The future of precision medicine: potential impacts for health technology assessment
Open accessAbstract
OBJECTIVE: Precision medicine allows healthcare interventions to be tailored to groups of patients based on their disease susceptibility, diagnostic or prognostic information, or treatment response. We analysed what developments are expected in precision medicine over the next decade and considered the implications for health technology assessment (HTA) agencies. METHODS: We performed a pragmatic literature search to account for the large size and wide scope of the precision medicine literature. We refined and enriched these results with a series of expert interviews up to 1 h in length, including representatives from HTA agencies, research councils and researchers designed to cover a wide spectrum of precision medicine applications and research. RESULTS: We identified 31 relevant papers and interviewed 13 experts. We found that three types of precision medicine are expected to emerge in clinical practice: complex algorithms, digital health applications and 'omics'-based tests. These are expected to impact upon each stage of the HTA process, from scoping and modelling through to decision-making and review. The complex and uncertain treatment pathways associated with patient stratification and fast-paced technological innovation are central to these effects. DISCUSSION: Innovation in precision medicine promises substantial benefits but will change the way in which some health services are delivered and evaluated. The shelf life of guidance may decrease, structural uncertainty may increase and new equity considerations will emerge. As biomarker discovery accelerates and artificial intelligence-based technologies emerge, refinements to the methods and processes of evidence assessments will help to adapt and maintain the objective of investing in healthcare that is value for money.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Other biomarkers such as protein expression, gut microbiome, and metabolic profile are employed with machine learning to enable personalized treatments.
“In addition to genetic information, other biomarkers such as protein expression, gut microbiome, and metabolic profile are also employed with machine learning to enable personalized treatments [12].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [12]
Omics-based tests use genetic information from a population pool with machine learning algorithms to find correlations and predict treatment responses for individual patients.
“Omics-based tests: Genetic information from a population pool is used with machine learning algorithms to find correlations and predict treatment responses for the individual patient.”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [12]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Methods of data collection in qualitative research: interviews and focus groups
Gill, P., Stewart, K., Treasure, E. T., & Chadwick, B. L. (2008) · BDJ · doi:10.1038/bdj.2008.192 · Cited by 2,779 · see citing works
The Framingham Heart Study and the epidemiology of cardiovascular disease: a historical perspective
Mahmood, S. S., Levy, D., Vasan, R. S., & Wang, T. J. (2014) · The Lancet · doi:10.1016/s0140-6736(13)61752-3 · Cited by 1,622 · see citing works
Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists
Jha, S., & Topol, E. J. (2016) · JAMA · doi:10.1001/jama.2016.17438 · Cited by 622 · see citing works
The Precision Medicine Initiative
Ashley, E. A. (2015) · JAMA · doi:10.1001/jama.2015.3595 · Cited by 562 · see citing works
Risk models and scores for type 2 diabetes: systematic review
Noble, D., Mathur, R., Dent, T., Meads, C., & Greenhalgh, T. (2011) · BMJ · doi:10.1136/bmj.d7163 · Cited by 551 · see citing works
Clinical Interpretation and Implications of Whole-Genome Sequencing
Dewey, F., Grove, M. E., Pan, C., Goldstein, B., Bernstein, J., Chaib, H., Merker, J., Goldfeder, R., Enns, G., David, S., et al. (2014) · Journal of the American Medical Association (JAMA) · doi:10.1001/jama.2014.1717 · Cited by 456 · see citing works
Brouwer, W. B. F., Culyer, A. J., van Exel, J. N. A., & Rutten, F. (2007) · Journal of Health Economics · doi:10.1016/j.jhealeco.2007.07.003 · Cited by 335 · see citing works
Updated UK Recommendations for HER2 assessment in breast cancer
Rakha, E. A., Pinder, S. E., Bartlett, J. M. S., Ibrahim, M., Starczynski, J., Carder, P. J., Provenzano, E., Hanby, A. M., Hales, S., Lee, A. H. S., et al. (2014) · Journal of Clinical Pathology · doi:10.1136/jclinpath-2014-202571 · Cited by 255 · see citing works
“Omics”-Informed Drug and Biomarker Discovery: Opportunities, Challenges and Future Perspectives
Matthews, H., Hanison, J., & Nirmalan, N. J. (2016) · Proteomes · doi:10.3390/proteomes4030028 · Cited by 192 · see citing works
Bojke, L., Claxton, K., Sculpher, M. J., & Palmer, S. (2009) · Value in Health · doi:10.1111/j.1524-4733.2008.00502.x · Cited by 186 · see citing works
Evaluating the utility of personal genomic information
Foster, M. W., Mulvihill, J. J., & Sharp, R. R. (2009) · Genetics in Medicine · doi:10.1097/gim.0b013e3181a2743e · Cited by 180 · see citing works
the members of Eurogentest Unit 6 and ESHG Education Committee, Skirton, H., Lewis, C., Kent, A., & Coviello, D. A. (2010) · European Journal of Human Genetics · doi:10.1038/ejhg.2010.64 · Cited by 176 · see citing works
On the Estimation of the Cost-Effectiveness Threshold: Why, What, How?
