Publications
573 results found
Beaney T, Neves AL, Alboksmaty A, et al., 2022, Trends and associated factors for Covid-19 hospitalisation and fatality risk in 2.3 million adults in England, Nature Communications, Vol: 13, Pages: 1-9, ISSN: 2041-1723
The Covid-19 mortality rate varies between countries and over time but the extent to which this is explained by the underlying risk in those infected is unclear. Using data on all adults in England with a positive Covid-19 test between 1st October 2020 and 30th April 2021 linked to clinical records, we examined trends and risk factors for hospital admission and mortality. Of 2,311,282 people included in the study, 164,046 (7.1%) were admitted and 53,156 (2.3%) died within 28 days of a positive Covid-19 test. We found significant variation in the case hospitalisation and mortality risk over time, which remained after accounting for the underlying risk of those infected. Older age groups, males, those resident in areas of greater socioeconomic deprivation, and those with obesity had higher odds of admission and death. People with severe mental illness and learning disability had the highest odds of admission and death. Our findings highlight both the role of external factors in Covid-19 admission and mortality risk and the need for more proactive care in the most vulnerable groups.
Iqbal FM, Joshi M, Khan S, et al., 2022, Key Stakeholder Barriers and Facilitators to Implementing Remote Monitoring Technologies: Protocol for a Mixed Methods Analysis (Preprint)
<sec> <title>BACKGROUND</title> <p>The implementation of novel digital solutions within the National Health Service has historically been challenging. Since the start of the COVID-19 pandemic, there has been a greater push for digitization and for operating remote monitoring solutions. However, the implementation and widespread adoption of this type of innovation have been poorly studied.</p> </sec> <sec> <title>OBJECTIVE</title> <p>We aim to investigate key stakeholder barriers and facilitators to implementing remote monitoring solutions to identify factors that could affect successful adoption.</p> </sec> <sec> <title>METHODS</title> <p>A mixed methods approach will be implemented. Semistructured interviews will be conducted with high-level stakeholders from industry and academia and health care providers who have played an instrumental role in, and have prior experience with, implementing digital solutions, alongside the use of an adapted version of the Technology Acceptance Model questionnaire.</p> </sec> <sec> <title>RESULTS</title> <p>Enrollment is currently underway, having started in February 2022. It is anticipated to end in July 2022, with data analysis scheduled to commence in August 2022.</p> </sec> <sec> <title>CONCLUSIONS</title> <p>The results of our study may highlight key barriers and facilitators to implementing digital remote monitoring solutions, thereby allowing for improved widespread adoption
Unsworth H, Wolfram V, Dillon B, et al., 2022, Building an evidence standards framework for artificial intelligence-enabled digital health technologies, LANCET DIGITAL HEALTH, Vol: 4
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- Citations: 5
Zhang J, Whebell S, Gallifant J, et al., 2022, An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research., Lancet Digit Health, Vol: 4, Pages: e212-e213
Sounderajah V, McCradden MD, Li X, et al., 2022, Ethics methods are required as part of reporting guidelines for artificial intelligence in healthcare, NATURE MACHINE INTELLIGENCE, Vol: 4, Pages: 316-317
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- Citations: 5
Nabeel A, Al-Sabah SK, Ashrafian H, 2022, Effective cleaning of endoscopic lenses to achieve visual clarity for minimally invasive abdominopelvic surgery: a systematic review, SURGICAL ENDOSCOPY AND OTHER INTERVENTIONAL TECHNIQUES, Vol: 36, Pages: 2382-2392, ISSN: 0930-2794
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- Citations: 4
Zhang J, Whebell S, Gallifant J, et al., 2022, An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research, LANCET DIGITAL HEALTH, Vol: 4
Han J, Davids J, Ashrafian H, et al., 2022, A systematic review of robotic surgery: From supervised paradigms to fully autonomous robotic approaches, International Journal of Medical Robotics and Computer Assisted Surgery, Vol: 18, Pages: 1-11, ISSN: 1478-5951
BackgroundFrom traditional open surgery to laparoscopic surgery and robot-assisted surgery, advances in robotics, machine learning, and imaging are pushing the surgical approach to-wards better clinical outcomes. Pre-clinical and clinical evidence suggests that automation may standardise techniques, increase efficiency, and reduce clinical complications.MethodsA PRISMA-guided search was conducted across PubMed and OVID.ResultsOf the 89 screened articles, 51 met the inclusion criteria, with 10 included in the final review. Automatic data segmentation, trajectory planning, intra-operative registration, trajectory drilling, and soft tissue robotic surgery were discussed.ConclusionAlthough automated surgical systems remain conceptual, several research groups have developed supervised autonomous robotic surgical systems with increasing consideration for ethico-legal issues for automation. Automation paves the way for precision surgery and improved safety and opens new possibilities for deploying more robust artificial intelligence models, better imaging modalities and robotics to improve clinical outcomes.
