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Artificial intelligence in drug development: present status and future prospects

Mak KK, Pichika MR

Drug Discov Today, 2019 03;24(3):773-780.
PMID: 30472429 DOI: 10.1016/j.drudis.2018.11.014

Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.

Matched MeSH terms: Artificial Intelligence*
Fold prediction problem: the application of new physical and physicochemical-based features

Dehzangi A, Phon-Amnuaisuk S

Protein Pept Lett, 2011 Feb;18(2):174-85.
PMID: 21054271

One of the most important goals in bioinformatics is the ability to predict tertiary structure of a protein from its amino acid sequence. In this paper, new feature groups based on the physical and physicochemical properties of amino acids (size of the amino acids' side chains, predicted secondary structure based on normalized frequency of β-Strands, Turns, and Reverse Turns) are proposed to tackle this task. The proposed features are extracted using a modified feature extraction method adapted from Dubchak et al. To study the effectiveness of the proposed features and the modified feature extraction method, AdaBoost.M1, Multi Layer Perceptron (MLP), and Support Vector Machine (SVM) that have been commonly and successfully applied to the protein folding problem are employed. Our experimental results show that the new feature groups altogether with the modified feature extraction method are capable of enhancing the protein fold prediction accuracy better than the previous works found in the literature.

Matched MeSH terms: Artificial Intelligence
Fulltext An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos

Diakiw SM, Hall JMM, VerMilyea M, Lim AYX, Quangkananurug W, Chanchamroen S, et al.

Reprod Biomed Online, 2022 Dec;45(6):1105-1117.
PMID: 36117079 DOI: 10.1016/j.rbmo.2022.07.018

RESEARCH QUESTION: Can better methods be developed to evaluate the performance and characteristics of an artificial intelligence model for evaluating the likelihood of clinical pregnancy based on analysis of day-5 blastocyst-stage embryos, such that performance evaluation more closely reflects clinical use in IVF procedures, and correlations with known features of embryo quality are identified?
DESIGN: De-identified images were provided retrospectively or collected prospectively by IVF clinics using the artificial intelligence model in clinical practice. A total of 9359 images were provided by 18 IVF clinics across six countries, from 4709 women who underwent IVF between 2011 and 2021. Main outcome measures included clinical pregnancy outcome (fetal heartbeat at first ultrasound scan), embryo morphology score, and/or pre-implantation genetic testing for aneuploidy (PGT-A) results.
RESULTS: A positive linear correlation of artificial intelligence scores with pregnancy outcomes was found, and up to a 12.2% reduction in time to pregnancy (TTP) was observed when comparing the artificial intelligence model with standard morphological grading methods using a novel simulated cohort ranking method. Artificial intelligence scores were significantly correlated with known morphological features of embryo quality based on the Gardner score, and with previously unknown morphological features associated with embryo ploidy status, including chromosomal abnormalities indicative of severity when considering embryos for transfer during IVF.
CONCLUSION: Improved methods for evaluating artificial intelligence for embryo selection were developed, and advantages of the artificial intelligence model over current grading approaches were highlighted, strongly supporting the use of the artificial intelligence model in a clinical setting.

Matched MeSH terms: Artificial Intelligence*
Artificial intelligence in colorectal surgery: an AI-powered systematic review

Spinelli A, Carrano FM, Laino ME, Andreozzi M, Koleth G, Hassan C, et al.

Tech Coloproctol, 2023 Aug;27(8):615-629.
PMID: 36805890 DOI: 10.1007/s10151-023-02772-8