Vallejo‐Torres, L., García‐Lorenzo, B., Castilla‐Rodríguez, I., Valcárcel‐Nazco, C., García‐Pérez, L., Linertová, R., Polentinos-Castro, E., & Serrano-Aguilar, P. G. (2016) · Value in Health · doi:10.1016/j.jval.2016.02.020 · Cited by 170 · see citing works
Faulkner, E. C., Annemans, L., Garrison, L., Helfand, M., Holtorf, A., Hornberger, J. C., Hughes, D. A., Li, T. M. W. Y., Malone, D. C., Payne, K., et al. (2012) · Value in Health · doi:10.1016/j.jval.2012.05.006 · Cited by 161 · see citing works
Realizing the Full Potential of Precision Medicine in Health and Health Care
Dzau, V. J., & Ginsburg, G. S. (2016) · JAMA · doi:10.1001/jama.2016.14117 · Cited by 127 · see citing works
Desai, S., & Jena, A. B. (2016) · BMJ · doi:10.1136/bmj.i6357 · Cited by 114 · see citing works
An efficient basket trial design
Cunanan, K. M., Iasonos, A. E., Shen, R., Begg, C. B., & Gönen, M. (2017) · Statistics in Medicine · doi:10.1002/sim.7227 · Cited by 110 · see citing works
Issues Surrounding the Health Economic Evaluation of Genomic Technologies
Buchanan, J., Wordsworth, S., & Schuh, A. (2013) · Pharmacogenomics · doi:10.2217/pgs.13.183 · Cited by 108 · see citing works
Garrison, L. P., Kamal‐Bahl, S. J., & Towse, A. (2017) · Value in Health · doi:10.1016/j.jval.2016.12.005 · Cited by 107 · see citing works
Concepts of ‘Personalization’ in Personalized Medicine: Implications for Economic Evaluation
Rogowski, W. H., Payne, K., Schnell‐Inderst, P., Manca, A., Rochau, U., Jahn, B., Alagöz, O., Leidl, R., & Siebert, U. (2014) · PharmacoEconomics · doi:10.1007/s40273-014-0211-5 · Cited by 98 · see citing works
Crocker-Buqué, T., Edelstein, M. L., & Mounier‐Jack, S. (2016) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2016-207572 · Cited by 96 · see citing works
The Value of Heterogeneity for Cost-Effectiveness Subgroup Analysis
Espinoza, M., Manca, A., Claxton, K., & Sculpher, M. J. (2014) · Medical Decision Making · doi:10.1177/0272989x14538705 · Cited by 95 · see citing works
Personalized medicine in diabetes: the role of ‘omics’ and biomarkers
Pearson, E. R. (2016) · Diabetic Medicine · doi:10.1111/dme.13075 · Cited by 94 · see citing works
Current Methodological Issues in the Economic Assessment of Personalized Medicine
Annemans, L., Redekop, K., & Payne, K. A. (2013) · Value in Health · doi:10.1016/j.jval.2013.06.008 · Cited by 93 · see citing works
The economic case for precision medicine
Gavan, S. P., Thompson, A., & Payne, K. (2018) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2018.1421858 · Cited by 93 · see citing works
Hawkley, L. C., Lavelle, L., Berntson, G. G., & Cacioppo, J. T. (2011) · Psychophysiology · doi:10.1111/j.1469-8986.2011.01185.x · Cited by 91 · see citing works
Economic evaluations of personalized medicine: existing challenges and current developments
Shabaruddin, F. H., Payne, K., & Fleeman, N. (2015) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s35063 · Cited by 55 · see citing works
Economic Incentives for Evidence Generation: Promoting an Efficient Path to Personalized Medicine
Towse, A., & Garrison, L. P. (2013) · Value in Health · doi:10.1016/j.jval.2013.06.003 · Cited by 52 · see citing works
Development of cancer genetic services in the UK: A national consultation
Slade, I., Riddell, D., Turnbull, C., Hanson, H., Rahman, N., & for the MCG programme (2015) · Genome Medicine · doi:10.1186/s13073-015-0128-4 · Cited by 43 · see citing works
Degeling, K., Koffijberg, H., & IJzerman, M. J. (2016) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2017.1273110 · Cited by 39 · see citing works
Chen, Y., Guzauskas, G. F., Gu, C., Wang, B., Furnback, W. E., Xie, G., Dong, P., & Garrison, L. P. (2016) · Journal of Personalized Medicine · doi:10.3390/jpm6040020 · Cited by 38 · see citing works
Goddard, K. A. B., Knaus, W. A., Whitlock, E. P., Lyman, G. H., Feigelson, H. S., Schully, S. D., Ramsey, S. D., Tunis, S., Freedman, A. N., Khoury, M. J., et al. (2012) · Genetics in Medicine · doi:10.1038/gim.2012.16 · Cited by 37 · see citing works
Husereau, D., Marshall, D. A., Levy, A. R., Peacock, S. J., & Hoch, J. S. (2014) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462314000142 · Cited by 33 · see citing works
The Health Technology Assessment of Companion Diagnostics: Experience of NICE
Byron, S., Crabb, N., GEORGE, E. L., Marlow, M., & Newland, A. C. (2014) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-13-1955 · Cited by 32 · see citing works
