Transforming Healthcare and Higher Education with Artificial Intelligence (AI)

This blog comes from Alwin Puthenpurakal, an experienced senior lecturer and researcher working at The University of Greenwich. @AlwinP13 @UniofGreenwich

In September 2021, the UK’s Secretary of State for Digital, Culture, Media, and Sport (DCMS) unveiled the inaugural Artificial Intelligence (AI) strategy, charting a 10-year course to position the UK as a “global AI superpower”1. The UK governments’ White Paper on AI regulation, accentuated a pro-innovation stance2. The strategy’s foundation rests on three pillars: investment in the long-term requirements of the AI ecosystem, ensuring widespread AI benefits across sectors and regions, and establishing effective AI governance. The overarching objectives include substantial growth in the number and diversity of AI systems discovered in the UK, harnessing economic and productivity gains from AI, and creating the world’s most trusted and pro-innovation AI governance system.

AI has emerged as a catalytic force across sectors, notably in healthcare and higher education. Over recent years, AI technologies have redefined healthcare delivery and enhanced the educational landscape. A notable breakthrough is evident in AI’s application to disease diagnosis and early detection3. AI-powered diagnostic tools, employing image recognition algorithms, have proven invaluable in interpreting medical images like X-rays, MRIs, and CT scans. The capacity to identify anomalies and abnormalities often overlooked by human observers aids in early disease detection, exemplified by Google’s DeepMind, which developed an AI system achieving remarkable accuracy in detecting eye diseases4. This supports personalized treatment plans by analyzing patient-specific data, including medical records, genetic information, and lifestyle factors. AI algorithms, guiding clinical decision-making, minimize trial-and-error approaches, enhance personalization, and transform patient care and treatment outcomes, particularly in complex long-term conditions5.

In the realm of healthcare, AI has made remarkable strides in the field of drug discovery and development. Traditionally characterized by slowness and expense, the drug discovery process has witnessed a transformative acceleration through AI. By analyzing extensive datasets, predicting potential drug candidates, and simulating their effects, AI, exemplified by companies like Atomwise6, has significantly reduced the time and resources required for developing drugs to treat various diseases. The impact of AI extends further into telemedicine, remote patient monitoring, and predictive analytics, gaining immense popularity during the Covid-19 pandemic. AI-driven telehealth platforms and remote patient monitoring allow patients to receive advice and monitoring from the comfort of their homes, alleviating the burden on healthcare professionals and enhances the overall patient experience. From a healthcare service provider perspective, the ability to monitor patient admission rates, staff schedules, waiting times, and overall patient care and satisfaction through various data points and analytics paves the way for more digitally oriented and efficient healthcare management.

Turning to the Higher Education landscape, AI is revolutionizing education by facilitating personalized learning experiences. Adaptive learning platforms employ algorithms to assess students’ strengths and weaknesses, tailoring content, and assignments to their individual needs. This not only enhances student engagement but also improves learning outcomes. Pioneering this approach, companies like DreamBox and Knewton7 contribute to supporting learning and engagement in students. The analysis of student engagement and success rates in university programs enables higher education institutions to identify students at risk of academic difficulties. Through the analysis of attendance, grades, and engagement, universities are better equipped to offer timely support, guidance, and interventions, ultimately aiding struggling students in their academic journey. This underscores how the use of AI can synergistically collaborate with large institutions, ensuring a customer-centred approach at the core of enterprise operations.

The term ‘Artificial Intelligence’ was coined in 1956 during the Dartmouth conference, where researchers from various disciplines gathered to explore the possibilities of creating machines that could mimic human intelligence8. Since then, AI has undergone significant evolution, with ongoing research contributing to its growth and applications across diverse fields. Its continuous development is directly attributed to the fields of computer science, mathematics, cognitive science, neuroscience, and philosophy. The interdisciplinary nature of AI reflects its diverse goals, ranging from replicating human cognitive abilities to solving complex problems through computational approaches. In research, AI simplifies the processing of vast amounts of data, uncovering patterns and correlations that may pose challenges for humans. AI-powered language models assist with literature reviews, data analysis, and the development of research hypotheses, facilitating global collaboration among researchers and transcending geographical boundaries and time zones.

