were noted to include “Gothenburg University, Chalmers University of Technology, and regional universities and colleges”. 25 reputable life science businesses were present in the market sector. The businesses were grouped by the European Union's classification of small and medium-sized businesses. Large-sized businesses were defined as organizations that did not fit any SME criteria. 15 micro-sized businesses were included in the research, in addition to 5 big businesses, 2 medium businesses, 3 small businesses, and 15 very small businesses.


Figure 1. Healthcare sectoral map


Functional assessment

The growth and spread of information (F1)

There is a knowledge shortage among decision-makers and AI professionals at many big and medium-sized companies. Almost all executives and managers have the necessary technical expertise to comprehend the possible effects that AI advancements may have on the industry or organizations. On the other hand, AI professionals inside the firms have the broad business perspective required to support higher AI expenditures. Industrial players lacked a basic understanding of the market, mostly because they had little experience with AI healthcare technology improvements. Figure 2 shows the modest growth in the number of technical papers discussing AI healthcare technology breakthroughs, but the total number of publications in the domains of life science and healthcare remains relatively small. Researchers from West Sweden who represented all industrial sectors wrote around six times as many papers as would be predicted based on worldwide Technological innovation systems (TIS) while looking at the total amount of articles published on the topic of AI technology across all industries. The extensive cooperation between the local automobile industry and academics, which has produced several research initiatives about electro-mobility, accounts in part for the high number of publications. As a result of the extensive number of operations in other areas, there existed a wide basis of technical knowledge.


Figure 2. AI healthcare technology innovations


Legitimation (F2)

To determine the function strength of legitimation (F2), subjective and additional information from sources that were published. In light of information liability and privacy considerations, AI specialists voiced their unhappiness with the challenges associated with using health data. For instance, recycling medical information collected by prior investigations can be an essential resource throughout development; however, consent forms that have been obtained from patients do not permit programmers to employ information initially intended for different study assignments, generating uncertainty regarding the ownership of data.


Resource mobilization (F3)

Resource mobilization (F3) was evaluated using both primary data from public sources and secondary data collected through qualitative research. Infrastructure and data accessibility are essential for the success of AI initiatives, with the availability of pertinent data serving as the foundation for training and optimizing the functionality and accuracy of AI.


Guidance of search (F4)

Additional information from public sources from participants in the market for downstream products was used to evaluate the function assistance of search (F4). The selection of actors was based on their familiarity with end-user demands and closeness to patients and healthcare facilities. Examples of the analyses included are University Hospitals and Healthcare Innovation Platforms. Despite several papers on the TIS by eminent healthcare experts, these pieces often did not address the requirement for AI technology advancements in the healthcare industry. Upstream market participants who felt that the demands to advance healthcare via AI technology advances had not been sufficiently articulated by top healthcare experts supported the conclusion.


Entrepreneurial experimentation (F5)

The function of the entrepreneurship experiment (F5) evaluation factors were the total number of participants, the total number of performances, and a total number of different approaches employed. Developed creativity locations, scientific gardens, and accelerators that actively engaged in a wide range of activities to encourage entrepreneurship and find fresh start-ups based on university spin-offs contributed to the region's strong environment for innovation, in turn, strengthened the function. This study uncovered a wide variety of testing and therapeutic end-user applications; 31 percent of these were related to decision support, 19 % to patient care, sixteen percent to education, 13 percent to the use of medical devices in treatment, 12 percent to the creation of new pharmaceuticals, and 10 % to the conduct of health diagnosis.


Market formation (F6)

The process of market formation (F6) was analyzed using qualitative data on how different firms gained entry to the market and the breadth of currently accessible AI technological breakthroughs. There was widespread agreement that collaboration will help technology-driven SMBs, MNCs, and enterprises reduce their time to market. Table 4 shows that neither big nor medium-sized enterprises have used any healthcare-related AI technology improvements in their commercial operations. Only 31 % of AI healthcare technology breakthroughs developed by micro and small firms were commercially realized, with most developments aimed at niche markets and not yet producing substantial income. Researchers concluded that there was little market for artificial intelligence-based healthcare technologies because of this.