Health technology assessment in the era of personalized health care
Becla, L., Lunshof, J. E., Gurwitz, D., den Bäumen, T. S. I., Westerhoff, H. V., Lange, B. M. H., & Brand, A. M. (2011) · International Journal of Technology Assessment in Health Care · doi:10.1017/s026646231100002x · Cited by 32 · see citing works
From here to 2025: Personalised medicine and healthcare for an immediate future
Horgan, D. (2018) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2017.12.008 · Cited by 27 · see citing works
Cowles, E., Marsden, G., Cole, A., & Devlin, N. J. (2017) · Applied Health Economics and Health Policy · doi:10.1007/s40258-017-0309-y · Cited by 24 · see citing works
Phillips, K. A., Douglas, M. P., Trosman, J. R., & Marshall, D. A. (2017) · Value in Health · doi:10.1016/j.jval.2016.08.736 · Cited by 23 · see citing works
Payne, K., Eden, M., Davison, N., & Bakker, E. (2017) · Personalized Medicine · doi:10.2217/pme-2016-0089 · Cited by 23 · see citing works
Big Data: transforming drug development and health policy decision making
Alemayehu, D., & Berger, M. L. (2016) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-016-0144-x · Cited by 21 · see citing works
The FOCUS4 design for biomarker stratified trials.
Kaplan, R. S. (2015) · PubMed · doi:10.3978/j.issn.2304-3865.2015.02.03 · Cited by 20 · see citing works
The economic evaluation of personalised oncology medicines: ethical challenges
Lewis, J., Lipworth, W. L., Kerridge, I., & Day, R. O. (2013) · The Medical Journal of Australia · doi:10.5694/mja13.10046 · Cited by 19 · see citing works
Budget impact and cost-effectiveness: can we afford precision medicine in oncology?
Doble, B. (2016) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365513.2016.1206437 · Cited by 19 · see citing works
Personalized medicine and economic evaluation in oncology: all theory and no practice?
Garattini, L., Curto, A., & Freemantle, N. (2015) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1586/14737167.2015.1078239 · Cited by 16 · see citing works
IJzerman, M. J., Manca, A., Keizer, J., & Ramsey, S. D. (2015) · Comparative Effectiveness Research · doi:10.2147/cer.s92212 · Cited by 13 · see citing works
Economic Evaluation in Stratified Medicine: Methodological Issues and Challenges
Fugel, H.-J., Nuijten, M. J. C., Postma, M. J., & Redekop, K. (2016) · Frontiers in Pharmacology · doi:10.3389/fphar.2016.00113 · Cited by 13 · see citing works
Hoogendoorn, M., Feenstra, T., Asukai, Y., Briggs, A. H., Borg, S., Negro, R. W. D., Hansen, R. N., Jansson, S., Leidl, R., Risebrough, N. A., et al. (2016) · Value in Health · doi:10.1016/j.jval.2016.04.002 · Cited by 12 · see citing works
CRISPR genome editing and its medical applications
Mahmoudian‐Sani, M., Farnoosh, G., Mahdavinezhad, A., & Saidijam, M. (2017) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2017.1406823 · Cited by 12 · see citing works
Genomics and Society : Ethical, Legal, Cultural and Socioeconomic Implications
Kumar, D. (2015) · Cited by 8 · see citing works
Espinoza, M., Manca, A., Claxton, K., & Sculpher, M. J. (2017) · Health Economics · doi:10.1002/hec.3559 · Cited by 7 · see citing works
The International Consortium for Personalised Medicine
Vicente, A. M. (2019) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · Cited by 4 · see citing works
Epstein, D. M., García‐Mochón, L., Espín, J., & Soares, M. O. (2013) · Medical Decision Making · doi:10.1177/0272989x13480130 · Cited by 3 · see citing works
Health Economic Perspectives of Genomics
Wordsworth, S., Buchanan, J., & Towse, A. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-420195-8.00005-7 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [46]Academy of Medical Sciences Health economics for stratified medicine. Summary of a workshop held on 5 October 2016 by the Academy of Medical Sciences and the UK Pharmacogenetics and Stratified Medicine Network 2016 London Academy of Medical Sciences 2016
- [73]National Institute for Health and Care Excellence Fast track appraisal: addendum to the guide to the processes of technology appraisal 2017 London NICE 27905710
Cited by
Sorted by citations
The rise of artificial intelligence in healthcare applications
Bohr, A., & Memarzadeh, K. (2020) · Artificial Intelligence in Healthcare · doi:10.1016/b978-0-12-818438-7.00002-2 · Cited by 1,636 · see citing works