Artificial intelligence (AI) has the potential to revolutionize surgery in the coming years. Still, it is essential to clarify what the meaningful current applications are and what can be reasonably expected. This AI-powered review assessed the role of AI in colorectal surgery. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-compliant systematic search of PubMed, Embase, Scopus, Cochrane Library databases, and gray literature was conducted on all available articles on AI in colorectal surgery (from January 1 1997 to March 1 2021), aiming to define the perioperative applications of AI. Potentially eligible studies were identified using novel software powered by natural language processing (NLP) and machine learning (ML) technologies dedicated to systematic reviews. Out of 1238 articles identified, 115 were included in the final analysis. Available articles addressed the role of AI in several areas of interest. In the preoperative phase, AI can be used to define tailored treatment algorithms, support clinical decision-making, assess the risk of complications, and predict surgical outcomes and survival. Intraoperatively, AI-enhanced surgery and integration of AI in robotic platforms have been suggested. After surgery, AI can be implemented in the Enhanced Recovery after Surgery (ERAS) pathway. Additional areas of applications included the assessment of patient-reported outcomes, automated pathology assessment, and research. Available data on these aspects are limited, and AI in colorectal surgery is still in its infancy. However, the rapid evolution of technologies makes it likely that it will increasingly be incorporated into everyday practice.

Matched MeSH terms: Artificial Intelligence*
Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis

Lim CP, Quek SS, Peh KK

J Pharm Biomed Anal, 2003 Feb 05;31(1):159-68.
PMID: 12560060

This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.

Matched MeSH terms: Artificial Intelligence
Targeting the genetic landscape of oral potentially malignant disorders has the potential as a preventative strategy in oral cancer

Prime SS, Cirillo N, Cheong SC, Prime MS, Parkinson EK

Cancer Lett, 2021 10 10;518:102-114.
PMID: 34139286 DOI: 10.1016/j.canlet.2021.05.025

This study reviews the molecular landscape of oral potentially malignant disorders (OPMD). We examine the impact of tumour heterogeneity, the spectrum of driver mutations (TP53, CDKN2A, TERT, NOTCH1, AJUBA, PIK3CA, CASP8) and gene transcription on tumour progression. We comment on how some of these mutations impact cellular senescence, field cancerization and cancer stem cells. We propose that OPMD can be monitored more closely and more dynamically through the use of liquid biopsies using an appropriate biomarker of transformation. We describe new gene interactions through the use of a systems biology approach and we highlight some of the first studies to identify functional genes using CRISPR-Cas9 technology. We believe that this information has translational implications for the use of re-purposed existing drugs and/or new drug development. Further, we argue that the use of digital technology encompassing clinical and laboratory-based data will create relevant datasets for machine learning/artificial intelligence. We believe that therapeutic intervention at an early molecular premalignant stage should be an important preventative strategy to inhibit the development of oral squamous cell carcinoma and that this approach is applicable to other aerodigestive tract cancers.

Matched MeSH terms: Artificial Intelligence
An efficient method for the computation of Legendre moments

Yap PT, Paramesran R

IEEE Trans Pattern Anal Mach Intell, 2005 Dec;27(12):1996-2002.
PMID: 16355666

Legendre moments are continuous moments, hence, when applied to discrete-space images, numerical approximation is involved and error occurs. This paper proposes a method to compute the exact values of the moments by mathematically integrating the Legendre polynomials over the corresponding intervals of the image pixels. Experimental results show that the values obtained match those calculated theoretically, and the image reconstructed from these moments have lower error than that of the conventional methods for the same order. Although the same set of exact Legendre moments can be obtained indirectly from the set of geometric moments, the computation time taken is much longer than the proposed method.

Matched MeSH terms: Artificial Intelligence*
Fulltext A modified active appearance model based on an adaptive artificial bee colony

Abdulameer MH, Sheikh Abdullah SN, Othman ZA

ScientificWorldJournal, 2014;2014:879031.
PMID: 25165748 DOI: 10.1155/2014/879031

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

Matched MeSH terms: Artificial Intelligence*
Remote protein homology detection and fold recognition using two-layer support vector machine classifiers

Muda HM, Saad P, Othman RM

Comput Biol Med, 2011 Aug;41(8):687-99.
PMID: 21704312 DOI: 10.1016/j.compbiomed.2011.06.004