Dewa LH, Roberts L, Lawrance E, et al., 2022, Authors' reply to: Toward a better understanding of quality social connections. Comment on "Quality social connection as an active ingredient in digital interventions for young people with depression and anxiety: systematic scoping review and meta-analysis", Journal of Medical Internet Research, Vol: 24, ISSN: 1438-8871
Elliott KS, Haber M, Daggag H, et al., 2022, Fine-Scale Genetic Structure in the United Arab Emirates Reflects Endogamous and Consanguineous Culture, Population History, and Geography, MOLECULAR BIOLOGY AND EVOLUTION, Vol: 39, ISSN: 0737-4038
Ruban A, Miras A, glaysher M, et al., 2022, Duodenal-jejunal bypass liner for the management of Type 2 diabetes and obesity: a multicenter randomized controlled trial, Annals of Surgery, Vol: 275, Pages: 440-447, ISSN: 0003-4932
Objective: The aim of this study was to examine the clinical efficacy and safety of the duodenal-jejunal bypass liner (DJBL) while in situ for 12 months and for 12 months after explantation.Summary Background Data: This is the largest randomized controlled trial (RCT) of the DJBL, a medical device used for the treatment of people with type 2 diabetes mellitus (T2DM) and obesity. Endoscopic interventions have been developed as potential alternatives to those not eligible or fearful of the risks of metabolic surgery.Methods: In this multicenter open-label RCT, 170 adults with inadequately controlled T2DM and obesity were randomized to intensive medical care with or without the DJBL. Primary outcome was the percentage of participants achieving a glycated hemoglobin reduction of ≥20% at 12 months. Secondary outcomes included weight loss and cardiometabolic risk factors at 12 and 24 months.Results: There were no significant differences in the percentage of patients achieving the primary outcome between both groups at 12 months [DJBL 54.6% (n = 30) vs control 55.2% (n = 32); odds ratio (OR) 0.93, 95% confidence interval (CI): 0.44–2.0; P = 0.85]. Twenty-four percent (n = 16) patients achieved ≥15% weight loss in the DJBL group compared to 4% (n = 2) in the controls at 12 months (OR 8.3, 95% CI: 1.8–39; P = .007). The DJBL group experienced superior reductions in systolic blood pressure, serum cholesterol, and alanine transaminase at 12 months. There were more adverse events in the DJBL group.Conclusions: The addition of the DJBL to intensive medical care was associated with superior weight loss, improvements in cardiometabolic risk factors, and fatty liver disease markers, but not glycemia, only while the device was in situ. The benefits of the devices need to be balanced against the higher rate of adverse events when making clinical decisions.Trial Registration: ISRCTN30845205. isrctn.org; Efficacy and Mechanism Evaluation Programme, a Medical Research
Danielli S, Donnelly P, Coffey T, et al., 2022, Measuring more than just economic growth to improve well-being, Journal of Public Health, Vol: 44, Pages: e76-e78, ISSN: 1741-3842
It's official: The UK is in a recession. The economy has suffered its biggest slump on record with a drop in gross domestic product (GDP) of 20.4%. 1 This is going to have a significant impact on our health and well-being. It risks creating a spiralling decay as we know good health is not only a consequence, but also a condition for sustained and sustainable economic development. 2 In this way, the health of a nation creates a virtuous circle of improved health and improved economic prosperity. How we measure prosperity is therefore important and needs to be considered.