Husereau, D., Drummond, M., Augustovski, F., de Bekker-Grob, E., Briggs, A. H., Carswell, C., Caulley, L., Chaiyakunapruk, N., Greenberg, D., Loder, E., et al. (2022) · Value in Health · doi:10.1016/j.jval.2021.10.008 · Cited by 847 · see citing works
Luo, X., Wu, K., & He, X. (2021) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-021-01968-w · Cited by 203 · see citing works
Bickman, L. (2020) · Administration and Policy in Mental Health and Mental Health Services Research · doi:10.1007/s10488-020-01065-8 · Cited by 184 · see citing works
Translational precision medicine: an industry perspective
Hartl, D., Luca, V. D., Kostikova, A., Laramie, J. M., Kennedy, S., Ferrero, E., Siegel, R. W., Fink, M., Ahmed, S., Millholland, J. M., et al. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02910-6 · Cited by 153 · see citing works
Personalised Medicine—Implementation to the Healthcare System in Europe (Focus Group Discussions)
Stefanicka-Wojtas, D., & Kurpas, D. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13030380 · Cited by 121 · see citing works
Clinical application of next-generation sequencing to the practice of neurology
Rexach, J. E., Lee, H.-N., Martinez-Agosto, J., Nemeth, A., & Fogel, B. (2019) · Lancet Neurology · doi:10.1016/s1474-4422(19)30033-x · Cited by 101 · see citing works
Faulkner, E. C., Holtorf, A., Walton, S. M., Liu, C. Y., Lin, H., Biltaj, E., Brixner, D. I., Barr, C. E., Oberg, J. A., Sandhu, G., et al. (2020) · Value in Health · doi:10.1016/j.jval.2019.11.010 · Cited by 111 · see citing works
Precision medicine in inflammatory bowel disease: concept, progress and challenges
Borg‐Bartolo, S., Boyapati, R. K., Satsangi, J., & Kalla, R. (2020) · F1000Research · doi:10.12688/f1000research.20928.1 · Cited by 90 · see citing works
Wunderlich, K., Suppa, M., Gandini, S., Lipski, J., White, J. M. L., & del Mármol, V. (2024) · Cancers · doi:10.3390/cancers16051016 · Cited by 90 · see citing works
What Makes Artificial Intelligence Exceptional in Health Technology Assessment?
Bélisle‐Pipon, J., Couture, V., Roy, M., Ganache, I., Goetghebeur, M. M., & Cohen, I. G. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.736697 · Cited by 72 · see citing works
Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data
Chato, L., & Regentova, E. E. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13121703 · Cited by 71 · see citing works
Exploring Digital Government transformation in the EUPreprint
Barcevičius, E., Guonda, C., Codagnone, C., Gineikytė, V., Klimavičiūtė, L., Liva, G., Loreta, M., Misuraca, G. C., & Irene, V. (2019) · RePEc: Research Papers in Economics · doi:10.2760/17207 · Cited by 69 · see citing works
Precision Medicine: Implications for value chains and business models in life sciences
Denicolai, S., & Previtali, P. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2019.119767 · Cited by 60 · see citing works
Managing Uncertainty in AI-Enabled Decision Making and Achieving Sustainability
Wu, J., & Shang, S. S. C. (2020) · Sustainability · doi:10.3390/su12218758 · Cited by 58 · see citing works
Challenges and solutions in patient treatment strategies for stage II colon cancer
Fotheringham, S. A., Mozolowski, G., Murray, E. M. A., & Kerr, D. J. (2019) · Gastroenterology report · doi:10.1093/gastro/goz006 · Cited by 56 · see citing works
Design of Blockchain-based Precision Health-Care Using Soft Systems Methodology
Sharma, R., Zhang, C., Wingreen, S. C., Kshetri, N., & Zahid, A. (2019) · Industrial Management & Data Systems · doi:10.1108/imds-07-2019-0401 · Cited by 49 · see citing works
Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis
Tsakiroglou, M., Evans, A. G., & Pirmohamed, M. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1100352 · Cited by 47 · see citing works
Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine
Vellekoop, H., Huygens, S. A., Versteegh, M., Szilberhorn, L., Zelei, T., Nagy, B., Koleva‐Kolarova, R., Tsiachristas, A., Wordsworth, S., & Mölken, M. R. (2021) · PharmacoEconomics · doi:10.1007/s40273-021-01010-z · Cited by 45 · see citing works
Artificial intelligence in healthcare: a mastery
Mukherjee, J., Sharma, R., Dutta, P., & Bhunia, B. (2023) · Biotechnology and Genetic Engineering Reviews · doi:10.1080/02648725.2023.2196476 · Cited by 43 · see citing works