Remote protein homology detection and fold recognition refer to detection of structural homology in proteins where there are small or no similarities in the sequence. To detect protein structural classes from protein primary sequence information, homology-based methods have been developed, which can be divided to three types: discriminative classifiers, generative models for protein families and pairwise sequence comparisons. Support Vector Machines (SVM) and Neural Networks (NN) are two popular discriminative methods. Recent studies have shown that SVM has fast speed during training, more accurate and efficient compared to NN. We present a comprehensive method based on two-layer classifiers. The 1st layer is used to detect up to superfamily and family in SCOP hierarchy using optimized binary SVM classification rules. It used the kernel function known as the Bio-kernel, which incorporates the biological information in the classification process. The 2nd layer uses discriminative SVM algorithm with string kernel that will detect up to protein fold level in SCOP hierarchy. The results obtained were evaluated using mean ROC and mean MRFP and the significance of the result produced with pairwise t-test was tested. Experimental results show that our approaches significantly improve the performance of remote protein homology detection and fold recognition for all three different version SCOP datasets (1.53, 1.67 and 1.73). We achieved 4.19% improvements in term of mean ROC in SCOP 1.53, 4.75% in SCOP 1.67 and 4.03% in SCOP 1.73 datasets when compared to the result produced by well-known methods. The combination of first layer and second layer of BioSVM-2L performs well in remote homology detection and fold recognition even in three different versions of datasets.

Matched MeSH terms: Artificial Intelligence
Macro-nutrients recovery from liquid waste as a sustainable resource for production of recovered mineral fertilizer: Uncovering alternative options to sustain global food security cost-effectively

Sniatala B, Kurniawan TA, Sobotka D, Makinia J, Othman MHD

Sci Total Environ, 2023 Jan 15;856(Pt 2):159283.
PMID: 36208738 DOI: 10.1016/j.scitotenv.2022.159283

Global food security, which has emerged as one of the sustainability challenges, impacts every country. As food cannot be generated without involving nutrients, research has intensified recently to recover unused nutrients from waste streams. As a finite resource, phosphorus (P) is largely wasted. This work critically reviews the technical applicability of various water technologies to recover macro-nutrients such as P, N, and K from wastewater. Struvite precipitation, adsorption, ion exchange, and membrane filtration are applied for nutrient recovery. Technological strengths and drawbacks in their applications are evaluated and compared. Their operational conditions such as pH, dose required, initial nutrient concentration, and treatment performance are presented. Cost-effectiveness of the technologies for P or N recovery is also elaborated. It is evident from a literature survey of 310 published studies (1985-2022) that no single technique can effectively and universally recover target macro-nutrients from liquid waste. Struvite precipitation is commonly used to recover over 95 % of P from sludge digestate with its concentration ranging from 200 to 4000 mg/L. The recovered precipitate can be reused as a fertilizer due to its high content of P and N. Phosphate removal of higher than 80 % can be achieved by struvite precipitation when the molar ratio of Mg2+/PO43- ranges between 1.1 and 1.3. The applications of artificial intelligence (AI) to collect data on critical parameters control optimization, improve treatment effectiveness, and facilitate water utilities to upscale water treatment plants. Such infrastructure in the plants could enable the recovered materials to be reused to sustain food security. As nutrient recovery is crucial in wastewater treatment, water treatment plant operators need to consider (1) the costs of nutrient recovery techniques; (2) their applicability; (3) their benefits and implications. It is essential to note that the treatment cost of P and/or N-laden wastewater depends on the process applied and local conditions.

Matched MeSH terms: Artificial Intelligence
Fulltext Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics

Suwinski P, Ong C, Ling MHT, Poh YM, Khan AM, Ong HS

Front Genet, 2019;10:49.
PMID: 30809243 DOI: 10.3389/fgene.2019.00049

There is a growing attention toward personalized medicine. This is led by a fundamental shift from the 'one size fits all' paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data "10 Vs" and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.

Matched MeSH terms: Artificial Intelligence
Fulltext Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms

Ling L, Aldoghachi AF, Chong ZX, Ho WY, Yeap SK, Chin RJ, et al.