Ashrafian H, 2022, Differential diagnosis of a thyroid mass, facial malar rash and ptosis on the flora in the primavera by Sandro Botticelli (1445-1510), JOURNAL OF ENDOCRINOLOGICAL INVESTIGATION, Vol: 45, Pages: 687-689, ISSN: 0391-4097
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- Citations: 3
Jiwa N, Kumar S, Gandhewar R, et al., 2022, Diagnostic Accuracy of Nipple Discharge Fluid Cytology: A Meta-Analysis and Systematic Review of the Literature, Publisher: SPRINGER, Pages: 1774-1786, ISSN: 1068-9265
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- Citations: 2
Sounderajah V, Ashrafian H, Karthikesalingam A, et al., 2022, Developing Specific Reporting Standards in Artificial Intelligence Centred Research, ANNALS OF SURGERY, Vol: 275, Pages: E547-E548, ISSN: 0003-4932
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- Citations: 5
Chidambaram S, Maheswaran Y, Chan C, et al., 2022, Misinformation About the Human Gut Microbiome in YouTube Videos: Cross-sectional Study (Preprint)
<sec> <title>BACKGROUND</title> <p>Social media platforms such as YouTube are integral tools for disseminating information about health and wellness to the public. However, anecdotal reports have cited that the human gut microbiome has been a particular focus of dubious, misleading, and, on occasion, harmful media content. Despite these claims, there have been no published studies investigating this phenomenon within popular social media platforms.</p> </sec> <sec> <title>OBJECTIVE</title> <p>The aim of this study is to (1) evaluate the accuracy and reliability of the content in YouTube videos related to the human gut microbiome and (2) investigate the correlation between content engagement metrics and video quality, as defined by validated criteria.</p> </sec> <sec> <title>METHODS</title> <p>In this cross-sectional study, videos about the human gut microbiome were searched for on the United Kingdom version of YouTube on September 20, 2021. The 600 most-viewed videos were extracted and screened for relevance. The contents and characteristics of the videos were extracted and independently rated using the DISCERN quality criteria by 2 researchers.</p> </sec> <sec> <title>RESULTS</title> <p>Overall, 319 videos accounting for 62,354,628 views were included. Of the 319 videos, 73.4% (n=234) were produced in North America and 78.7% (n=251) were uploaded between 2019 and 2021. A total of 41.1% (131/319) of videos were produced by nonprofit organizations. Of the videos, 16.3% (52/319) included an advertisement for a product or promoted a health
Joshi M, Archer S, Morbi A, et al., 2022, Perceptions on the use of wearable sensors and continuous monitoring in surgical patients: interview study among surgical staff, JMIR Formative Research, Vol: 6, Pages: 1-9, ISSN: 2561-326X
BACKGROUND: Continuous vital sign monitoring by using wearable sensors may result in the earlier detection of patient deterioration and sepsis. Few studies have explored the perspectives of surgical team members on the use of such sensors in surgical patients. OBJECTIVE: This study aims to understand the views of surgical team members regarding novel wearable sensors for surgical patients. METHODS: Wearable sensors that monitor vital signs (heart rate, respiratory rate, and temperature) continuously were used by acute surgical patients. The opinions of surgical staff who were treating patients with these sensors were collated through in-depth semistructured interviews to thematic saturation. Interviews were audio recorded, transcribed, and analyzed via thematic analysis. RESULTS: A total of 48 interviews were performed with senior and junior surgeons and senior and junior nurses. The main themes of interest that emerged from the interviews were (1) problems with current monitoring, (2) the anticipated impact of wearables on patient safety, (3) the impact on staff, (4) the impact on patients overall, (5) potential new changes, and (6) the future and views on technology. CONCLUSIONS: Overall, the feedback from staff who were continuously monitoring surgical patients via wearable sensors was positive, and relatively few concerns were raised. Surgical staff members identify problems with current monitoring and anticipate that sensors will both improve patient safety and be the future of monitoring.