Promises of artificial intelligence in neuroradiology: a systematic technographic review
Olthof, A., van Ooijen, P. V., & Mehrizi, M. R. R. (2020) · Neuroradiology · doi:10.1007/s00234-020-02424-w · Cited by 27 · see citing works
Precision Medicine 2.0: How Digital Health and AI Are Changing the Game
Giansanti, D. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13071057 · Cited by 36 · see citing works
The Current and Future Role of Technology in Respiratory Care
Honkoop, P., Usmani, O. S., & Bonini, M. (2022) · Pulmonary Therapy · doi:10.1007/s41030-022-00191-y · Cited by 38 · see citing works
Farah, L., Borget, I., Martelli, N., & Vallée, A. (2024) · Journal of Medical Internet Research · doi:10.2196/51514 · Cited by 35 · see citing works
Vervoort, D., Tam, D. Y., & Wijeysundera, H. C. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.08.015 · Cited by 34 · see citing works
Baird, A., Westphalen, C. B., Blum, S. M., Nafría, B., Knott, T., Sargeant, I., Harnik, H., Brooke, N., Wicki, N., & Wong‐Rieger, D. (2023) · Health Science Reports · doi:10.1002/hsr2.1349 · Cited by 33 · see citing works
Emerging role of artificial intelligence in waste management practices
Sharma, P., & Vaid, U. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/889/1/012047 · Cited by 32 · see citing works
Li, M., Wang, W., & Zhou, K. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121064 · Cited by 32 · see citing works
Grieve, R., O’Neill, S., Basu, A., Keele, L., Rowan, K. M., & Harris, S. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.7704 · Cited by 31 · see citing works
García, L. P. O., Ehmann, F., Hines, P. A., Ritzhaupt, A., & Brand, A. M. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.753187 · Cited by 30 · see citing works
de Vos, J., Visser, L. A., de Beer, A. A., Fornasa, M., Thoral, P. J., Elbers, P., & Cinà, G. (2021) · Value in Health · doi:10.1016/j.jval.2021.06.018 · Cited by 30 · see citing works
Fahr, P., Buchanan, J. P., & Wordsworth, S. (2019) · Applied Health Economics and Health Policy · doi:10.1007/s40258-019-00474-7 · Cited by 28 · see citing works
Bogoslov, I. A., Corman, S., & Lungu, A. E. (2024) · Sustainability · doi:10.3390/su16114579 · Cited by 26 · see citing works
A Review of Causal Inference for External Comparator Arm Studies
Rippin, G., Ballarini, N. M., Sanz, H., Largent, J. A., Quinten, C., & Pignatti, F. (2022) · Drug Safety · doi:10.1007/s40264-022-01206-y · Cited by 25 · see citing works
Manders, E. A., van den Berg, S., de Visser, S. J., & Hollak, C. E. M. (2024) · The European Journal of Health Economics · doi:10.1007/s10198-024-01731-w · Cited by 22 · see citing works
Role of Artificial Intelligence in Municipal Solid Waste Management
Kumari, N., Pandey, S., Pandey, A. K., & Banerjee, M. (2023) · British Journal of Multidisciplinary and Advanced Studies · doi:10.37745/bjmas.2022.0180 · Cited by 22 · see citing works
Horgan, D., Ciliberto, G., Conte, P., Curigliano, G., Seijo, L. M., Montuenga, L. M., Paz-Ares, L., Garassino, M., Frederique, P.-L., Galli, F., et al. (2021) · Cancers · doi:10.3390/cancers13030583 · Cited by 21 · see citing works
Chen, W., Wang, Y., Zemlyanska, Y., Butani, D. H., Wong, N. C. B., Virabhak, S., Matchar, D. B., & Teerawattananon, Y. (2023) · Value in Health · doi:10.1016/j.jval.2023.05.001 · Cited by 20 · see citing works
ARTIFICIAL INTELLIGENCE IN GERIATRICS
Koç, M. (2023) · The Turkish Journal of Geriatrics · doi:10.29400/tjgeri.2023.362 · Cited by 18 · see citing works
Healthcare revolution: Advances in AI-driven medical imaging and diagnosis
Suman, A., Suman, P., Padhy, S. K., Kumar, N., & Singh, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24788-0.00007-8 · Cited by 20 · see citing works
Núñez‐Merino, M., Marín, J. M. M., Moyano‐Fuentes, J., & Castaño-Moraga, C. A. (2024) · Production Planning & Control · doi:10.1080/09537287.2024.2349224 · Cited by 17 · see citing works
Meskó, B., Kristóf, T., Dhunnoo, P., Árvai, N., & Katonai, G. (2025) · Journal of Medical Internet Research · doi:10.2196/82820 · Cited by 11 · see citing works
Vrijenhoek, T., Tõnisson, N., Kääriäinen, H. A., Leitsalu, L., & Rigter, T. (2021) · Journal of Community Genetics · doi:10.1007/s12687-021-00514-7 · Cited by 13 · see citing works
An Overview of Continuous Learning Artificial Intelligence-Enabled Medical Devices