Int J Mol Sci, 2022 Dec 06;23(23).
PMID: 36499713 DOI: 10.3390/ijms232315382

Detecting breast cancer (BC) at the initial stages of progression has always been regarded as a lifesaving intervention. With modern technology, extensive studies have unraveled the complexity of BC, but the current standard practice of early breast cancer screening and clinical management of cancer progression is still heavily dependent on tissue biopsies, which are invasive and limited in capturing definitive cancer signatures for more comprehensive applications to improve outcomes in BC care and treatments. In recent years, reviews and studies have shown that liquid biopsies in the form of blood, containing free circulating and exosomal microRNAs (miRNAs), have become increasingly evident as a potential minimally invasive alternative to tissue biopsy or as a complement to biomarkers in assessing and classifying BC. As such, in this review, the potential of miRNAs as the key BC signatures in liquid biopsy are addressed, including the role of artificial intelligence (AI) and machine learning platforms (ML), in capitalizing on the big data of miRNA for a more comprehensive assessment of the cancer, leading to practical clinical utility in BC management.

Matched MeSH terms: Artificial Intelligence
An insight into the role of Artificial Intelligence in the early diagnosis of Alzheimer's disease

Verma RK, Pandey M, Chawla P, Choudhury H, Mayuren J, Bhattamisra SK, et al.

CNS Neurol Disord Drug Targets, 2021 May 11.
PMID: 33982657 DOI: 10.2174/1871527320666210512014505

BACKGROUND: The complication of Alzheimer's disease (AD) has made the development of its therapeutic a challenging task. Even after decades of research, we have achieved no more than a few years of symptomatic relief. The inability to diagnose the disease early is the foremost hurdle behind its treatment. Several studies have aimed to identify potential biomarkers that can be detected in body fluids (CSF, blood, urine, etc) or assessed by neuroimaging (i.e., PET and MRI). However, the clinical implementation of these biomarkers is incomplete as they cannot be validated.
METHOD: To overcome the limitation, the use of artificial intelligence along with technical tools has been extensively investigated for AD diagnosis. For developing a promising artificial intelligence strategy that can diagnose AD early, it is critical to supervise neuropsychological outcomes and imaging-based readouts with a proper clinical review.
CONCLUSION: Profound knowledge, a large data pool, and detailed investigations are required for the successful implementation of this tool. This review will enlighten various aspects of early diagnosis of AD using artificial intelligence.

Matched MeSH terms: Artificial Intelligence
Application of artificial intelligence methods for monsoonal river classification in Selangor river basin, Malaysia

Wong YJ, Shimizu Y, Kamiya A, Maneechot L, Bharambe KP, Fong CS, et al.

Environ Monit Assess, 2021 Jun 22;193(7):438.
PMID: 34159431 DOI: 10.1007/s10661-021-09202-y

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.

Matched MeSH terms: Artificial Intelligence
Fulltext Testing the Feasibility and Acceptability of Using an Artificial Intelligence Chatbot to Promote HIV Testing and Pre-Exposure Prophylaxis in Malaysia: Mixed Methods Study

Cheah MH, Gan YN, Altice FL, Wickersham JA, Shrestha R, Salleh NAM, et al.