Sounderajah V, 2022, Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study, npj Digital Medicine, Vol: 5, Pages: 1-13, ISSN: 2398-6352
Artificial intelligence (AI) centred diagnostic systems are increasingly recognized as robust solutions in healthcare delivery pathways. In turn, there has been a concurrent rise in secondary research studies regarding these technologies in order to influence key clinical and policymaking decisions. It is therefore essential that these studies accurately appraise methodological quality and risk of bias within shortlisted trials and reports. In order to assess whether this critical step is performed, we undertook a meta-research study evaluating adherence to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool within AI diagnostic accuracy systematic reviews. A literature search was conducted on all studies published from 2000 to December 2020. Of 50 included reviews, 36 performed quality assessment, of which 27 utilised the QUADAS-2 tool. Bias was reported across all four domains of QUADAS-2. 243 of 423 studies (57.5%) across all systematic reviews utilising QUADAS-2 reported a high or unclear risk of bias in the patient selection domain, 110 (26%) reported a high or unclear risk of bias in the index test domain, 121 (28.6%) in the reference standard domain and 157 (37.1%) in the flow and timing domain. This study demonstrates incomplete uptake of quality assessment tools in reviews of AI-based diagnostic accuracy studies and highlights inconsistent reporting across all domains of quality assessment. Poor standards of reporting act as barriers to clinical implementation. The creation of an AI specific extension for quality assessment tools of diagnostic accuracy AI studies may facilitate the safe translation of AI tools into clinical practice.
Wei J, Nazarian S, Teare J, et al., 2022, A case for improved assessment of gut permeability: a meta-analysis quantifying the lactulose:mannitol ratio in coeliac and Crohn’s disease, BMC Gastroenterology, Vol: 22, ISSN: 1471-230X
Background:A widely used method in assessing small bowel permeability is the lactulose:mannitol test, where the lactulose:mannitol ratio (LMR) is measured. However, there is discrepancy in how the test is conducted and in the values of LMR obtained across studies. This meta-analysis aims to determine LMR in healthy subjects, coeliac and Crohn’s disease.Methods:A literature search was performed using PRISMA guidance to identify studies assessing LMR in coeliac or Crohn’s disease. 19 studies included in the meta-analysis measured gut permeability in coeliac disease, 17 studies in Crohn’s disease. Outcomes of interest were LMR values and comparisons of standard mean difference (SMD) and weighted mean difference (WMD) in healthy controls, inactive Crohn’s, active Crohn’s, treated coeliac and untreated coeliac. Pooled estimates of differences in LMR were calculated using the random effects model.Results:Pooled LMR in healthy controls was 0.014 (95% CI: 0.006–0.022) while pooled LMRs in untreated and treated coeliac were 0.133 (95% CI: 0.089–0.178) and 0.037 (95% CI: 0.019–0.055). In active and inactive Crohn’s disease, pooled LMRs were 0.093 (95% CI: 0.031–0.156) and 0.028 (95% CI: 0.015–0.041). Significant differences were observed in LMR between: (1) healthy controls and treated coeliacs (SMD = 0.409 95% CI 0.034 to 0.783, p = 0.032), (2) healthy controls and untreated coeliacs (SMD = 1.362 95% CI: 0.740 to 1.984, p < 0.001), (3) treated coeliacs and untreated coeliacs (SMD = 0.722 95% CI: 0.286 to 1.157, p = 0.001), (4) healthy controls and inactive Crohn’s (SMD = 1.265 95% CI: 0.845 to 1.686, p < 0.001), (5) healthy controls and active Crohn’s (SMD = 2.868 95% CI: 2.112 to 3.623, p < 0.001), and (6) active Crohn’s and inactive Crohn&rsquo
Yeung KTD, Penney N, Whiley L, et al., 2022, The impact of bariatric surgery on serum tryptophan-kynurenine pathway metabolites, Scientific Reports, Vol: 12, ISSN: 2045-2322