SMITH, A. B., & Severn, M. (2022) · Canadian Journal of Health Technologies · doi:10.51731/cjht.2022.341 · Cited by 12 · see citing works
Assessing the value of precision medicine health technologies to detect and manage melanoma
Mahumud, R. A., Janda, M., Soyer, H. P., Fernández‐Peñas, P., Mar, V., & Morton, R. L. (2022) · The Medical Journal of Australia · doi:10.5694/mja2.51696 · Cited by 12 · see citing works
Describing Sources of Uncertainty in Cancer Drug Formulary Priority Setting across Canada
Jenei, K., Peacock, S. J., Burgess, M., & Mitton, C. (2021) · Current Oncology · doi:10.3390/curroncol28040236 · Cited by 11 · see citing works
Single Cell Sequencing in Cancer Diagnostics
Bagger, F. O., & Probst, V. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-981-15-4494-1_15 · Cited by 10 · see citing works
Early technology assessment of using whole genome sequencing in personalized oncology
Simons, M., van de Ven, M., Coupé, V. M. H., Joore, M. A., IJzerman, M. J., Koffijberg, H., Frederix, G., Groot, C. U., Cuppen, E. P. J. G., van Harten, W. H., et al. (2021) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2021.1917386 · Cited by 11 · see citing works
Coût des technologies de séquençage haut débit (NGS) : revue de la littérature et enseignements
Bayle, A., Marino, P., Baffert, S., Margier, J., & Bonastre, J. (2023) · Bulletin du Cancer · doi:10.1016/j.bulcan.2023.08.013 · Cited by 10 · see citing works
(2020) · Biointerface Research in Applied Chemistry · doi:10.33263/briac112.87738779 · Cited by 10 · see citing works
The Equitable Implementation of Cystic Fibrosis Personalized Medicines in Canada
Shemie, G., Nguyen, M. T., Wallenburg, J., Ratjen, F. A., & Knoppers, B. M. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11050382 · Cited by 10 · see citing works
Technology assessment framework for precision health applications
Hussain, M. S., Silvera‐Tawil, D., & Farr‐Wharton, G. (2021) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462321000350 · Cited by 10 · see citing works
Gauvreau, C. L., Wight, L., Subasri, M., Palmer, A., Hayeems, R. Z., Croker, A. K., Abelson, J., Fraser, B., Bombard, Y., Hepburn, C. M., et al. (2023) · Health Expectations · doi:10.1111/hex.13697 · Cited by 9 · see citing works
Lee, D., & Kim, K. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031291 · Cited by 9 · see citing works
Guidotti, E., Vinci, B., Attanasio, F., & Vola, F. (2019) · Journal of Pharmaceutical Policy and Practice · doi:10.1186/s40545-019-0194-x · Cited by 9 · see citing works
Pezzullo, A. M., Gris, A. V., Scarsi, N., Tona, D. M., Porcelli, M., Pumpo, M. D., Pikó, P., Ádány, R., Kannan, P., Perola, M., et al. (2025) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2025.111729 · Cited by 8 · see citing works
Gaultney, J. G., Bouvy, J. C., Chapman, R. H., Upton, A. J., Kowal, S. L., Bokemeyer, C., Solà-Morales, O., Wolf, J., & Briggs, A. H. (2021) · Applied Health Economics and Health Policy · doi:10.1007/s40258-021-00654-4 · Cited by 8 · see citing works
Vu, M., Degeling, K., Thompson, E. R., Blombery, P. A., Westerman, D., & IJzerman, M. J. (2022) · European Journal Of Haematology · doi:10.1111/ejh.13755 · Cited by 8 · see citing works
Kang, J., & Cairns, J. (2022) · BMC Cancer · doi:10.1186/s12885-022-10350-8 · Cited by 8 · see citing works
Chen, Y., Chirikov, V., Marston, X. L., Yang, J., Qiu, H., Xie, J., Sun, N., Gu, C., Dong, P., & Gao, X. Y. (2020) · Journal of health economics and outcomes research · doi:10.36469/jheor.2020.12698 · Cited by 8 · see citing works
Extending Capabilities of Artificial Intelligence for Decision-Making and Healthcare Education
Javaid, M., Haleem, A., Khan, I. H., Vaishya, R., & Vaish, A. (2020) · Apollo Medicine · doi:10.4103/am.am_10_20 · Cited by 7 · see citing works
Stelzer, I. V., Sierawska, A. K., Buyx, A., & Simon, J. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040276 · Cited by 7 · see citing works
The Impact of Artificial Intelligence on Waste Management for Climate Change
Alshater, H., Moemen, Y. S., & El‐Sayed, I. E. (2023) · Studies in big data · doi:10.1007/978-3-031-22456-0_3 · Cited by 7 · see citing works
Situación actual del cáncer de colon en Chile: una mirada traslacional
Ríos, J. A., Barake, F., Arce, M. J., López-Köstner, F., Labbé, T., Villena, J., & Becerra, S. (2020) · Revista médica de Chile · doi:10.4067/s0034-98872020000600858 · Cited by 7 · see citing works