Table 4. AI healthcare technologies currently on the market

Developments in AI used in commercial healthcare settings

Company size







12 % (2)

88 % (7)


101 % (2)


51 %(2)

51 % (2)


22 % (2)

28 % (5)

51 % (6)


System-wide synergies (F7)

Several experts found it challenging to launch new initiatives involving players from other fields, leading them to seek out formal networks to expand their horizons and foster more interdisciplinary cooperation. Some big and medium-sized businesses were interested in teaming up with technology-focused start-ups since they had the subject expertise but not the technical ones. In contrast, most of the smaller businesses in our analysis formed because of unique technological developments, and thus lacked the requisite expertise in their respective fields. Interdisciplinary work and the launch of new innovative initiatives were also hampered by the so-called professor exemption law, which shielded university workers' IP, and by strong limitations on cooperation among the private sector and the medical community.


Functional pattern

Healthcare organizations were pushed by decision-makers to specify their requirements for using AI technology to improve healthcare. More actors will be encouraged to join the sector as a result of clearly expressed demands and growing legitimacy for AI breakthroughs. As a consequence, more funds would be allocated to the creation of new products, strengthening the knowledge base. So, legitimation (F2), entrepreneurial experimentation (F5), and resource mobilization (F3) are all indirectly impacted by the direction of search (F4). It also has an indirect effect on knowledge development and dissemination (F1). They conclude that by focusing on improving F3 (resource mobilization) and F4 (search guidance), they may set off a chain reaction that will have both indirect effects and direct effects on all functions figure 3.


Figure 3. Integration of AI into health care activities



The goals of this research were to evaluate the effectiveness of the innovation framework and pinpoint the barriers to the advancement of AI healthcare technology in the life sciences sector.


Policy Implications and intervention

The goal of policy efforts and actions might be to strengthen specific system-blocking mechanisms as functions interact and have an influence on one another. Based on the findings of this study, it is clear that the innovation framework faces significant limitations due to inherent flaws, such as insufficient resources and poor communication from senior healthcare professionals regarding their needs for improving healthcare through the application of AI technological advances. A particular strategy for dealing with the system-blocking processes discovered in the present research might be to develop a discussed project inventory device, preferably supported by agencies of government, to promote multidisciplinary cooperation and enable participants since the governance academic, and market, spheres to effort organized on assignments through specific objectives and areas. If these activities were to increase, actors would have more opportunities to learn concerning and explore AI technology.


Theoretical contributions

In additional inspections related to creativity systems, the requirements for business experimentation (F5) and search guidance (F4) in the initial stages of technological advancement have been shown to depend on the growth and diffusion of information (F1). Figure 4 depicts the results of our study, which reveal that the production and dissemination of information (F1) and entrepreneurial experimentation (F5) are reliant on one another and provide the groundwork for legitimization (F2), resource mobilization (F3), search guidance (F4), and market formation (F6). Furthermore, the findings show that the creativity organization's effectiveness is largely constrained by the scheme flaws of low properties and poor statements from top healthcare experts on their requirements for enhancing healthcare via AI technology breakthroughs. Thus, our research's theoretical implications suggest a possible relationship between information growth and dissemination (F1) and entrepreneurial experimentation (F5) in the formative stages of innovative scheme construction.


Figure 4. Relational interactions among innovation system functions



In conclusion, AI has completely changed the face of life science research and healthcare delivery by facilitating more rapid and precise data processing, boosting diagnostics and tailored medication, and improving patient care. They can anticipate AI to continue altering these sectors, resulting in more effective therapies, improved outcomes for patients, and eventually increasing the general standard of healthcare, as long as researchers, healthcare practitioners, and AI specialists continue to collaborate on its development and implementation. This study reveals that government interventions aiming at increasing the available resources and developing vision and purpose statements for the betterment of healthcare via AI technology breakthroughs may improve the efficiency of the system for innovation. The findings of the research suggest that the innovation system's functioning is hampered by its constraints, such as a lack of properties and poor statements from leading healthcare specialists about their requirements for enhancing healthcare through the use of AI technical developments. They recommend future investigation studies employing this TIS in a healthcare environment to better understand innovation uptake, although more research is required to enhance the empirical base initially.



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No financing.






Conceptualization: Harshal Shah, Bhuvana Jayabalan, Amali Mery.

Methodology: Harshal Shah, Bhuvana Jayabalan, Amali Mery.

Drafting - original draft: Harshal Shah, Bhuvana Jayabalan, Amali Mery.

Writing - proofreading and editing: Harshal Shah, Bhuvana Jayabalan, Amali Mery.