Objectives: This study aims to explore the immediate effects of bariatric surgery on serum tryptophan – kynurenine pathway metabolites in individuals with type 2 diabetes and BMI >30. With the goal of providing insight into the link between tryptophan pathway metabolites, type 2 diabetes, and chronic obesity-induced inflammation. Methods: This longitudinal study included 20 participants. Half were diagnosed with type 2 diabetes. 11 and 9 underwent RYGB and SG respectively. Blood samples were obtained at pre-operative and three months post-operative timepoints. Tryptophan and downstream metabolites of the kynurenine pathway were quantified with an ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionisation method. Results: At 3 months post-operation, RYGB led to significant reductions in tryptophan, kynurenic acid and xanthurenic acid levels when compared to baseline. Significant reductions of the same metabolites after surgery were also observed in individuals with T2D irrespective of surgical procedure. These metabolites were significantly correlated with serum HbA1c levels and BMI. Conclusions: Bariatric surgery, in particular RYGB reduces serum levels of tryptophan and its downstream kynurenine metabolites. These metabolites are associated with T2D and thought to be potentially mechanistic in the systemic processes of obesity induced inflammation leading to insulin resistance. Its reduction after surgery is associated with an improvement in glycaemic control (HbA1c).
Davids J, Ashrafian H, 2022, AIM in Nanomedicine, Artificial Intelligence in Medicine, Pages: 1169-1185, ISBN: 9783030645724
Nanotechnology and its sister field of quantum technologies are interdisciplinary sciences that have been touted as one of the holy grails of technological advancements still yet to reach critical mass and unveil their transformative potential. Similarly, artificial intelligence and machine learning constitute another technological advancement that has captivated scientific hearts and minds, with both leading to next generation industrial revolutions. The unification of the latter and former technologies has thus elevated opportunities for exciting emerging discoveries and promises to offer further combinatorically exponential translational discoveries for medicine and humankind. This chapter explores the use of AI in the subfield of nanomedicine.We explore the applications of machine learning algorithms to aspects of drug discovery, toxicology and regenerative medicine, as well as medical and surgical robotics.
Davids J, Ashrafian H, 2022, AIM and mHealth, Smartphones and Apps, Artificial Intelligence in Medicine, Pages: 1229-1246, ISBN: 9783030645724
Incremental advances in miniaturized transistor technologies and improvements in network infrastructure have paved the way for the development of smartphones and applications augmenting the management of various conditions ranging from respiratory to neuropsychiatric disorders. Other applications have also been borne out of the necessity to streamline services in order to tackle logistical challenges that often arise in medicine and healthcare. These include using smartphone apps to aid diagnostics, such as skin lesion classification, or to leverage online platforms and facilitate easier access to patient information. What would otherwise take up additional unnecessary hospital resources is now achievable through mHealth systems such as patient flow monitoring. The ease with which information can now be accessed, curated, and pre-processed has paved the way for the concept of medical informatics. Informaticians now have the capability to apply artificial intelligence and machine learning to derive critical insights, automate, and make predictive analysis to improve clinical and quasi-clinical healthcare delivery. This chapter explores the global evolution of the artificial intelligence paradigm for mHealth, eHealth, and smartphone and mobile phone apps for medicine. The chapter outlines some of their applications for various medical subspecialties including cardiorespiratory, neuropsychiatric, and rehabilitation medicine.