Beyond Traditional Conceptions of Policing and Crime Control
Rosenbaum, D. P. (2025) · Cambridge University Press eBooks · doi:10.1017/9781009612784 · Cited by 6 · see citing works
Meue, E. D., Smeijers, S., Langmans, C., Clément, P. M., & Depreitere, B. (2021) · Acta Clinica Belgica · doi:10.1080/17843286.2021.1925028 · Cited by 6 · see citing works
Aguiar, P. N., Matsas, S., Dienstmann, R., & Ferreira, C. G. (2023) · Personalized Medicine · doi:10.2217/pme-2022-0008 · Cited by 6 · see citing works
Sahu, C., Sahu, R. K., & Roy, A. (2024) · Current Drug Targets · doi:10.2174/0113894501312571240920070441 · Cited by 6 · see citing works
van Schaik, L. F., Engelhardt, E. G., van Harten, W. H., & Retèl, V. P. (2024) · BMC Cancer · doi:10.1186/s12885-024-13167-9 · Cited by 6 · see citing works
Pataky, R. E., Bryan, S., Sadatsafavi, M., Peacock, S. J., & Regier, D. A. (2022) · PharmacoEconomics · doi:10.1007/s40273-022-01176-0 · Cited by 6 · see citing works
Andersson, S. W., Richardson, M. X., Stridsberg, S. L., Ryan, J. C., Sukums, F. K., & Goh, Y. S. (2022) · JMIR Research Protocols · doi:10.2196/40565 · Cited by 6 · see citing works
Medicina de precisión: conceptos, aplicaciones y proyecciones
Hurtado, C. (2022) · Revista Médica Clínica Las Condes · doi:10.1016/j.rmclc.2022.01.002 · Cited by 6 · see citing works
Avaliação de Tecnologias em Saúde: informada pela ciência ou a serviço da política?
de Soárez, P. C. (2021) · Revista de Saúde Pública · doi:10.11606/s1518-8787.2021055003234 · Cited by 5 · see citing works
Muijzer, M. B., Noordmans, H. J., Delbeke, H., Dickman, M. M., Nuijts, R. M. M. A., Dunker, S. L., Imhof, S. M., & Wisse, R. P. L. (2022) · Cornea · doi:10.1097/ico.0000000000003006 · Cited by 5 · see citing works
Osti, T., Taha, A., Reviriego, E., Pastorino, R., Boccia, S., & Gutiérrez‐Ibarluzea, I. (2025) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462325103279 · Cited by 4 · see citing works
Dullabh, P., Zott, C., Gauthreaux, N. A., Peterson, C., Aronoff, A., Monkhouse, K., & Sittig, D. F. (2026) · Journal of Medical Internet Research · doi:10.2196/81628 · Cited by 7 · see citing works
Association Between Chronic Back Pain and Protective Behaviors is Subjective and Context Dependent
Uddin, M. T., Zamzmi, G., & Canavan, S. J. (2023) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc40787.2023.10340621 · Cited by 4 · see citing works
Decoding Gait Signatures: Exploring Individual Patterns in Pathological Gait Using Explainable AI
Slijepčević, D., Horst, F., Simak, M., Schöllhorn, W. I., Horsak, B., & Zeppelzauer, M. (2024) · IEEE Access · doi:10.1109/access.2024.3513893 · Cited by 4 · see citing works
Pitini, E., Migliara, G., Baccolini, V., Isonne, C., Mazzalai, E., Turatto, F., Salerno, C., Pagano, F., Menzano, M. T., Vito, C. D., et al. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.932093 · Cited by 4 · see citing works
Artificial Intelligence and the Contributions of Nanotechnology to the Biomedical Sector
Widjaja, G., Kumar, A., Chandrasekar, V., Shankar, B. B., & Nayak, B. B. (2023) · Advances in chemical and materials engineering book series · doi:10.4018/978-1-6684-7412-9.ch005 · Cited by 4 · see citing works
Triyono, E. A., Mahanani, M. F., Anggraini, S. D., Maulana, H., Pratiwi, W. D., Yochanan, C., Tan, F., & Masyfufah, L. (2023) · Jurnal Berkala Epidemiologi · doi:10.20473/jbe.v11i32023.267-276 · Cited by 4 · see citing works
Cereceda, K., Jorquera, R., & Villarroel‐Espíndola, F. (2021) · Advances in Laboratory Medicine / Avances en Medicina de Laboratorio · doi:10.1515/almed-2021-0075 · Cited by 4 · see citing works
Muijzer, M. B., Noordmans, H. J., Delbeke, H., Dickman, M. M., Nuijts, R. M. M. A., Dunker, S. L., Imhof, S. M., Wisse, R. P. L., Team Innovatie Research MTKF, researchprogram), O. R. (. I. M., et al. (2023) · Utrecht University Repository (Utrecht University) · Cited by 4 · see citing works
de França, G. V. A., Lupatini, E. D. O., Rocha, R. T., Toledo, R. D. C. P., Figueiredo, G. S. F., Gonçalves, C. E. I., Machado, I. E., Anzolin, A. P., Gontijo, C. C., Vidal, J. F. D., et al. (2025) · The BRICS Health Journal · doi:10.47093/3034-4700.2025.2.3.41-59 · Cited by 3 · see citing works
Barsky, L. L., Speier, W. F., Fuller, G., Cheng, S. C., Kim, A., Joung, S. Y., Arnold, C. W., Dhawan, S., Lopez, M., Mastali, M., et al. (2022) · Biology of Sex Differences · doi:10.1186/s13293-022-00423-5 · Cited by 3 · see citing works