JMIR Hum Factors, 2024 Jan 26;11:e52055.
PMID: 38277206 DOI: 10.2196/52055

BACKGROUND: The HIV epidemic continues to grow fastest among men who have sex with men (MSM) in Malaysia in the presence of stigma and discrimination. Engaging MSM on the internet using chatbots supported through artificial intelligence (AI) can potentially help HIV prevention efforts. We previously identified the benefits, limitations, and preferred features of HIV prevention AI chatbots and developed an AI chatbot prototype that is now tested for feasibility and acceptability.
OBJECTIVE: This study aims to test the feasibility and acceptability of an AI chatbot in promoting the uptake of HIV testing and pre-exposure prophylaxis (PrEP) in MSM.
METHODS: We conducted beta testing with 14 MSM from February to April 2022 using Zoom (Zoom Video Communications, Inc). Beta testing involved 3 steps: a 45-minute human-chatbot interaction using the think-aloud method, a 35-minute semistructured interview, and a 10-minute web-based survey. The first 2 steps were recorded, transcribed verbatim, and analyzed using the Unified Theory of Acceptance and Use of Technology. Emerging themes from the qualitative data were mapped on the 4 domains of the Unified Theory of Acceptance and Use of Technology: performance expectancy, effort expectancy, facilitating conditions, and social influence.
RESULTS: Most participants (13/14, 93%) perceived the chatbot to be useful because it provided comprehensive information on HIV testing and PrEP (performance expectancy). All participants indicated that the chatbot was easy to use because of its simple, straightforward design and quick, friendly responses (effort expectancy). Moreover, 93% (13/14) of the participants rated the overall chatbot quality as high, and all participants perceived the chatbot as a helpful tool and would refer it to others. Approximately 79% (11/14) of the participants agreed they would continue using the chatbot. They suggested adding a local language (ie, Bahasa Malaysia) to customize the chatbot to the Malaysian context (facilitating condition) and suggested that the chatbot should also incorporate more information on mental health, HIV risk assessment, and consequences of HIV. In terms of social influence, all participants perceived the chatbot as helpful in avoiding stigma-inducing interactions and thus could increase the frequency of HIV testing and PrEP uptake among MSM.
CONCLUSIONS: The current AI chatbot is feasible and acceptable to promote the uptake of HIV testing and PrEP. To ensure the successful implementation and dissemination of AI chatbots in Malaysia, they should be customized to communicate in Bahasa Malaysia and upgraded to provide other HIV-related information to improve usability, such as mental health support, risk assessment for sexually transmitted infections, AIDS treatment, and the consequences of contracting HIV.

Matched MeSH terms: Artificial Intelligence
Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models

Ke B, Nguyen H, Bui XN, Bui HB, Choi Y, Zhou J, et al.

Chemosphere, 2021 Aug;276:130204.
PMID: 34088091 DOI: 10.1016/j.chemosphere.2021.130204

Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the water, especially wastewater treatment systems. Nevertheless, heavy metal's sorption efficiency on the biochar systems is highly dependent on the biochar characteristics, metal sources, and environmental conditions. Therefore, this study implicates the feasibility of biochar systems in the heavy metal sorption in water/wastewater and the use of artificial intelligence (AI) models in investigating efficiency sorption of heavy metal on biochar. Accordingly, this work investigated and proposed 20 artificial intelligent models for forecasting the sorption efficiency of heavy metal onto biochar based on five machine learning algorithms and bagging technique (BA). Accordingly, support vector machine (SVM), random forest (RF), artificial neural network (ANN), M5Tree, and Gaussian process (GP) algorithms were used as the key algorithms for the aim of this study. Subsequently, the individual models were bagged with each other to generate new ensemble models. Finally, 20 intelligent models were developed and evaluated, including SVM, RF, M5Tree, GP, ANN, BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN. Of those, the hybrid models (i.e., BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN) are introduced as the novelty of this study for estimating the heavy metal's sorption efficiency on the biochar systems. Also, the biochar characteristics, metal sources, and environmental conditions were comprehensively assessed and used, and they are considered as a novelty of the study as well. For this aim, a dataset of sorption efficiency of heavy metal was collected and processed with 353 experimental tests. Various performance indexes were applied to evaluate the models, such as RMSE, R2, MAE, color intensity, Taylor diagram, box and whiskers plots. This study's findings revealed that AI models could predict heavy metal's sorption efficiency onto biochar with high reliability, and the efficiency of the ensemble models is higher than those of individual models. The results also reported that the SVM-ANN ensemble model is the most superior model among 20 developed models. The predictive model proposed that heavy metal's efficiency sorption on biochar can be accurately forecasted and early warning for the water pollution by heavy metal.

Matched MeSH terms: Artificial Intelligence*
Random multispace quantization as an analytic mechanism for BioHashing of biometric and random identity inputs

Teoh AB, Goh A, Ngo DC

IEEE Trans Pattern Anal Mach Intell, 2006 Dec;28(12):1892-901.
PMID: 17108365

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the Random Multispace Quantization (RMQ) of biometric and external random inputs.