Davids J, Lidströmer N, Ashrafian H, 2022, Artificial Intelligence for Physiotherapy and Rehabilitation, Artificial Intelligence in Medicine, Pages: 1789-1807, ISBN: 9783030645724
Physiotherapy is a natural component of modern clinical medicine, and frequently the most efficient remedy to a wide range of medical conditions. Physiotherapists are an integral part of the medical team of professionals. After surgeries or accidents, especially those involving bones and joints, and against osteoarthritis and other conditions involving pain, the physiotherapeutic treatment is commonly used in clinical practice. It is often the sole curing ingredient. At other times patients usually present to physiotherapists with backpain that have sinister underlying pathologies such as cancer or cauda equina syndromes. Therefore, this chapter aims to delve into how this remedy could be augmented with artificial intelligence (AI). It will show how AI can increase the supportive frameworks of physiotherapy in the era of digitizing medicine, through a plethora of new applications, ranging from in real time video instructions in combination with pose and joint angle detections to give optimal feedback, to motivational and psychotherapeutic components to increase exercise precision and discipline. AI will contribute to making physiotherapy even more personalized, enduring, and further integrate cognitive behavioral therapy and virtual reality into the precise treatment regimens. It will also increase the frequency of exercise, since the patient can work out in several places, with the use of telemedicine for physiotherapy too.
Lidströmer N, Aresu F, Ashrafian H, 2022, Introductory Approaches for Applying Artificial Intelligence in Clinical Medicine, Artificial Intelligence in Medicine, Pages: 57-74, ISBN: 9783030645724
The urge of computerized, automatized medical decision making as well as having more efficient and organized health data records for financial and medical purposes have brought the necessity to introduce artificial intelligence algorithms to healthcare. The first artificial intelligence applications in the medical field were to be seen in the introduction of Electronic Health Records followed by the development of Learning Health Systems and Clinical Decision Support systems. Currently, the development and increment of artificial intelligence applications by larger and smaller entities from all over the world is in an ongoing process, following the market and its needs.
Lidströmer N, Ashrafian H, 2022, Artificial Intelligence in Medicine, ISBN: 9783030645724
This book provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting. Artificial Intelligence in Medicine aims to give readers the required knowledge to apply artificial intelligence to clinical practice. The book is relevant to medical students, specialist doctors, and researchers whose work will be affected by artificial intelligence.
Sounderajah V, Normahani P, Aggarwal R, et al., 2022, Reporting Standards and Quality Assessment Tools in Artificial Intelligence-Centered Healthcare Research, Artificial Intelligence in Medicine, Pages: 385-395, ISBN: 9783030645724
The practice of incomplete study reporting is rife within scientific literature. It hinders the adoption of technologies, introduces considerable “research waste, " and represents a significant moral hazard. In order to combat this issue, there has been a shift towards the use of reporting standards and quality assessment tools, a move that has been endorsed by major biomedical journals as well as other key stakeholders. These instruments help [1] to improve the quality and completeness of study reporting as well as [2] to aid researchers in their assessment of a study’s risk of bias and applicability. These instruments are carefully created through a multistep evidence generation process and are specific to individual study designs or specialties. Recently, it has been noted that many of the existing instruments are poorly suited to aid the reporting and assessment of artificial intelligence (AI)- based studies on account of their niche study considerations. As such, there has been a concerted effort to produce AI-specific extensions to preexisting instruments, such as CONSORT, SPIRIT, STARD, TRIPOD, QUADAS, and PROBAST. This chapter expands upon why AI-specific amendments to these instruments are required in addition to highlighting their contents and proposed scope.