Hilty, D. M., Peled, A., & Luxton, D. D. (2023) · Springer eBooks · doi:10.1007/978-3-030-42825-9_152-1 · Cited by 3 · see citing works
Monestime, S., Page, R. D., Shaw, N., Martin, R. D., Jordan, W., Rangel, J., & Aryal, S. (2020) · Journal of Oncology Pharmacy Practice · doi:10.1177/1078155220950003 · Cited by 3 · see citing works
Machine learning in precision medicine
Sengupta, D. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821777-1.00013-6 · Cited by 3 · see citing works
Emerging trends in precision medicine and its impact in pharmacy practice
Krishna, A., Nair, H. V., Dharan, S. S., & Nair, K. (2024) · International Journal of Basic & Clinical Pharmacology · doi:10.18203/2319-2003.ijbcp20242441 · Cited by 2 · see citing works
The Landscape of PPM Informatics and the Future of Medicine
Aliferis, C., & Adam, T. J. (2019) · Health informatics · doi:10.1007/978-3-030-18626-5_21 · Cited by 2 · see citing works
Role of Artificial Intelligence in Healthcare Management
Bhaskar, A., Yadav, P. K., Singh, S. P., Kumar, V., Srivastava, S., Singh, S. K., Singh, B., & Dutt, A. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815165791123010006 · Cited by 3 · see citing works
Applications of Artificial Intelligence (AI) in Healthcare: Research Paper
Mulla, F. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr231226113454 · Cited by 2 · see citing works
Vu, M., Degeling, K., Westerman, D., & IJzerman, M. J. (2024) · Journal of Cancer Policy · doi:10.1016/j.jcpo.2024.100501 · Cited by 1 · see citing works
Green communication system for AI-based healthcare networks
Anwar, R. W., & Ali, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26476-4.00007-1 · Cited by 1 · see citing works
Klein, A. D., & Hoppe, A. (2026) · Journal of Precision Health · doi:10.1186/s44450-025-00003-x · Cited by 1 · see citing works
What Is Precision Psychotherapy?
Kind, A., Fink, S. B., & Walter, H. (2026) · The Journal of Medicine and Philosophy A Forum for Bioethics and Philosophy of Medicine · doi:10.1093/jmp/jhag007 · Cited by 1 · see citing works
Precision Medicine: A Personalized Treatment from Your Gene
Balasubramani, P., Preetha, K., & Harish, M. (2019) · ICON · doi:10.1007/978-3-030-25135-2_2
The Health Economics of Personalized Precision Medicine
Hays, P. V. (2021) · Springer eBooks · doi:10.1007/978-3-030-80100-7_12 · Cited by 1 · see citing works
Dulsamphan, T., Juntama, P., Suwanpanich, C., Isaranuwatchai, W., Silzle, M., Poonmaksatit, S., Boonsimma, P., Shotelersuk, V., Visudtibhan, A., Lusawat, A., et al. (2023) · International Journal of Technology Assessment in Health Care · doi:10.1017/s0266462323002714 · Cited by 1 · see citing works
Hilty, D. M., Peled, A., & Luxton, D. D. (2024) · Springer eBooks · doi:10.1007/978-3-030-51366-5_152 · Cited by 1 · see citing works
- Authors
- Love-Koh, J., Peel, A., Rejon-Parrilla, J. C., Ennis, K., Lovett, R., Manca, A., Chalkidou, A., Wood, H., & Taylor, M.
- Affiliations
- University of York (GB)
- University of Liverpool (GB)
- National Institute for Health and Care Excellence (GB)
- Luxembourg Institute of Health (LU)
- Corresponding author
- Love-Koh, James
- Venue
- PharmacoEconomics
- Published
- 2018-07-13
- Volume
- 36
- Issue
- 12
- Pages
- 1439-1451
- ISSN
- 1170-7690 (ISSN-L)
- 1179-2027
- PMC
- PMC6244622
- OpenAlex
- W2884983901
- Cited by
- 131
- References
- 53
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 17 · 2023: 25 · 2022: 16 · 2021: 28 · 2020: 15 · 2019: 11
- FWCI
- 20.46
- Type
- journal article
- Language
- en
- Update notices
- correction (2018-10-26)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- PharmacoEconomics (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by-nc)
- Funders
- National Institute for Health and Care Excellence
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Pharmaceutical Economics and Policy
- Keywords
- precision medicine, health technology assessment, patient stratification, digital health applications, artificial intelligence in healthcare, structural uncertainty, health care equity, value for money, decision-making
- MeSH terms
- Artificial Intelligence, Decision Making, Humans, Technology Assessment, Biomedical, Biomarkers, Biomedical Technology, Uncertainty, Precision Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