In navigating the landscape of innovation and technology throughout history, it is imperative to acknowledge the challenges that accompany progress. The advent of AI introduces a spectrum of challenges, encompassing issues of privacy, data security, bias in AI algorithms, regulatory compliance, and ethical utilization of AI9. This holds true in both healthcare and higher education, where the transformative impact of AI involves the handling of sensitive personal information. Safeguarding patient and student data against breaches and misuse remains a paramount concern for institutional IT departments. Robust cybersecurity measures and stringent privacy policies play a pivotal role in mitigating these risks.

At the core of AI’s technological framework lies a myriad of algorithms, which, if unchecked, have the potential to perpetuate bias and lead to discriminatory or unjust outcomes. In healthcare, this bias might contribute to disparities in diagnosis and treatment, while in higher education, it could manifest in biased admissions decisions or unequal support provision for students. The imperative of fairness and transparency in practices, coupled with a commitment to learning from mistakes, is crucial in mitigating institutional risks associated with AI. Stringent policies alone may not provide the perfect solution; instead, collaborative partnerships and ongoing surveillance from regulatory and professional bodies are essential to meet the legal and ethical standards necessary for the safe and effective use of this technology10,11. A new AI regulatory and summit framework further provides leadership and guidance, enabling institutions to align closely with expected good practices in the realm of AI.

As we usher in the future with AI-powered technologies, it is a fair and justified assessment to affirm that AI is a permanent fixture. Its role is not merely transformative but enriching, enhancing, and efficient. This paradigm shift has inaugurated a new era of transformation, particularly in healthcare and higher education. The profound impact of AI is evident in its support for improved patient care, personalized learning, and a myriad of other benefits. Nevertheless, it is equally crucial to recognize and address the challenges and ethical considerations that accompany this technological evolution. Preparing for a positive change necessitates higher education institutions, healthcare organisations and the staff who work within the to remain agile, adaptable, and ethical in their practices and processes, ensuring the seamless integration of AI to harness its full potential for the betterment of society.


  1. Department for Science, I. and T. (2021) National AI strategy, Available at: (Accessed: 10 November 2023).
  2. Department for Science, I. and T. (2023) Ai regulation: A pro-innovation approach, Available at: (Accessed: 10 November 2023).
  3. Sharma, D.K., Bhargava, S., Jha, A. and Singh, P., 2021. Early detection and diagnosis using deep learning. In Handbook of deep learning in biomedical Engineering(pp. 191-217). Academic Press.
  4. Ting, D.S.W., Pasquale, L.R., Peng, L., Campbell, J.P., Lee, A.Y., Raman, R., Tan, G.S.W., Schmetterer, L., Keane, P.A. and Wong, T.Y., 2019. Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology103(2), pp.167-175.
  5. Johnson, K.B., Wei, W.Q., Weeraratne, D., Frisse, M.E., Misulis, K., Rhee, K., Zhao, J. and Snowdon, J.L., 2021. Precision medicine, AI, and the future of personalized health care. Clinical and translational science14(1), pp.86-93.
  6. Bess, A., Berglind, F., Mukhopadhyay, S., Brylinski, M., Griggs, N., Cho, T., Galliano, C. and Wasan, K.M., 2022. Artificial intelligence for the discovery of novel antimicrobial agents for emerging infectious diseases. Drug discovery today27(4), pp.1099-1107.
  7. Alam, A. and Mohanty, A., 2022, November. Business models, business strategies, and innovations in EdTech companies: integration of learning analytics and artificial intelligence in higher education. In 2022 IEEE 6th Conference on Information and Communication Technology (CICT)(pp. 1-6). IEEE.
  8. Moor, J., 2006. The Dartmouth College artificial intelligence conference: The next fifty years. Ai Magazine27(4), pp.87-87.
  9. Jobin, A., Ienca, M. and Vayena, E., 2019. The global landscape of AI ethics guidelines. Nature machine intelligence1(9), pp.389-399.
  10. Black, J. and Murray, A.D., 2019. Regulating AI and machine learning: setting the regulatory agenda. European journal of law and technology10(3).
  11. Galaz, V., Centeno, M.A., Callahan, P.W., Causevic, A., Patterson, T., Brass, I., Baum, S., Farber, D., Fischer, J., Garcia, D. and McPhearson, T., 2021. Artificial intelligence, systemic risks, and sustainability. Technology in Society67, p.101741.

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