Tukra S, Lidströmer N, Ashrafian H, 2022, Meta Learning and the AI Learning Process, Artificial Intelligence in Medicine, Pages: 407-421, ISBN: 9783030645724
The huge torrent in data collection and the advent of deep learning have been transformative in artificial intelligence research, where deep learning models have achieved enormous success in divergent complex tasks ranging from computer vision to robotic control. However, the success of these models necessitates large quantities of data and exhaustive computational resources. Meta learning on the contrary aims to improve the AI learning process by imitating the human learning process, thereby enabling the AI to learn new concepts and generalize even from few samples of data. In this chapter, we highlight the most prominent approaches in meta learning and its applicability in medicine.
Davids J, Ashrafian H, 2022, AIM in Neurodegenerative Diseases: Parkinson and Alzheimer, Artificial Intelligence in Medicine, Pages: 1675-1689, ISBN: 9783030645724
Parkinson’s disease and dementia are two of the most debilitating neurodegenerative disorders to ever plague humankind. They cause significant biopsychosocial and economic burden on society and affect the community and carers in particular, necessitating holistic multidisciplinary care. The rise of artificial intelligence for medical applications in recent years, including disease prediction, diagnostics, disease progression monitoring, risk stratification, and prognostication, has also seen the development of applications for Parkinson’s disease and dementia. This chapter explores the use of artificial intelligence and machine learning in terms of the diagnosis, management, and prognosis predictions for these two neurodegenerative conditions. We discuss the medical and the surgical applications of AI for Parkinson’s disease and also highlight the artificial intelligent models that have been used for various forms of dementia. The chapter begins by introducing the reader to the impacts of AI on dementia diagnosis, treatment, and prognosis, extending the discussion to dementia with Lewy body disease before tackling specific aspects of AI related to Parkinson’s disease.
Davids J, Ashrafian H, 2022, AIM in Haematology, Artificial Intelligence in Medicine, Pages: 1425-1440, ISBN: 9783030645724
Haematology is a field that offers the gateway to life itself. This chapter provides a very brief treatment and discussion of haematology and artificial intelligence, looking at areas where machine learning has captivated the imaginations of haematologists to aid the diagnosis and management of blood-related disorders. We commence the chapter with a historical overview of AI in haematology and then investigate the subspecialties of haematology where AI has been used to discover new answers to various questions that haematologists have been asking for many decades. We explore decision support systems for diagnostic haematopathology, genetic profiling in haematology, and areas such as immune checkpoint blockade analysis in haemoncology including AI in leukaemia, lymphoma, and lymphoproliferative disorders, as well as AI for haemo-parasitology and AI for haemo-virology. We then suggest future areas where AI may impact the treatment of current diseases such as sickle cell anaemia.
Davids J, Lidströmer N, Ashrafian H, 2022, Artificial Intelligence in Medicine Using Quantum Computing in the Future of Healthcare, Artificial Intelligence in Medicine, Pages: 423-446, ISBN: 9783030645724
The concept of quantumcomputing has evolved over nearly a century to a point now where it is no longer science-fiction. However, conceptual extensions of quantum computation and many body systems to quantum clinical medicine and quantum surgery are completely original areas that are yet to be realized in terms of their development and full potential. Novel formalisms and approaches will have to evolve to enable these areas to fully materialize and mature into safe clinical applications that will benefit mankind. Nevertheless, factors paving the way for this exciting area of medical and future surgical science include the exponential advances in computational power gained through newly evolved mathematical formalisms for algorithmic design such as quantum mechanics, category theory, quantum algebraic geometry and others, coupled with advances in precision nanoengineering. This chapter offers a cursory non-exhaustive primer to the topic of quantum machine learning for medicine, surgery and healthcare, highlighting some of the areas where the authors theorise that quantum computing will help augment medicine, surgery and healthcare to usher in next-level precision medical diagnostics and therapeutics. In the not-too-distant future, quantum medicine and surgery will offer the ability to re-calibrate the continuous state of flux that occurs in conditions like cancer and neurological diseases to a manageably consistent curative state.
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