Matched MeSH terms: Artificial Intelligence*
Exploratory study on student perception on the use of chat AI in process-driven problem-based learning

Hamid H, Zulkifli K, Naimat F, Che Yaacob NL, Ng KW

Curr Pharm Teach Learn, 2023 Dec;15(12):1017-1025.
PMID: 37923639 DOI: 10.1016/j.cptl.2023.10.001

INTRODUCTION: With the increasing prevalence of artificial intelligence (AI) technology, it is imperative to investigate its influence on education and the resulting impact on student learning outcomes. This includes exploring the potential application of AI in process-driven problem-based learning (PDPBL). This study aimed to investigate the perceptions of students towards the use of ChatGPT) build on GPT-3.5 in PDPBL in the Bachelor of Pharmacy program.
METHODS: Eighteen students with prior experience in traditional PDPBL processes participated in the study, divided into three groups to perform PDPBL sessions with various triggers from pharmaceutical chemistry, pharmaceutics, and clinical pharmacy fields, while utilizing chat AI provided by ChatGPT to assist with data searching and problem-solving. Questionnaires were used to collect data on the impact of ChatGPT on students' satisfaction, engagement, participation, and learning experience during the PBL sessions.
RESULTS: The survey revealed that ChatGPT improved group collaboration and engagement during PDPBL, while increasing motivation and encouraging more questions. Nevertheless, some students encountered difficulties understanding ChatGPT's information and questioned its reliability and credibility. Despite these challenges, most students saw ChatGPT's potential to eventually replace traditional information-seeking methods.
CONCLUSIONS: The study suggests that ChatGPT has the potential to enhance PDPBL in pharmacy education. However, further research is needed to examine the validity and reliability of the information provided by ChatGPT, and its impact on a larger sample size.

Matched MeSH terms: Artificial Intelligence
The perceptions of medical physicists towards relevance and impact of artificial intelligence

Santos JC, Wong JHD, Pallath V, Ng KH

Phys Eng Sci Med, 2021 Sep;44(3):833-841.
PMID: 34283393 DOI: 10.1007/s13246-021-01036-9

Artificial intelligence (AI) is an innovative tool with the potential to impact medical physicists' clinical practices, research, and the profession. The relevance of AI and its impact on the clinical practice and routine of professionals in medical physics were evaluated by medical physicists and researchers in this field. An online survey questionnaire was designed for distribution to professionals and students in medical physics around the world. In addition to demographics questions, we surveyed opinions on the role of AI in medical physicists' practices, the possibility of AI threatening/disrupting the medical physicists' practices and career, the need for medical physicists to acquire knowledge on AI, and the need for teaching AI in postgraduate medical physics programmes. The level of knowledge of medical physicists on AI was also consulted. A total of 1019 respondents from 94 countries participated. More than 85% of the respondents agreed that AI would play an essential role in medical physicists' practices. AI should be taught in the postgraduate medical physics programmes, and that more applications such as quality control (QC), treatment planning would be performed by AI. Half of the respondents thought AI would not threaten/disrupt the medical physicists' practices. AI knowledge was mainly acquired through self-taught and work-related activities. Nonetheless, many (40%) reported that they have no skill in AI. The general perception of medical physicists was that AI is here to stay, influencing our practices. Medical physicists should be prepared with education and training for this new reality.

Matched MeSH terms: Artificial Intelligence*
Opportunistic Extraction of Quantitative CT Biomarkers: Turning the Incidental Into Prognostic Information

Md Shah MN, Azman RR, Chan WY, Ng KH

Can Assoc Radiol J, 2024 Feb;75(1):92-97.
PMID: 37075322 DOI: 10.1177/08465371231171700

The past two decades have seen a significant increase in the use of CT, with a corresponding rise in the mean population radiation dose. This rise in CT use has caused improved diagnostic certainty in conditions that were not previously routinely evaluated using CT, such as headaches, back pain, and chest pain. Unused data, unrelated to the primary diagnosis, embedded within these scans have the potential to provide organ-specific measurements that can be used to prognosticate or risk-profile patients for a wide variety of conditions. The recent increased availability of computing power, expertise and software for automated segmentation and measurements, assisted by artificial intelligence, provides a conducive environment for the deployment of these analyses into routine use. Data gathering from CT has the potential to add value to examinations and help offset the public perception of harm from radiation exposure. We review the potential for the collection of these data and propose the incorporation of this strategy into routine clinical practice.

Matched MeSH terms: Artificial Intelligence*

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